40 years of cognitive architectures: core cognitive abilities and practical applications

The goal of this paper is to provide a broad overview of the last 40 years of research in cognitive architectures with an emphasis on the core capabilities of perception, attention mechanisms, action selection, learning, memory, reasoning, metareasoning and their practical applications. Although the field of cognitive architectures has been steadily expanding, most of the surveys published in the past 10 years do not reflect this growth and feature essentially the same set comprising a few established architectures.

The latest large-scale study was conducted by Samsonovich (2010) in an attempt to catalog the implemented cognitive architectures. His survey contains descriptions of 26 cognitive architectures submitted by their respective authors. The same information is also presented online in a Comparative Table of Cognitive Architectures.¹ In addition to surveys, there are multiple on-line sources listing cognitive architectures, but they rarely go beyond a short description and a link to the project site or a software repository.

Since there is no exhaustive list of cognitive architectures, their exact number is unknown, but it is estimated to be around three hundred, out of which at least one-third of the projects are currently active. To form the initial list for our study we combined the architectures mentioned in surveys (published within the last 10 years) and several large on-line catalogs. We also included more recent projects not yet mentioned in the survey literature. Figure 1 shows a visualization of 195 cognitive architectures featured in 17 sources (surveys, on-line catalogs and on Google Scholar). It is apparent from this diagram that a small group of architectures such as ACT-R, Soar, CLARION, ICARUS, EPIC, LIDA and a few others are present in most sources, and all other projects are only briefly mentioned in on-line catalogs.

Even though the theoretical and practical contributions of the major architectures are undeniable, they represent only a part of the research in the field. Thus, in this review the focus is shifted away from the deep study of the major architectures or discussion of what could be the best approach to modeling cognition, which has been done elsewhere. For example, in a recent paper by Laird et al. (2017) ACT-R, Soar and Sigma are compared based on their structural organization and approaches to modelling core cognitive abilities. Further, a new Standard Model of the Mind is proposed as a reference model born out of consensus between the three architectures. Our goal, on the other hand, is to present a broad, inclusive, judgment-neutral snapshot of the past 40 years of development of the field. We hope to inform the future cognitive architecture research by introducing the diversity of ideas that have been tried and their relative success.

To make this survey manageable we reduced the original list of architectures to 84 items by considering only implemented architectures with at least one practical application and several peer-reviewed publications. We do not explicitly include some of the philosophical architectures such as CogAff (Sloman 2003), Society of Mind (Minsky 1986), Global Workspace Theory (GWT) (Baars 2005) and Pandemonium Theory (Selfridge 1958), however we examine cognitive architectures heavily influenced by these theories (e.g. LIDA, ARCADIA, CERA-CRANIUM, ASMO, COGNET and Copycat/Metacat, also see discussion in Sect. 5). We also exclude large-scale brain modeling projects, which are low-level and do not easily map onto the breadth of cognitive capabilities modeled by other types of cognitive architectures. Further, many of the existing brain models do not yet have practical applications, and thus do not fit the parameters of the present survey. Figure 2 shows all architectures featured in this survey with their approximate timelines recovered from the publications. Of these projects 49 are currently active.²

As we mentioned earlier, the first step towards creating an inclusive and organized catalog of implemented cognitive architectures was made by Samsonovich (2010). His work contained extended descriptions of 26 projects with the following information: short overview, a schematic diagram of major elements, common components and features (memory types, attention, consciousness, etc.), learning and cognitive development, cognitive modeling and applications, scalability and limitations. A survey of this kind brings together researchers from several disjoint communities and helps to establish a mapping between the different approaches and terminology they use. However, the descriptive or tabular format does not facilitate easy comparisons between architectures. Since our sample of architectures is large, we experimented with alternative visualization strategies.

In the following sections, we will provide an overview of the definitions of cognition and approaches to categorizing cognitive architectures. As one of our contributions, we map cognitive architectures according to their perception modality, implemented mechanisms of attention, memory organization, types of learning, action selection and practical applications.

In the process of preparing this paper, we thoroughly examined the literature and this activity led to an extensive bibliography of more than 2500 relevant publications. We provide this bibliography, as well as interactive versions of the diagrams in this paper on our project webpage.³

Cognitive architectures are a part of research in general AI, which began in the 1950s with the goal of creating programs that could reason about problems across different domains, develop insights, adapt to new situations and reflect on themselves. Similarly, the ultimate goal of research in cognitive architectures is to model the human mind, eventually enabling us to build human-level artificial intelligence. To this end, cognitive architectures attempt to provide evidence what particular mechanisms succeed in producing intelligent behavior and thus contribute to cognitive science. Moreover, the body of work represented by the cognitive architectures covered in this review, documents what methods or strategies have been tried previously (and what have not), how they have been used, and what level of success has been achieved or lessons learned, all important elements that help guide future research efforts. For AI and engineering, documentation of past mechanistic work has obvious import. But this is just as important for cognitive science, since most experimental work eventually attempts to connect to explanations of how observed human behavior may be generated.

According to Russell and Norvig (1995) artificial intelligence may be realized in four different ways: systems that think like humans, systems that think rationally, systems that act like humans, and systems that act rationally. The existing cognitive architectures have explored all four possibilities. For instance, human-like thought is pursued by the architectures stemming from cognitive modeling. In this case, the errors made by an intelligent system should match the errors typically made by people in similar situations. This is in contrast to rationally thinking systems which are required to produce consistent and correct conclusions for arbitrary tasks. A similar distinction is made for machines that act like humans or act rationally. Machines in either of these groups are not expected to think like humans, only their actions or behavior is taken into account.

Given the multitude of approaches that may lead to human-level AI and in the absense of clear definition and general theory of cognition, each cognitive architecture is built on a particular set of premises and assumptions, making comparison and evaluation of progress across architectures difficult. Several papers were published to resolve these issues, the most prominent being Sun’s desiderata for cognitive architectures (Sun 2004) and Newell’s functional criteria [first published in Newell (1980) and Newell (1992)], and later restated by Anderson and Lebiere (2003). Newell’s criteria include flexible behavior, real-time operation, rationality, large knowledge base, learning, development, linguistic abilities, self-awareness and brain realization. Sun’s desiderata are broader and include ecological, cognitive and bio-evolutionary realism, adaptation, modularity, routineness and synergistic interaction. Besides defining these criteria and applying them to a range of cognitive architectures, Sun also pointed out the lack of clearly defined cognitive assumptions and methodological approaches, which hinder progress in studying intelligence. He also noted an uncertainty regarding essential dichotomies (implicit/explicit, procedural/declarative, etc.), modularity of cognition and structure of memory. However, a quick look at the existing cognitive architectures reveals persisting disagreements in terms of their research goals, structure, operation and application.

Given the issues with defining intelligence (Legg and Hutter 2007), a more practical solution is to treat it as a set of competencies and behaviors demonstrated by the system. While no comprehensive list of capabilities required for intelligence exists, several broad areas have been identified that may serve as guidance for ongoing work in the cognitive architecture domain. For example, Adams et al. (2012) suggest broad areas such as perception, memory, attention, actuation, social interaction, planning, motivation, emotion, etc. These are further split into subareas. Arguably, some of these categories may seem more important than the others and historically attracted more attention (further discussed in Sect. 11.2).

At the same time, implementing even a reduced set of abilities in a single architecture is a substantial undertaking. Unsurprisingly, the goal of achieving Artificial General Intelligence (AGI) is explicitly pursued by a small number of architectures, among which are Soar, ACT-R, NARS (Wang 2013), LIDA (Faghihi and Franklin 2012), and several recent projects, such as SiMA (formerly ARS) (Schaat et al. 2015), Sigma (Pynadath et al. 2014) and CogPrime (Goertzel and Yu 2014). Majority of architectures study particular aspects of cognition, e.g. attention [ARCADIA (Bridewell and Bello 2015), STAR (Tsotsos 2017)], emotions [CELTS (Faghihi et al. 2011b)], perception of symmetry [Cognitive Symmetry Engine (Henderson and Joshi 2013)] or problem solving [FORR (Epstein 2004), PRODIGY (Epstein 2004)]. There are also narrowly specialized architectures designed for particular applications, such as ARDIS (Martin et al. 2009) for visual inspection of surfaces or MusiCog (Maxwell 2014) for music comprehension and generation.

