Advances in Cognitive Informatics and Cognitive Computing

This chapter explores the cutting-edge field of cognitive informatics (CI) and its applications in cognitive computing. CI is a transdisciplinary study of cognitive and information sciences, which investigates the internal information processing mechanisms and processes of the natural intelligence – human brains and minds – and their engineering applications in computational intelligence. The 7th IEEE International Conference on Cognitive Informatics (ICCI 2008) has been organized at Stanford University during August 14-16, 2008. The ICCI’08 program covers a wide spectrum of subjects that contribute to cognitive informatics and cognitive computing. This chapter highlights the latest advances in CI and cognitive computing presented in ICCI’08. The theoretical framework and applications of CI and cognitive computing are described. A set of selected papers from ICCI’08 is introduced.

The growth of ubiquitous computing and Web services is expected to make everyday life of people more convenient, and it is also expected to make the society safer and more active. However, the problems of the Internet age such as the digital divide, network criminals and the insecurity of privacy prevent many people from seeing the benefits of the Internet. To solve the problems, we have been studying a methodology which aims at developing a cognitive agent which supports the social activities of a person based on a symbiotic relationship between the person and the agent. The symbiotic relationship means that the person and the agent are tightly coupled in a ubiquitous environment and keep their partnership to support each other, according to Licklider’s symbiosis. In this chapter, we propose a concept of a symbiotic agent and an agent model to design and implement functions defined in the symbiotic agent. To realize the concept, we have implemented an agent platform to run multi-agent systems consisting of primitive agents using Steel Bank Common Lisp running on Linux and MacOS.

New methodology of adaptive monitoring and evaluation of complicated dynamic data is introduced. The major objectives are monitoring and evaluation of both instantaneous and long-term attributes of complex dynamic behavior, such as of chaotic systems and real-world dynamical systems. In the sense of monitoring, the methodology introduces a novel approach to quantification and visualization of cognitively observed system behavior in a real time without further processing of these observations. In the sense of evaluation, the methodology opens new possibilities for consequent qualitative and quantitative processing of cognitively monitored system behavior. Techniques and enhancements are introduced to improve the stability of low-dimensional neural architectures and to improve their capability in approximating nonlinear dynamical systems that behave complex in high-dimensional state space. Low-dimensional dynamic quadratic neural units enhanced as forced dynamic oscillators are introduced to improve the approximation quality of higher dimensional systems. However, the introduced methodology can be universally used for adaptive evaluation of dynamic behavior variability also with other neural architectures and adaptive models, and it can be used for theoretical chaotic systems as well as for real-word dynamical systems. Simulation results on applications to deterministic, however, highly chaotic time series are shown to explain the new methodology and to demonstrate its capability in sensitive and instantaneous detections of changing behavior, and these detections serve for monitoring and evaluating the level of determinism (predictability) in complex signals. Results of this new methodology are shown also for real-world data, and its limitations are discussed.

A broad variety of internal and external entities solicit managers to update software programs in a business. Organizational measures frequently are not in line with the needs, and software maintenance still makes a hot problem in companies and institutions.

There are numerous theoretical studies related to software evolution processes but the origins of software evolution do not seem completely clear. There are contradictory opinions on the argument, some deem a software update as an accident or an occasional disturb, others consider software maintenance as a systematic phase of software implementation. The intention of this paper is to scrutinize the root-causes of software evolutions so that one can be fully conscious of the nature of software changes and can handle practical countermeasures in more appropriate manners.

In a preliminary stage we develop two broad, referential notions: the human intelligence and the information system, and we attempt to see how both of them contribute to the successful adaptation of work organizations. Later we see how software programs are significantly involved in the adaptation process just seen; how they contribute to the continuous evolution of companies and organizations.

Finally we see viable suggestions for the management of software development and maintenance which are deduced from the preliminary theoretical frame.

This study presents a general agent architecture to simulate human-like intelligence. The design philosophy behind the architecture is driven by a combination of Maslow’s theories of needs and Dennett’s intentional notion. Furthermore, to explain motives of intelligent agents, we adopt Alderfer’s theory of needs which revises the ideas of Maslow. Intelligent agents are considered as entities driven by unsatisfied needs, and in order to satisfy those needs they act intentionally. Based on these ideas, we present a three-tiered cognitive agent architecture to mimic many aspects of human intelligence. The reactive layer enables an agent to continuously observe internal and external conditions and act accordingly. The deliberative layer provides the means for learning, planning, confict resolution with other agents, and dispatching tasks to the components in the reactive layer. The causal layer oversees the high-level decision-making and emotion generation processes.

Global Software Development (GSD) projects became a common way to develop software during the last decade. However, despite the economic benefits that globalization may introduce, GSD faces a series of factors that affect communication and challenge its success. In order to improve communication in such environments, we focus on techniques from the field of cognitive psychology to define a new approach to groupware tools selection. In this paper we present a series of strategies to find the best choice for a given group of people, taking into account the different combinations of cognitive profiles that can arise in a GSD project, as well as the application of one of these strategies in a case study.

This paper presents the design and implementation of a software tool for modeling dynamic knowledge to be used in knowledge based systems or the Semantic Web. The tool presented has been developed based on the Inferential Modeling Technique, which is a knowledge modeling technique for representing both static and dynamic knowledge elements of a problem domain. A major deficiency of existing tools is the lack of support for modeling dynamic knowledge. To address this inadequacy, the focus of this work is on dynamic knowledge modeling. A Protégé plug-in, called Dyna, has been developed which supports modeling task behavior using the Task Behaviour Language (TBL). Dyna also can create test cases for testing task behavior. Test cases are runnable and can enable verification that the model is working as expected. The dynamic knowledge models are stored in XML and OWL, and can be shared and re-used. The tool is applied for constructing a knowledge model in the petroleum contamination remediation selection domain.