In view of such diversity of existing architectures and their proclaimed goals, naturally, the question then arises as to what system can be considered a cognitive architecture. Different opinions on this can be found in the literature. Laird (2012a) discusses how cognitive architectures differ from other intelligent software systems. While all of them have memory storage, control components, data representation, and input/output devices, the latter provide only a fixed model for general computation. Cognitive architectures, on the other hand, must change through development and efficiently use knowledge to perform new tasks. Furthermore, he suggests that toolkits and frameworks for building intelligent agents (e.g. GOMS, BDI, etc.) cannot themselves be considered cognitive architectures. [Note, however, the authors of Pogamut, a framework for building intelligent agents, consider it a cognitive architecture as it is included in Samsonovich (2010).] Some researchers contrast the engineering approach taken in the field of artificial intelligence with the scientific approach of cognitive architectures (Sun 2007; Jordan and Russell 1999; Lieto et al. 2018b). According to Sun, psychologically based cognitive architectures should facilitate the study of human mind by modeling not only the human behavior but also the underlying cognitive processes. Such models, unlike software engineering-oriented “cognitive” architectures, are explicit representations of the general human cognitive mechanisms, which are essential for understanding of the mind.

In practice the term “cognitive architecture” is not as restrictive, as made evident by the representative surveys of the field. Most of the surveys define cognitive architectures as a blueprint for intelligence, or, more specifically, a proposal about the mental representations and computational procedures that operate on these representations enabling a range of intelligent behaviors (Butt et al. 2013; Duch et al. 2008; Langley et al. 2009; Profanter 2012; Thagard 2012). There is generally no need to justify the inclusion of the established cognitive architectures such as Soar, ACT-R, EPIC, LIDA, CLARION, ICARUS and a few others. The same applies to many biomimetic and neuroscience-inspired cognitive architectures that model cognitive processes on a neuronal level (e.g. CAPS, BBD, BECCA, DAC, SPA). Arguably, engineering-oriented architectures pursue a similar goal as they have a set of structural components for perception, reasoning and action, and model interactions between them, which is very much in the spirit of the Newell’s call for “unified theories of cognition” (Hayes-Roth 1995). However, when it comes to less common or new projects, the reasons for considering them are less clear. As an example, AKIRA, a framework that explicitly does not self-identify as a cognitive architecture (Pezzulo and Calvi 2005), is featured in some surveys anyway (Thórisson and Helgasson 2012). Similarly, a knowledge base Cyc (Foxvog 2010), which does not make any claims about general intelligence, is presented as an AGI architecture in Goertzel et al. (2014).

Recently, claims have been made that deep learning is capable of solving AI by Google (DeepMind⁴). Likewise, Facebook AI Research (FAIR⁵) and other companies are actively working in the same direction. However, the question is where does this work stand with respect to cognitive architectures? Overall, the DeepMind research addresses a number of important issues in AI, such as natural language understanding, perceptual processing, general learning, and strategies for evaluating artificial intelligence. Although particular models already demonstrate cognitive abilities in limited domains, at this point they do not represent a unified model of intelligence.

Differently from DeepMind, Mikolov et al. (2015) from a Facebook research team explicitly discuss their work in a broader context of developing intelligent machines. Their main argument is that AI is too complex to be built all at once and instead its general characteristics should be defined first. Two such characteristics of intelligence are defined, namely, communication and learning, and a concrete roadmap are proposed for developing them incrementally.

Currently, there are no publications about developing such a system, but overall the research topics pursued by FAIR align with their proposal for AI and also the business interests of the company. Common topics include visual processing, especially segmentation and object detection, data mining, natural language processing, human-computer interaction and network security. Since the current deep learning techniques are mainly applied to solving practical problems and do not represent a unified framework we do not include them in this review. However, given their prevalence in other areas of AI, deep learning methods will likely play important role in the cognitive architectures of the future.

In view of the above discussion and to ensure both inclusiveness and consistency, cognitive architectures in this survey are selected based on the following criteria: self-evaluation as cognitive, robotic or agent architecture, existing implementation (not necessarily open-source), and mechanisms for perception, attention, action selection, memory and learning. Furthermore, we considered the architectures with at least several peer-reviewed papers and practical applications beyond simple illustrative examples. For the most recent architectures still under development, some of these conditions were relaxed.

An important point to keep in mind while reading this survey is that cognitive architectures should be distinguished from the models or agents that implement them. For instance, ACT-R, Soar, HTM and many other architectures serve as the basis for multiple software agents that demonstrate only a subset of capabilities declared in theory. On the other hand, some agents may implement extra features that are not available in the cognitive architecture. A good example is the perceptual system of Rosie (Kirk and Laird 2014), one of the robotic agents implemented in Soar, whereas Soar itself does not include a real perceptual system for physical sensors as part of the architecture. Unfortunately, in many cases the level of detail presented in the publications does not allow one to judge whether the particular capability is enabled by the architectural mechanisms (and is common among all models) or is custom-made for a particular application. Therefore, to avoid confusion, we do not make this distinction and list all capabilities demonstrated by the architecture.

Note also that some architectures underwent structural and conceptual changes throughout their development, notably the long-running projects such as Soar, ACT-R, CLARION, Disciple and few others. For example, Soar 9 and subsequent versions added some non-symbolic elements (Laird 2012b) and CAPS4 better accounts for the time course of cognition and individual differences compared to its predecessors 3CAPS and CAPS.⁶ Sometimes these changes are well documented (e.g. CLARION,⁷ Soar⁸), but more often they are not. In general we use the most recent variant of the architecture for analysis and assume that the features and abilities of the previous versions are retained in the new version unless there is contradicting evidence.

Many papers published within the last decade address the problem of evaluation rather than categorization of cognitive architectures. As mentioned earlier, Newell’s criteria (Anderson and Lebiere 2003; Newell 1980, 1992) and Sun’s desiderata (Sun 2004) belong in this category. Furthermore, surveys of cognitive architectures define various capabilities, properties and evaluation criteria, which include recognition, decision making, perception, prediction, planning, acting, communication, learning, goal setting, adaptability, generality, autonomy, problem solving, real-time operation, meta-learning, etc. (Asselman et al. 2015; Langley et al. 2009; Thórisson and Helgasson 2012; Vernon et al. 2007).

While these criteria could be used for classification, many of them are too fine-grained to be applied to a generic architecture. Thus, a more general grouping of architectures can be based on the type of representation and information processing they implement. Three major paradigms are currently recognized: symbolic (also referred to as cognitivist), emergent (connectionist) and hybrid. Which of these representations, if any, correctly reflects the human cognitive processes, remains an open question and has been debated for the last 30 years (Kelley 2003; Sun and Bookman 1994).

Symbolic systems represent concepts using symbols that can be manipulated using a predefined instruction set. Such instructions can be implemented as if-then rules applied to the symbols representing the facts known about the world (e.g. ACT-R, Soar and other production rule architectures). Because it is a natural and intuitive representation of knowledge, symbolic manipulation remains very common. Although, by design, symbolic systems excel at planning and reasoning, they are less able to deal with the flexibility and robustness that are required for dealing with a changing environment and for perceptual processing.

The emergent approach resolves the adaptability and learning issues by building massively parallel models, analogous to neural networks, where information flow is represented by a propagation of signals from the input nodes. However, the resulting system also loses its transparency, since knowledge is no longer a set of symbolic entities and instead is distributed throughout the network. For these reasons, logical inference in a traditional sense becomes problematic (although not impossible) in emergent architectures.

Naturally, each paradigm has its strengths and weaknesses. For example, any symbolic architecture requires a lot of work to create an initial knowledge base, but once it is done the architecture is fully functional. On the other hand, emergent architectures are easier to design, but must be trained in order to produce useful behavior. Furthermore, their existing knowledge may deteriorate with the subsequent learning of new behaviors.

As neither paradigm is capable of addressing all major aspects of cognition, hybrid architectures attempt to combine elements of both symbolic and emergent approaches.⁹ Such systems are the most common in our selection of architectures (and, likely, overall). In general, there are no restrictions on how the hybridization is done and many possibilities have been explored. Multiple taxonomies of the types of hybridization have been proposed (Duch 2007; Hilario 1997; Sun 1996; Wermter 1997; Wermter and Sun 2000). Besides representation, one can classify the systems as single or multi-module, heterogeneous or homogeneous, take into account the granularity of hybridization (coarse-grained or fine-grained), the coupling between the symbolic and sub-symbolic components, and types of memory and learning. However, not all the hybrid architectures explicitly address what is referred to as symbolic and sub-symbolic elements and reasons for combining them. Only a few of them, namely ACT-R, CLARION, DUAL, CogPrime, CAPS, SiMA, GMU-BICA and Sigma, view this integration as essential and discuss it extensively. In general, we found that symbolic and sub-symbolic elements cannot be identified for all reviewed architectures due to lack of such fine-grained detail in many publications, thus we will focus on representation and processing.

Figure 3 shows the architectures grouped according to the new taxonomy. The top level of the hierarchy is represented by the symbolic, emergent and hybrid approaches. The definitions of these terms in the literature are vague, which often leads to the inconsistent assignment of architectures to either group. There seems to be no agreement even regarding the most known architectures, Soar and ACT-R. Although both combine symbolic and sub-symbolic elements, ACT-R explicitly self-identifies as hybrid, while Soar does not (Laird 2012b). The surveys are inconsistent as well, for instance, both Soar and ACT-R are called cognitivist in Vernon et al. (2007) and Goertzel et al. (2010b), while (Asselman et al. 2015) lists them as hybrid.

Further complicating our analysis is the fact that the authors of the architectures rarely mention the types of representations used in their systems. Specifically, out of the 84 architectures we surveyed, only 34 have a self-assigned label. Fewer still provide details on the particular symbolic/subsymbolic processes or elements.

It is universally agreed that symbols (labels, strings of characters, frames), production rules and non-probabilistic logical inference are symbolic and that distributed representations such as neural networks are sub-symbolic. For example, probabilistic action selection is considered as symbolic in CARACaS (Huntsberger and Stoica 2010), CHREST (Schiller and Gobet 2012) and CogPrime (Goertzel 2012), but is described as sub-symbolic in ACT-R (Lebiere et al. 2013), CELTS (Faghihi et al. 2011b), CoJACK (Ritter et al. 2012), Copycat/Metacat (Marshall 2006) and iCub (Sandini et al. 2007). Likewise, numeric data is treated as symbolic in CAPS (Just and Varma 2007), AIS (Hayes-Roth 1995) and EPIC (Kieras 2004), but is regarded as sub-symbolic in SASE (Weng 2002). Similarly, there are disagreements regarding the status of the activations (weights or probabilities) assigned to the symbols, reinforcement learning, image data, etc.¹⁰

To avoid inconsistent grouping, we did not rely on self-assigned labels or conflicting classification of elements found in the literature. We assume that explicit symbols are atoms of symbolic representation and can be combined to form meaningful expressions and used for inference or syntactical parsing. Sub-symbolic representations are generally associated with the metaphor of a neuron. A typical example of such representation is the neural network, where knowledge is encoded as a numerical pattern distributed among many simple neuron-like objects. Weights associated with units control processing and are acquired by learning. For our classification, we assume that anything that is not an explicit symbol and processing other than syntactic manipulation is sub-symbolic (e.g. numeric data, pixels, probabilities, spreading activations, reinforcement learning, etc.). Hybrid representation may combine any number of elements from both representations.

Given these definitions, we assigned labels to all the architectures and visualized them as shown in Fig. 3. We distinguish between two groups in the emergent category: neuronal models, which implement models of biological neurons, and connectionist logic systems, which are closer to artificial neural networks. Within the hybrid architectures we separate symbolic sub-processing as a type of hybridization where a symbolic architecture is combined with self-contained module performing sub-symbolic computation, e.g. for processing sensory data. Other types of functional hybrids also exist, for example, co-processing, meta-processing and chain-processing, however, these are harder to identify from the limited data available in the publications and thus are not included. The fully integrated category combines all other types of hybrids.

The fully integrated architectures use a variety of approaches for combining different representations. ACT-R, Soar, CAPS, Copycat/Metacat, CHREST, CHARISMA, CELTS, CoJACK, CLARION, REM, NARS and Xapagy combine symbolic concepts and rules with sub-symbolic elements such as activation values, spreading activation, stochastic selection process, reinforcement learning, etc. On the other hand, DUAL consists of a large number of highly interconnected hybrid agents, each of which has a symbolic and sub-symbolic representation, implementing integration at a micro-level. In the case of SiMA hybridization is done on multiple levels: neural networks are used to build neurosymbols from sensors and actuators (connectionist logic systems) and the top layer is a symbol processing system (symbolic subprocessing). For the rest of the architectures, it is hard to clearly identify how hybridization was realized. For instance, many architectures are implemented as a set of interconnected competing and cooperating modules, where individual modules are not restricted to a particular representation (Kismet, LIDA, MACSi, ISAC, iCub, GMU-BICA, CORTEX, ASMO, CELTS, PolyScheme, FORR).

Another hybridization strategy is to combine two different stand-alone architectures with complementary features. Even though additional effort is required to build interfaces for communication between them, this approach takes advantage of the strengths of each architecture. A good overview of conceptual and technical challenges involved in creating an interface between cognitive and robotic architectures is given in Scheutz et al. (2013). The proposed framework is demonstrated with two examples: ACT-R/DIARC and ICARUS/DIARC integration. Other attempts found in the literature are CERA-CRANIUM/Pogamut (Arrabales et al. 2009c) and CERA-CRANIUM/Soar (Llargues Asensio et al. 2014) hybrids for playing video games and IMPRINT/ACT-R intended for human error modeling (Lebiere et al. 2002).

Besides hybrids built for specific purposes, few long-standing projects (based on the number of publications) also exist. A canonical example is SAL, which comprises ACT-R and Leabra architectures (Jilk et al. 2008). Here ACT-R is used to guide learning of Leabra models. Another instance is the robotic architecture ADAPT, where Soar is utilized for control and separate modules are responsible for modeling a 3D world from sensor information and for visual processing (Benjamin et al. 2013).

Compared to hybrids, emergent architectures form a more uniform group. As mentioned before, the main difference between neuronal modeling and connectionist logic systems is in their biological plausibility. All systems in the first group implement particular neuron models and aim to accurately reproduce the low-level brain processes. The architectures in the second group are based on artificial neural networks. Despite being heavily influenced by neuroscience and having the ability to closely model certain elements of human cognition, the biological plausibility of these models is not claimed by their authors.

In conclusion, as can be seen in Fig. 3, hybrid architectures are the most numerous and diverse group, showing the tendency to grow even more, thus confirming a prediction made almost a decade ago (Duch et al. 2008). Hybrid architectures form a continuum between emergent and symbolic systems depending on the proportions and roles played by symbolic and sub-symbolic components. Although quantitative analysis of this space is not feasible, it is possible to crudely subdivide it. For instance, some architectures such as CogPrime and Sigma are conceptually closer to emergent systems as they share many properties with the neural networks. On the other hand, REM, CHREST, and RALPH, as well as the architectures implementing symbolic sub-processing, e.g. 3T and ATLANTIS, are very much within the cognitivist paradigm. These architectures are primarily symbolic but also utilize probabilistic reasoning and learning mechanisms.

Regardless of its design and purpose, an intelligent system cannot exist in isolation and requires input to produce behavior. Although historically all major cognitive architectures focused on higher-level reasoning, it is becoming evident that perception and action also play an important role in human cognition (Anderson et al. 2004).

Perception is a process that transforms raw input into the system’s internal representation for carrying out cognitive tasks. Depending on the origin and properties of the incoming data, multiple sensory modalities are distinguished. The five most common ones are vision, hearing, smell, touch and taste. Other recognized human senses include proprioception, thermoception, nocioception, sense of time, etc. Naturally, cognitive architectures implement some of these, as well as other modalities that do not have a correlate among human senses such as symbolic input [e.g. input via keyboard or graphical user interface (GUI)] and various sensors such as LiDAR, laser, compass, etc.

Depending on its cognitive capabilities, an intelligent system can process various amounts and types of data as perceptual input. In this section we will investigate the diversity of the data inputs used in cognitive architectures, what information is extracted from these sources and how it is applied. The visualization in Fig. 4 addresses the first part of this question by mapping the cognitive architectures to the sensory modalities they implement: vision (V), hearing (A), touch (T), smell (S), proprioception (P) as well as the data input (D), other sensors (O) and multi-modal (M).¹¹ Note that data presented in the diagram is aggregated from many publications and most architectures do not demonstrate all listed sensory modalities in a single model.