The quest to define human intelligence has led researchers down a large range of paths. One such path has been the search for a single, basic psychometric measure that can be used to account for a large portion of the variance in human mental ability. Inspection Time (IT) has emerged at the forefront of these efforts and can be shown to account for approximately 25% of the variance in psychometric tests of intelligence (e.g., IQ). In this study, we attempt to gain an insight into the nature of IT as a psychometric measure by first contrasting individuals that are adept at performing the IT task (those with low ITs) with individuals that are not (those with high ITs) using oculomotor and task-performance measures recorded during two visual tasks. The results of the first experiment show that the current prevailing theory regarding IT, the integration theory, is incapable of accounting for the results found during the visual tasks. This leads us to introduce a novel theory of IT, the watered-tree theory, which places IT as a measure of information propagation. We then perform a second experiment to test the opposing predictions of the integration theory and the watered-tree theory and find that the results are in line with the predictions of the watered-tree theory. A discussion is presented on the implications of the proposed theory and the need for its future validation.

Socialware is a multiagent system that supports social activities in the symbiotic society. In this chapter, we focused on supporting people with such cognitive disabilities as dementia, aphasia, higher cerebral dysfunction, and senior citizens suffering from cognitive decline. We propose general socialware architecture for people with disabilities as multiagent systems that are composed of personal, communication, and community agents. Three experimental systems are introduced: a networked reminiscence system, a walk navigation system using photographs, and a conversation support system for people with aphasia.

This chapter describes a new way of pattern interpretation aimed at the automatic semantic categorization and image content understanding. Such an understanding is based on the linguistic theories of pattern classification and is aimed at facilitating the content analysis of some classes of medical and economical patterns. The approach presented in this chapter shows great opportunities for the automatic disease interpretation in some analyzed structures, and for supporting information management using the grammar approach. The interpretation is based on cognitive resonance processes which imitate the psychological processes of understanding registered patterns which take place in the brain of a human beings. Cognitive and thinking processes taking place in the human brain have become the basis for defining classes of cognitive categorization systems designed for the in-depth, meaning-based interpretation and analysis of data. This type of an analysis is only possible thanks to applying interpretation and reasoning processes usually taking place in the human brain in a system. In addition, this type of an analysis is made possible by the use of linguistic algorithms for describing, analyzing and interpreting data in computer systems. Algorithms of this type support a meaning-based analysis of data which leads to understanding the semantic content of the analyzed data and to attempts at making forecasts with regard to the analyzed information.

Classification is a fundamental problem in data mining, which has extensive applications in information technology. Data understanding is highly relevant to how to sense and perceive them. However, the existing approaches for classification have been developed mainly based on dividing dataset space, less or no emphasis paid on simulating human or animal visual cognition. This chapter attempts to understand visual classification by using both psychophysical and machine learning techniques. A new Hyper Surface Classification method (HSC) has been studied since 2002. In HSC, a model of hyper surface is obtained by adaptively dividing the sample space and then the hyper surface is directly used to classify large database based on Jordan Curve Theorem in Topology. In this chapter we point out that HSC is a data understanding method which accords with visual cognitive mechanism. Simulation results show that the proposed method is effective on large test data with complex distribution and high density. In particular, we show that HSC can deal with high dimensional data and build corresponding visual hyper surface using dimension transposition or ensemble method which accords with visual dimension transposition and multi-dimension cognitive mechanism respectively.

In this chapter we address a basic question in the functional model of the mind: with which mechanism a cognitive agent can understand new concepts? and propose an answer: the cognitive prism mechanism. This mechanism is rooted in the information process of a neuron. Research results in cognitive psychology and linguistics support that such mechanism is used in concept-understanding in our everyday-life. We show that this mechanism is used to integrate spatial environments existing at different temporal points and form a spatial concept. Lakoff’s theory in concept-understanding can be reformulated in terms of the cognitive prism mechanism. The classic mathematical logic, as well as fuzzy logic, can be understood as the (prism) mapping from language to true or false values. In Chinese medicine, human-body structure is referenced to spatial concepts through certain cognitive prism mechanism. We argue that metaphor is not only the mechanism to relate concepts in non-physical domain to physical ones, but also the mechanism to relate concepts within the physical domain. We briefly criticize the current theory of joke and propose a novel perspective to the understanding of jokes in term of ‘potential tension’ of cognitive prism. We conclude that equipped with the cognitive prism mechanism and concepts of spatial environment cognitive agents shall understand quite a lot of spatial/non-spatial concepts.

The last few decades of physics, chemistry, biology, computer science, engineering, and social sciences have been marked by major developments of views on cognitive systems, dynamical systems, complex systems, complexity, self-organization, and emergent phenomena that originate from the interactions among the constituent components (agents) and with the environment, without any central authority. How can measures of complexity capture the intuitive sense of pattern, order, structure, regularity, evolution of features, memory, and correlation? This chapter describes several key ideas, including dynamical systems, complex systems, complexity, and quantification of complexity. As there is no single definition of a complex system, its complexity and complexity measures too have many definitions. As a major contribution, this chapter provides a new comprehensive taxonomy of such measures. This chapter also addresses some practical aspects of acquiring the observables properly.