Several observations can be made from the visualization in Fig. 4. For instance, vision is the most commonly implemented sensory modality, however, more than half of the architectures use simulation for visual input instead of the physical camera. Modalities such as touch and proprioception are mainly used in the physically embodied architectures. Few senses remain relatively unexplored, e.g. sense of smell is only featured in three architectures [GLAIR (Shapiro and Kandefer 2005), DAC (Mathews et al. 2009) and PRS (Taylor and Padgham 1996)]. As symbolic architectures by design have limited perceptual abilities, they tend to use direct data input as the only source of information (see left side of the diagram). On the other hand, hybrid and emergent architectures (located mainly in the right half of the diagram) implement a broader variety of sensory modalities both with simulated and physical sensors.

In any case, regardless of its source, incoming sensory data is usually not usable in the raw form (except, maybe, for symbolic input) and requires further processing. Below we discuss various approaches to the problem of efficient and adequate perceptual processing in cognitive architectures.

Perceptual attention plays an important role in human cognition, as it mediates the selection of relevant and filters out irrelevant information from the incoming sensory data. However, it would be wrong to think of attention as a monolithic structure that decides what to process next. Rather, the opposite may be true. There is ample evidence in favor of attention as a set of mechanisms affecting both perceptual and cognitive processes (Tsotsos and Kruijne 2014). Currently, visual attention remains the most studied form of attention, as there are no comprehensive attentional frameworks for other sensory modalities. Since only a few architectures have rudimentary mechanisms for modulating auditory data [OMAR (Deutsch et al. 1997), iCub (Ruesch et al. 2008), EPIC (Kieras et al. 2016) and MIDAS (Gore et al. 2009)], this section will be dedicated to visual attention.

For the following analysis, we use the taxonomy of attentional mechanisms proposed by Tsotsos (2011). Elements of attention are grouped into three classes of information reduction mechanisms: selection (choose one from many), restriction (choose some from many) and suppression (suppress some from many). Selection mechanisms include gaze and viewpoint selection, world model (selection of objects/events to focus on) and time/region/features/objects/events of interest. Restriction mechanisms serve to prune the search space by priming¹³ (preparing the visual system for input based on task demands), endogenous motivations (domain knowledge), exogenous cues (external stimuli), exogenous task (restrict attention to objects relevant for the task), and visual field (limited field of view). Suppression mechanisms consist of feature/spatial surround inhibition (temporary suppression of the features around the object while attending), task-irrelevant stimuli suppression, negative priming, and location/object inhibition of return (a bias against returning attention to previous attended location or stimuli). Finally, branch-and-bound mechanisms combine elements of suppression, selection and restriction. For more detailed explanations of the attentional mechanisms and review of the relevant psychological and neuroscience literature refer to Tsotsos (2011).

The diagram in Fig. 6 shows a summary of the implemented visual attention elements in the cognitive architectures. Here we only consider the architectures with implemented real or simulated vision and omit those with insufficient technical details (as in the previous section). It is evident that most of the implemented mechanisms of attention belong to the selection and restriction group.

Only a handful of architectures implement suppression mechanisms. For instance, suppression of task-irrelevant visual information is explicitly done in only four architectures: in BECCA irrelevant features are suppressed through the WTA mechanisms (Rohrer 2011c), in DSO background features are suppressed to speed up processing (Xiao et al. 2015), in MACSi depth is used to ignore regions that are not reachable by the robot (Lyubova et al. 2013) and in ARCADIA non-central regions of the visual field are inhibited at each cycle since the cue always appears in the center (Bridewell and Bello 2015). Another suppression mechanism is inhibition of return (IOR), which prevents the visual system from repeatedly attending to the same salient stimuli. In ACT-R activation values and distance are used to ignore objects for consecutive WHERE requests (Nyamsuren and Taatgen 2013). In ART, iCub and STAR an additional map is used to keep records of the attended locations. The temporal nature of inhibition can be modeled with a time decay function, as it is done in iCub (Ruesch et al. 2008). ARCADIA mentions a covert inhibition mechanism, however, specific implementation details are not provided (Bridewell and Bello 2015).

Selection mechanisms are much more common in the cognitive architectures. For example, a world model by default is a part of any visual system. Likewise, viewpoint/gaze selection is a necessary component of active vision systems. Specifically, gaze control allows to focus on a region in the environment and viewpoint selection allows to get more information about the region/object by moving away/towards it or by viewing it from different angles. As a result, physically embodied architectures automatically support viewpoint selection because a camera installed on a robot can be moved around the environment. Eye movements are usually simulated, but some humanoid robot implement them physically (iCub, Kismet).

Other mechanisms in this group include selection of various features/objects of interest. The selection of visual data to attend can be data-driven (bottom-up) or task-driven (top-down). The bottom-up attentional mechanisms identify salient regions whose visual features are distinct from the surrounding image features, usually along a combination of dimensions, such as color channels, edges, motion, etc. Some architectures resort to the classical visual saliency algorithms, such as Guided Search (Wolfe 1994) used in ACT-R (Nyamsuren and Taatgen 2013) and Kismet (Breazeal and Scassellati 1999), the Itti-Koch-Niebur model (Itti et al. 1998) used by ARCADIA (Bridewell and Bello 2015), iCub (Ruesch et al. 2008) and DAC (Mathews et al. 2012) or AIM (Bruce and Tsotsos 2007) used in STAR (Kotseruba 2016). Other approaches include filtering [DSO (Xiao et al. 2015)], finding unusual motion patterns [MACSi (Ivaldi et al. 2014)] or discrepancies between observed and expected data [RCS (Albus et al. 2002)].

Top-down selection can be applied to further limit the sensory data provided by the bottom-up processing. A classic example is visual search, where knowing desired features of the object (e.g., the color red) can narrow down the options provided by the data-driven Fig.-ground segmentation. Many architectures resort to this mechanism to improve search efficiency [ACT-R (Salvucci 2000), APEX (Freed 1998), ARCADIA (Bello et al. 2016), CERA-CRANIUM (Arrabales et al. 2009a), CHARISMA (Conforth and Meng 2011a), DAC (Mathews et al. 2012)]. Another option is to use a hard-coded or learned heuristics. For example, CHREST looks at typical positions on a chess board (Lane et al. 2009) and MIDAS replicates common eye scan patterns of pilots (Gore et al. 2009). The limitation of the current top-down approaches is that they can direct vision for only a limited set of predefined visual tasks, however, ongoing research in STAR attempts to address this problem (Tsotsos 2011; Tsotsos and Kruijne 2014).

Restriction mechanisms allow to further reduce the complexity of vision by limiting the search space to certain features (priming), events (exogenous cues), space (visual field), objects (exogenous task) and knowledge (endogenous motivations). Exogenous task and endogenous motivations are implemented in all architectures by default as domain knowledge and task instructions. In addition to that, attention can be modulated by internal signals such as motivation, emotions, which will be discussed in the next section on action selection.

A limited visual field is a feature of any physically embodied system since most cameras do not have a 360-degree field of view. This feature is also present in some simulated vision systems. An ability to react to the sudden events (exogenous cues), such as fast movement or bright color, is useful in social robotics applications [e.g. ISAC (Kawamura et al. 2004), Kismet (Breazeal et al. 2000)]. On the other hand, priming allows to bias the visual system towards particular types of stimuli via task instruction, e.g. by assigning more weight to skin-colored features during the saliency computation to improve human detection [Kismet (Breazeal et al. 2001)] or by utilizing the knowledge of the probable location of some objects to spatially bias detection [STAR (Kotseruba 2016)]. Neural mechanisms of top-down priming are studied in ART models (Carpenter and Grossberg 1987).

Conclusion Overall, visual attention is largely overlooked in cognitive architectures research with the exception of the biologically plausible visual models (e.g. ART) and the architectures that specifically focus on vision research (ARCADIA, STAR).¹⁴ This is surprising because strong theoretical arguments as to its importance in dealing with the computational complexity of visual processing have been known for decades (Tsotsos 1990). Many of the attention mechanisms found in the cognitive architectures are side-effects of other design decisions. For example, task constraints, world model and domain knowledge are necessary for the functioning of other aspects of intelligent system and are implemented by default. Limited visual field and viewpoint changes often result from physical embodiment. Otherwise, region of interest selection and visual reaction to exogenous cues are the two most common mechanisms explicitly included for optimizing visual processing.

In the cognitive and psychological literature, attention is also used as a broad term for the allocation of limited resources (Rosenbloom et al. 2015b). For instance, in the Global Workspace Theory (GWT) (Baars 2005) attentional mechanisms are central to perception, cognition and action. According to GWT, the nervous system is organized as multiple specialized processes running in parallel. Coalitions of these processes compete for attention in the global workspace and the contents of the winning coalition are broadcast to all other processes. For instance, the LIDA architecture is an implementation of GWT¹⁵ (Franklin et al. 2012). Other architectures influenced by the GWT include ARCADIA (Bridewell and Bello 2015) and CERA-CRANIUM (Arrabales et al. 2009b).

Along the same lines, cognition is simulated as a set of autonomous independent processes in ASMO [inspired by Society of Mind (Minsky 1986)]. Here an attention value for each process is either assigned directly or learned from experience. Attention values vary dynamically and affect action selection and resource allocation (Novianto et al. 2010). A similar idea is implemented in COGNET (Zachary et al. 1993), DUAL (Kiryazov et al. 2007) and Copycat/Metacat (Mitchell and Hofstadter 1990) in which multiple processes also compete for attention. Other computational mechanisms, such as queue [PolyScheme (Kurup et al. 2011)], Hopfield nets [CogPrime (Ikle and Goertzel 2011)] and modulators [MicroPsi (Bach 2015)] have been used to implement a focus of attention. In MLECOG, in addition to saccades that operate on visual data, mental saccades allow to switch between the most activated memory neurons (Starzyk and Graham 2015).

Informally speaking, action selection determines at any point in time “what to do next”. At the highest level, action selection can be split into the “what” part involving the decision making and the “how” part related to motor control (Ozturk 2009). However, this distinction is not always explicitly made in the literature, where action selection may refer to goal, task or motor commands. For example, in the MIDAS architecture action selection involves both the selection of the next goal to pursue as well as actions that implement it (Tyler et al. 1998). Similarly, in MIDCA the next action is normally selected from a planned sequence if one exists. In addition to this, a different mechanism is responsible for the adoption of the new goals based on dynamically determined priorities (Paisner et al. 2013). In COGNET and DIARC, selection of a task/goal triggers an execution of the associated procedural knowledge (Zachary et al. 2016; Brick et al. 2007). In DSO, the selector module chooses between available actions or decisions to reach current goals and sub-goals (Ng et al. 2012). Since the treatment of action selection in various cognitive architectures is inconsistent, in the following discussion the term “action selection mechanisms” may apply both to decision-making and motor actions.

The diagram in Fig. 7 illustrates all implemented action selection mechanisms organized by the type of the corresponding architecture (symbolic, hybrid and emergent). We distinguish between two major approaches to action selection: planning and dynamic action selection. Planning refers to the traditional AI algorithms for determining a sequence of steps to reach a certain goal or to solve a problem in advance of their execution. In dynamic action selection, one best action is chosen among the alternatives based on the knowledge available at the time. For this category, we consider the type of selection (winner-take-all (WTA), probabilistic, predefined) and criteria for selection (relevance, utility, emotion). The default choice is always the best action based on the defined criteria (e.g. action with the highest activation value). Reactive actions are executed bypassing action selection. Finally, learning can also affect action selection but will be discussed in Sect. 8. Note that these action selection mechanisms are not mutually exclusive and most of the architectures have more than one. Even though few architectures implement the same set of action selection mechanisms (as can be seen in the Fig. 7), the whole space of valid combinations is likely much larger. Below we discuss approaches to action selection and what cognitive processes they correspond to in more detail.

Memory is an essential part of any systems-level cognitive model, regardless of whether the model is being used for studying the human mind or for solving engineering problems. Thus, nearly all the architectures featured in this review have memory systems that store intermediate results of computations, enabling learning and adaptation to the changing environment. However, despite their functional similarity, particular implementations of memory systems differ significantly and depend on the research goals and conceptual limitations, such as biological plausibility and engineering factors (e.g. programming language, software architecture, use of frameworks, etc.).

In the cognitive architecture literature, memory is described in terms of its duration (short- and long-term) and type (procedural, declarative, semantic, etc.), although it is not necessarily implemented as separate knowledge stores. This multi-store memory concept is influenced by the Atkinson-Shiffrin model (1968), later modified by Baddeley (Baddeley and Hitch 1974). Such view of memory is dominant in psychology, but its utility for engineering is questioned by some because it does not provide a functional description of various memory mechanisms (Perner and Zeilinger 2011). Nevertheless, most architectures do distinguish between various memory types, although naming conventions may differ. For instance, architectures designed for planning and problem solving have short- and long-term memory storage systems but do not use terminology from cognitive psychology. Long-term knowledge in planners is usually referred to as a knowledge base for facts and problem-solving rules, which correspond to semantic and procedural long-term memory [e.g. Disciple (Tecuci et al. 2000), MACSi (Ivaldi et al. 2014), PRS (Georgeff and Lansky 1986), ARDIS (Martin et al. 2011), ATLANTIS (Gat 1992), IMPRINT (Brett et al. 2002)]. Some architectures also save previously implemented tasks and solved problems, imitating episodic memory [REM (Murdock and Goel 2001), PRODIGY (Veloso 1993)]. Short-term storage in planners is usually represented by a current world model or the contents of the goal stack.

Figure 8 shows a visualization of various types of memory implemented by the architectures. Here we follow the convention of distinguishing between the long-term and short-term storage. Long-term storage is further subdivided into semantic, procedural and episodic types, which store factual knowledge, information on what actions should be taken under certain conditions and episodes from the personal experience of the system respectively. Short-term storage is split into sensory and working memory following (Cowan 2008). Sensory or perceptual memory is a very short-term buffer that stores several recent percepts. Working memory is a temporary storage for percepts that also contains other items related to the current task and is frequently associated with the current focus of attention.

Learning is the capability of a system to improve its performance over time. Ultimately, any kind of learning is based on experience. For example, a system may be able to infer facts and behaviors from the observed events or from results of its own actions. The type of learning and its realization depend on many factors, such as design paradigm (e.g. biological, psychological), application scenario, data structures and the algorithms used for implementing the architecture, etc. However, we will not attempt to analyze all these aspects given the diversity and number of the cognitive architectures surveyed. Besides, not all of these pieces of information can be easily found in the publications.

Thus, a more general summary is preferable, where types of learning are defined following taxonomy by Squire (1992). Learning is divided into declarative or explicit knowledge acquisition and non-declarative, which includes perceptual, procedural, associative and non-associative types of learning. Figure 9 shows a visualization of these types of learning for all cognitive architectures.

We also identified 19 architectures (mostly symbolic and hybrid) that do not implement any learning. In some areas of research, learning is not even necessary, for example, in human performance modeling, where accurate replication of human performance data is required instead (e.g. APEX, EPIC, IMPRINT, MAMID, MIDAS, etc.). Some of the newer architectures are still in the early development stage and may add learning in the future (e.g. ARCADIA, SiMA, Sigma).

Reasoning, originally a major research topic in philosophy and epistemology, in the past decades has become one of the focal points in psychology and cognitive sciences as well. As an ability to logically and systematically process knowledge, reasoning can affect or structure virtually any form of human activity. As a result, aside from the classic triad of logical inference (deduction, induction and abduction), other kinds of reasoning are now being considered, such as heuristic, defeasible, analogical, narrative, moral, etc.

Predictably, all cognitive architectures are concerned with practical reasoning, whose end goal is to find the next best action and perform it, as opposed to the theoretical reasoning that aims at establishing or evaluating beliefs. There is also a third option, exemplified by the Subsumption architecture, which views reasoning about actions as an unnecessary step (Brooks 1987) and instead pursues physically grounded action (Brooks 1990). Granted, one could still argue that a significant amount of reasoning and planning is required from a designer to construct a grounded system with non-trivial capabilities. Otherwise, in the context of cognitive architectures, reasoning is primarily mentioned with regards to planning, decision-making and learning, as well as perception, language understanding and problem-solving.

One of the main challenges a human, and consequently any human-level intelligence, faces regularly is acting based on insufficient knowledge or “making rational decisions against a background of pervasive ignorance” (Pollock 2008). In addition to sparse domain knowledge, there are limitations in terms of available internal resources, such as information processing capacity. Furthermore, external constraints, such as real-time execution, may also be introduced by the task. It should be noted that even in the absence of these restrictions, reasoning is by no means trivial. Consider, for instance, the complex machinery used by Copycat and Metacat to model analogical reasoning in a micro-domain of character strings (Hofstadter 1993).

So far, the most consolidated effort has been spent on overcoming the insufficient knowledge and/or resources problem in continuously changing environments. In fact, this is the goal of general-purpose reasoning systems such as the Procedural Reasoning System (PRS), the Non-Axiomatic Reasoning System (NARS), OSCAR and the Rational Agent with Limited-Performance Hardware (RALPH). PRS is one of the earliest instances of the BDI (belief-desire-intention) model. In each reasoning cycle, it selects a plan matching the current beliefs, adds it to the intention stack and executes it. If new goals are generated during the execution, new intentions are created (Georgeff and Ingrand 1989). NARS approaches the issue of insufficient knowledge and resources by iteratively reevaluating existing evidence and adjusting its solutions accordingly. This is possible with Non-Axiomatic Logic, which associates truth values with each statement thus allowing agents to express their confidence in the belief, a feature not available in PRS (Hammer et al. 2016). RALPH tackles complex goal-driven behavior in complex domain using decision-theoretic approach. Thus, computation is synonymous with action and action with the highest utility value (based on its expected effect) is always selected (Russel and Wefald 1988). The OSCAR architecture explores defeasible reasoning, i.e. reasoning which is rationally compelling but not deductively valid (Koons 2017). This is a more accurate representation of everyday reasoning, where knowledge is sparse, tasks are complex and there are no certain criteria for measuring success.

All the architectures mentioned above aim at creating rational agents with human-level intelligence but they do not necessarily try to model human reasoning processes. This is the goal of architectures such as ACT-R, Soar, DUAL and CLARION. In particular, Human Reasoning Module implemented in ACT-R works under the assumption that human reasoning is probabilistic and inductive (although deductive reasoning may still occur in the context of some tasks). This is demonstrated by combining a deterministic rule-based inference mechanism with long-term declarative memory with properties similar to human memory, i.e. incomplete and inconsistent knowledge. Together, the uncertainty inherent in the knowledge and production rules enable human-like reasoning behavior, which has been confirmed experimentally (Nyamsuren and Taatgen 2014). Similarly, symbolic and sub-symbolic mechanisms in CLARION (Helie and Sun 2014) and DUAL (Kokinov 1994) architectures are used to model and explain various psychological phenomena associated with deductive, inductive, analogical and heuristic reasoning.

As discussed in Sect. 3, the question of whether higher human cognition is inherently symbolic or not is still unresolved. The symbolic and hybrid architectures considered so far, view reasoning mainly as a symbolic manipulation. Given that in emergent architectures the information is represented by the weights between individual units, what kind of reasoning, if any, can they support? It appears, based on the cognitive architecture literature, that many of the emergent architectures, e.g. ART, HTM, DAC, BBD, BECCA, simply do not address reasoning, although they certainly exhibit complex intelligent behavior. On the other hand, since it is possible to establish a correspondence between the neural networks and logical reasoning systems (Martins and Mendes 2001), then it should also be possible to simulate symbolic reasoning via neural mechanisms. Indeed, several architectures demonstrate symbolic reasoning and planning (SASE, SHRUTI, MicroPsi). To date, one of the most successful implementations of symbolic reasoning (and other low- and high-level cognitive phenomena) in a neural architecture is represented by SPA (Rasmussen and Eliasmith 2013). Architectures like this also raise interesting questions whether it makes sense to try and segregate symbolic parts of cognition from sub-symbolic. As most existing cognitive architectures represent a continuum from purely symbolic to connectionist, the same may be true for the human cognition as well.

Metacognition (Flavell 1979), intuitively defined as “thinking about thinking”, is a set of abilities that introspectively monitor internal processes and reason about them.¹⁶ There has been a growing interest in developing metacognition for artificial agents, both due to its essential role in human experience and practical necessity for identifying, explaining and correcting erroneous decisions. Approximately one-third of the surveyed architectures, mainly symbolic or hybrid ones with a significant symbolic component, support metacognition with respect to decision-making and learning. Here we will focus on three most common metacognitive mechanisms, namely self-observation, self-analysis and self-regulation.

Self-observation allows gathering data pertaining to the internal operation and status of the system. The data commonly includes the availability/requirements of internal resources [AIS (Hayes-Roth 1995), COGNET (Zachary et al. 2000), Soar (Laird 2012a)] and current task knowledge with associated confidence values [CLARION (Sun 2016), CoJACK (Evertsz et al. 2007), Companions (Friedman et al. 2011), Soar (Laird 2012a)]. In addition, some architectures support a temporal representation (trace) of the current and/or past solutions [Companions (Friedman et al. 2011), Metacat (Jensen and Veloso 1998), MIDCA (Dannenhauer et al. 2014)].

Overall, the amount and granularity of the collected data depend on the further analysis and application. In decision-making applications, after establishing the amount of available resources, conflicting requests for resources, discrepancies in the conditions, etc., the system may change the priorities of different tasks/plans [AIS (Hayes-Roth 1995), CLARION (Sun 2016), COGNET (Zachary et al. 2000), CoJACK (Evertsz et al. 2007), MAMID (Hudlicka 2009), PRODIGY (Veloso et al. 1998), RALPH (Ogasawara and Russell 1993)]. The trace of the execution and/or recorded solutions for the past problems can improve learning. For example, repeating patterns found in past solutions may be exploited to reduce computation for similar problems in the future [Metacat (Marshall 2002b), FORR (Epstein and Petrovic 2008), GLAIR (Shapiro and Bona 2010), Soar (Laird 2012a)]. Traces are also useful for detecting and breaking out of repetitive behavior [Metacat (Marshall 2002b)], as well as stopping learning if the accuracy does not improve [FORR (Epstein and Petrovic 2008)].

Although in theory many architectures support metacognition, its usefullness has only been demonstrated in limited domains. For instance, Metacat applies metacognition to analogical reasoning in a micro-domain of strings of characters (e.g. if abc \(\rightarrow \) abd; mrrjjj \(\rightarrow \) ?). Self-watching allows the system to remember past solutions, compare different answers and justify its decisions (Marshall 2002b). In PRS, metacognition enables efficient problem solving in playing games such as Othello (Russell and Wefald 1989), better control of the simulated vehicle (Ogasawara and Russell 1993) and real-time adaptability to continuously changing environments (Georgeff and Ingrand 1989). Internal error monitoring, i.e. the comparison between the actual and expected perceptual inputs, enables the SAL architecture to determine the success or failure of its actions in a simple task, such as stacking blocks, and to learn better actions in the future (Vinokurov et al. 2013). The metacognitive abilities integrated into the dialog system make the CoSy architecture more robust to variable dialogue patterns and increase its autonomy (Christensen and Kruijff 2010). The Companions architecture demonstrates the importance of self-reflection by replicating data from the psychological experiments on how people organize, represent and combine incomplete domain knowledge into explanations (Friedman et al. 2011). Likewise, CLARION models human data for the Metcalfe (1986) task and replicates the Gentner and Collins (1981) experiment, both of which involve metacognitive monitoring (Sun et al. 2006).

Metacognition is also necessary for social cognition, particularly for the skill known in the psychological literature as a Theory of Mind (ToM). ToM refers to being able to acknowledge and understand mental states of other people, use the judgment of their mental state to predict their behavior and inform one’s own decision-making. Very few architectures support this ability. For instance, recently Sigma demonstrated two distinct mechanisms for ToM in several single-stage simultaneous-move games, such as the well-known Prisoner’s dilemma. The first mechanism is automatic as it involves probabilistic reasoning over the trellis graph and the second is a combinatorial search across the problem space (Pynadath et al. 2013).

PolyScheme applies ToM to perspective taking in a human-robot interaction scenario. The robot and human in this scenario are together in a room with two traffic cones and multiple occluding elements. The human gives a command to move towards a cone, without specifying which one. If only one cone is visible to the human, the robot can model the scene from the human’s perspective and use this information to disambiguate the command (Trafton et al. 2005). Another example is reasoning about beliefs in a false belief task. In this scenario, two agents A and B observe a cookie placed in a jar. After B leaves, the cookie is moved to another jar. When B comes back, A is able to reason that B still believes that cookie is still in the first jar (Scally et al. 2012).

ACT-R was used to build several models of false belief and second-order false belief tasks (answering questions of the kind “Where does Ayla think Murat will look for chocolate?”) which are typically used to assess whether children developed a Theory of Mind (Triona et al. 2001; Barslanrugnl et al. 2004). However, of particular interest is the recent developmental model of ToM based on ACT-R, which was subsequently implemented on a mobile robot. Several scenarios were set up to show how ToM ability can improve the quality of interaction between the robot and humans. For instance, in a patrol scenario both the robot and human receive the task of patrolling the south area of the building, but as they start, the instructions are changed to head west. When the human starts walking towards south, the robot infers that she might have forgotten about the new task and reminds her of the new changes (Trafton et al. 2013).

Cognitive architectures reviewed in this paper are mainly used as research tools and very few are developed outside of academia. However, it is still appropriate to talk about their practical applications, since useful and non-trivial behavior in arbitrary domains is perceived as intelligent and in a sense validates the underlying theories. We seek answers to the following questions: what cognitive abilities have been demonstrated by the cognitive architectures and what particular practical tasks have they been applied to?

After a thorough search through the publications, we identified more than 900 projects implemented using 84 cognitive architectures. Our findings are summarized in Fig. 10. It shows the cognitive abilities associated with applications, the total number of applications for each architecture (represented by the length of the bars) and what application categories they belong to (the types of categories and the corresponding number of applications in each category are shown by the color and length of the bar segments respectively).

The main contribution of this survey is in gathering and summarizing information on a large number of cognitive architectures influenced by various disciplines (computer science, cognitive psychology, philosophy and neuroscience). In particular, we discuss common approaches to modeling important elements of human cognition, such as perception, attention, action selection, learning, memory and reasoning. In our analysis, we also point out what approaches are more successful in modeling human cognitive processes or exhibiting useful behavior. In order to evaluate practical aspects of cognitive architectures we categorize their existing practical applications into several broad categories. Furthermore, we map these practical applications to a number of competency areas required for human-level intelligence to assess the current progress in that direction. This map may also serve as an approximate indication of what major areas of human cognition have received more attention than others. Thus, there are two main outcomes of this review. First, we present a broad and inclusive snapshot of the progress made in cognitive architectures research over the past four decades. Second, by documenting the wide variety of tested mechanisms that may help in developing explanations and models for observed human behavior, we inform future research in cognitive science and in the component disciplines that feed it. In the remainder of this section we will summarize the main themes discussed in the present survey and pertaining issues which can guide directions for future research.

Even though they are often described as opposing, in practice the the two paradigms are not as separated in practice (which causes inconsistencies in the survey literature as discussed in Sect. 2). Despite the differences in the theory and terminology both cognitive and agent architectures have functional modules corresponding to human cognitive abilities and tackle the issues of action selection, adaptive behavior, efficient data processing and storage. For example, methods of action selection widely used in robotics and classic AI, ranging from priority queue to reinforcement learning (Pirjanian 1999), are also found in many cognitively-oriented architectures. Likewise, some agent architectures incorporate cognitively plausible structures and mechanisms.

Biologically and neurally plausible models of human mind primarily aim to explain how known cognitive and behavioral phenomena arise from low-level brain processes. It has been argued that their support for inference and general reasoning is inadequate for modeling all aspects of human cognition, although some of the latest neuronal models demonstrate that it is far from being proven.

The newest paradigm in AI is represented by machine learning methods, particularly data-driven deep learning, which have found enormous practical success in limited domains and even claim some biological plausibility. The bulk of work in this area is on perception although there have been attempts at implementing more general inference and memory mechanisms. The results of these efforts are stand-alone models that have yet to be incorporated within a unified framework. At present these new techniques are not widely incorporated into existing cognitive architectures.

Range of supported cognitive abilities and remaining gaps The list of cognitive abilities and phenomena covered in this survey is by no means exhaustive. However, none of the systems we reviewed is close to supporting in theory or demonstrating in practice even this restricted subset, let alone a set of identified cognitive abilities (a comprehensive survey by Carroll (1993) lists nearly 3000 of them).

Most effort thus far has been dedicated to studying high-level abilities such as action selection (reactive, deliberative and, to some extent, emotional), memory (both short and long-term), learning (declarative and non-declarative) and reasoning (logical inference, probabilistic and meta-reasoning). Decades of work in these areas, both in traditional AI research and cognitive architectures, resulted in creation of multiple algorithms and data structures. Furthermore, various combinations of these approaches have been theoretically justified and practically validated in different architectures, albeit under restricted conditions.

We only briefly cover abilities that rely on the core set of perception, action-selection, memory and learning. At present, only a fraction of the architectures have the theoretical and software/hardware foundation to support creative problem solving, communication and social interaction (particularly in the fields of social robotics and HCI/HRI), natural language understanding and complex motor control (e.g. grasping).

With respect to the range of abilities and realism of the scenarios there remains a significant gap between the state-of-the-art in specialized areas of AI and the research in these areas within the cognitive architectures domain. Arguably some of the AI algorithms demonstrate achievements that are already on par with or even exceed human abilities [e.g. in image classification (He et al. 2014), face recognition (Taigman et al. 2014), playing simple video games (Mnih et al. 2015)], which raises the bar for cognitive architectures.

Due to the complexity of modeling the human mind, groups of researchers have been pursuing complementary lines of investigation. Owing to tradition, work on high-level cognitive architectures is still centered mainly on reasoning and planning in simulated environments, downplaying issues of cognitive control of perception, symbol grounding and realistic attention mechanisms. On the other hand, embodied architectures must deal with the real-world constraints and thus pay more attention to low-level perception-motor coupling, while most of the deliberative planning and reasoning effort is spent on navigation and motor control. There are, however, trends towards overcoming these differences as classical cognitive architectures experiment with embodiment (Mohan et al. 2012; Trafton et al. 2013) or are being interfaced with robotic architectures (Scheutz et al. 2013) to utilize their respective strengths.

There also remains a gap between the biologically inspired architectures and neural simulations and the rest of the architectures. Even though low-level models can demonstrate how neural processes give rise to some higher-level cognitive functions, they do not have the same range and efficiency in practical applications compared to the less theoretically restricted systems. Some exceptions exist, for example Grok—a commercial application for IT analytics based on the biologically inspired HTM architecture or Neural Information Retrieval System implemented as a hierarchy of ART networks for storing 2D and 3D parts designs built for the Boeing company (Smith et al. 1997).

Finally, most of the featured architectures cannot reuse the capabilities or accumulate knowledge as they are applied to new tasks. Instead, every new task or skill is demonstrated using a separate model, specific set of parameters or knowledge base.²¹ Examples of architectures capable of switching between various scenarios/environments without resetting or changing parameters are sparse and are explicitly declared as such, e.g. the SPA architecture which can perform eight unrelated tasks (Eliasmith and Stewart 2012).

Outstanding issues and future directions One of the outcomes of this survey, besides summarizing past and present attempts at modeling various human cognitive abilities, is highlighting the problems needing further research via interactive visualization. We also reviewed future directions suggested by scholars in a large number of publications in search of problems deemed important. We present some of the main themes below, excluding architecture- and implementation-specific issues.

Adequate experimental validation A large number of papers end in a call for thorough experimental testing of the given architecture in more diverse, challenging and realistic environments (Firby et al. 1995; Nunez et al. 2016; Rohrer 2012) and real-world situations (Sun and Helie 2015) using more elaborate scenarios (Rousseau and Hayes-Roth 1996) and diverse tasks (Herd et al. 2013). This is by far the most pressing and long-standing issue [found in papers from the early 90s (Matthies 1992) up until 2017 (Fedor et al. 2017)] which remains largely unresolved. The data presented in this survey (e.g. Sect. 11.2 on practical applications) shows that, by and large, the architectures (including those vying for AGI) have practical achievements only in several distinct areas and in highly controlled environments. Likewise, common validation methods have many limitations. For instance, replicating psychological experiments, the most frequent method in our sample of architectures, presents the following challenges: it is usually based on a small set of data points from human participants, the amount of prior knowledge for the experiment is often unknown and modeled in an ad hoc manner, pre-processing of stimuli is required in case the architecture does not posess adequate perception, and, furthermore, the task definition is narrow and tests only a particular aspect of the ability (Jones et al. 2012). Overall, such restricted evaluation tells us little about the actual abilities of the system outside the laboratory setting.

Realistic perception The problem of perception is most acute in robotic architectures operating in noisy unstructured environments. Frequently mentioned issues inlcude the lack of active vision (Huber and Kortenkamp 1995), accurate localization and tracking (Wolf et al. 2010), robust performance under noise and uncertainty (Romero-Garcés et al. 2015a) and the utilization of context information to improve detection and localization (Christensen and Kruijff 2010). More recently, deep learning is being considered as a viable option for bottom-up perception (Bona 2013; Ivaldi et al. 2014).

These concerns are supported by our data as well. Overall, in comparison to higher-level cognitive abilities, the treatment of perception, and vision in particular, is rather superficial, both in terms of capturing the underlying processes and practical applications. For instance, almost half of the reviewed architectures do not implement any vision (Fig. 5). The remaining projects, with few exceptions, either default to simulations or operate in controlled environments.

Other sensory modalities, such as audition, proprioception and touch, often rely on off-the-shelf software solutions or are trivially implemented via physical sensors/simulations. Multi-modal perception in the architectures that support it, is approached mechanistically and is tailored for a particular application. Finally, bottom-up and top-down interactions between perception and higher-level cognition are largely overlooked both in theory and practice. As discussed in Sect. 5, many of the mechanisms are a by-product of other design decisions rather than a result of deliberate efforts.

Human-like learning Even though various types of learning are represented in the cognitive architectures, there is still a need for developing more robust and flexible learning mechanisms (Hinrichs and Forbus 2014; Menager and Choi 2016), knowledge transfer (Herd et al. 2013) and accumulation of new knowledge without affecting prior learning (Jonsdottir and Thórisson 2013). Learning is especially important for developmental approach to modeling cognition, specifically motor skills and new declarative knowledge (as discussed in Sect. 8). However, artificial development is restricted and far less efficient because visual and auditory perception in robots is not yet on par with even the small children (Christensen and Kruijff 2010).

Natural communication Verbal communication is the most common mode of interaction between the artificial agents and humans. Many issues are yet to be resolved as current approaches do not possess a sufficiently large knowledge base for generating dialogues (Faghihi et al. 2013) and generally lack robustness (Hinrichs and Forbus 2014; Williams et al. 2015). Few, if any, architectures are capable of detecting the emotional state and intentions of the interlocutor (Breazeal 2003a) or give personalized responses (Gobet and Lane 2012). Non-verbal aspects of communication, such as performing and detecting gestures and facial expressions, natural turn-taking, etc., are investigated by only a handful of architectures, many of which are no longer in development. As a result, demonstrated interactions are heavily scripted and often serve as voice control interfaces for supplying instructions instead of enabling collaboration between human and the machine (Myers et al. 2002) (see also discussion in Sect. 11.2.5).

Autobiographic memory Even though memory, a necessary component of any computational model, is well represented and well researched in the domain of cognitive architectures, episodic memory is comparatively under-examined (Bölöni 2012; Borrajo et al. 2015; Danker and Anderson 2010; Menager and Choi 2016). The existence of this memory structure has been known for decades (Tulving 1972) and its importance for learning, communication and self-reflection is widely recognized, however episodic memory remains relatively neglected in computational models of cognition (see Sect. 8). This is also true for general models of human mind such as Soar and ICARUS, which included it relatively recently.

Currently the majority of the architectures save past episodes as time-stamped snapshots of the state of the system. This approach is suitable for agents with a short life-span and a limited representational detail but more elaborate solutions will be needed for life-long learning and managing large-scale memory storage (Laird and Derbinsky 2009). The issues of scale are relevant to other types of long-term memory as well (Bellas et al. 2014; Henderson et al. 2012; Murdock and Goel 2008).

Computational performance The problem of computational efficiency, for obvious reasons, is more pressing in robotic architectures (Gat and Dorais 1994; Wilson and Scheutz 2014) and interactive applications (Arrabales Moreno and Sanchis de Miguel 2006; Ash and Hayes-Roth 1996). However, it is also reported for non-embodied architectures (Jones et al. 2016; Sun 2016), particularly neural simulations (Tripp and Eliasmith 2016; Wendelken 2003). Overall, time and space complexity is glossed over in the literature, frequently described in qualitative terms (e.g. ’real-time’ without specifics) or omitted from discussion altogether.

A related point is an issue of scale. It has been shown that scaling up existing algorithms to larger amounts of data introduces additional challenges, such as the need for more efficient data processing and storage. For example, the difficulties associated with maintaining a knowledge base equivalent to human brain memory-storage capacity are discussed in Sect. 7. These problems are currently addressed by only a handful of architectures, even though solving them is crucial for further development of theoretical and applied AI.

In addition to concerns collectively expressed by the researches (and supported by the data in this survey) below we examine two more observations: (1) the need for an objective evaluation of the results and measuring the overall progress towards understanding and replicating human-level intelligence and (2) reproducibility of the research in the cognitive architectures.

Surveys are a first step towards mapping the landscape of approaches to modeling human intelligence, but they can only identify the blank spots needing further exploration and are ill-suited for comparative analysis. For instance, in this paper, visualizations show only the existence of any particular feature, be it memory, learning method or perception modality. However, they cannot represent the extent of the ability nor whether it is enabled by the architectural mechanisms or is an ad hoc implementation.²² Thus, any comparisons between the architectures are discouraged and any such conclusions should be taken with caveats.

A compounding factor for evaluation is that cognitive architectures comprise both theoretical and engineering components, which leads to a combinatorial explosion of possible implementations. Consider, for example, the theoretical and technical nuances of various instantiations of the global workspace concept, e.g. ARCADIA (Bridewell and Bello 2015), ASMO (Novianto et al. 2010) and LIDA (Franklin et al. 2012) or the number of planning algorithms and approaches to dynamic action selection in Sect. 6. Furthermore, building a cognitive architecture is a major commitment, requiring years of development to put even the basic components together (average age of projects in our sample is \(\approx 15\) years), making any major changes harder with time. Thus, evaluation is crucial for identifying more promising approaches and pruning suboptimal paths for exploration. To some extent, it has already been happening organically via a combination of theoretical considerations and collective experience gathered over the years. For instance, symbolic representation, once dominant, is being replaced by more flexible emergent approaches (see Fig. 2) or augmented with sub-symbolic elements (e.g. Soar, REM). Clearly, more consistent and directed effort is needed to make this process faster and more efficient.

Ideally, proper evaluation should target every aspect of cognitive architectures using a combination of theoretical analysis, software testing techniques, benchmarking, subjective evaluation and challenges. All of these have already been sparingly applied to individual architectures. For example, a recent theoretical examination of the knowledge representations used in major cognitive architectures reveals their limitations and suggests ways to amend them (Lieto 2016; Lieto et al. 2018a). Over the years, a number of benchmarks and challenges have been developed in other areas of AI and a few have already been used to evaluate cognitive architectures, e.g. 2K BotPrize competition [Pogamut (Gemrot et al. 2010)], various benchmarks, including satellite image classification [ART (Carpenter and Gaddam 2010)], medical data analysis [ART (Carpenter and Milenova 2000)] and face recognition [HTM (Stolc and Bajla 2010)], as well as robotics competitions such as AAAI Robot Competition (DIARC (Schermerhorn et al. 2006) and RoboCup [BBD (Szatmary et al. 2006)].

Tests probing multiple abilities via series of tasks are suggested for evaluating cognitive architectures and general-purpose AI systems. Some examples include, “Cognitive Decathlon” (Mueller and Minnery 2008) and I-Athlon (Adams et al. 2016), both inspired by the Turing test, as well as Catell-Horn-Carroll model examining factors of intelligence (Ichise 2016) or a battery of tests for assessing different cognitive dimensions proposed by Samsonovich et al. (Samsonovich et al. 2006). However, most of the work in this area remains theoretical and has not resulted in solid proposals ready to be implemented or complete frameworks.

For further discussion of ample literature concerning evaluation of AI and cognitive architectures we refer the readers to a very recent and comprehensive review of these methods in Hernandez-Orallo (2017).

Reproducibility of the results Literature on cognitive architectures gives far more importance to the cognitive, psychological or philosophical aspects of modeling human cognition, while technical implementation details are often incomplete or missing. For example, in Sect. 5 on visual processing we list architectures that only briefly mention some perceptual capabilities but are not specific enough for analysis. Likewise, justifications of using particular algorithms/representations along with their advantages and limitations are not always disclosed. Such lack of full technical detail compromises reproducibility of the research.

In part, this issue can be alleviated by providing access to the software/source code, but only one-third of the architectures do so. To a lesser extent this applies to architectures such as BBD, SASE, Kismet, Ymir, Subsumption and a few others that are physically embodied and depend on a particular robotic platform. However, the majority of architectures we reviewed have a substantial software component, releasing which could be of benefit to the community and the researchers themselves. For instance, cognitive architectures such as ART, ACT-R, Soar, HTM and Pogamut, are used by many researchers outside the main developing group.

In conclusion, our hope is that this work will serve as an overview and a guide to the vast field of cognitive architecture research as we strived to objectively and quantitatively assess the state-of-the-art in modeling human cognition.