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Artificial Intelligence Review
Erschienen in: Artificial Intelligence Review 2/2019

09.12.2017

Evaluating representational systems in artificial intelligence

verfasst von: John Licato, Zhitian Zhang

Erschienen in: Artificial Intelligence Review | Ausgabe 2/2019

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Abstract

All artificial reasoners work within representational systems. These systems, which may have varying levels of formality or detail, determine the space of possible representations over which the artificial reasoner can operate, by defining the syntactic and semantic properties of the symbols, structures, and inferences that they manipulate. But we are now seeing an increasing need for the ability to reason over representational systems, rather than just working within them. A prerequisite of performing such reasoning is the ability to evaluate and compare representational objects (and to know the difference between them). We survey the criteria that are used for such evaluations in AI, machine learning, and other AI-related fields. To aid our survey, we introduce a formalism of representations, representational systems, and representational spaces that lends itself nicely to an analysis of the criteria typically used for evaluating them.
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Fußnoten
1
By “AI-related research” we mean the areas of research that have criteria for comparing or evaluating representational objects which might be of use to AI researchers.
 
2
It is sometimes claimed (e.g. Griffiths et al. 2012) that frameworks should be thought of as tools for describing models, and cannot be falsified. If the analogy from frameworks and models to RSes and representations holds, then, we might infer that RSes cannot be falsified. That is, however, not a claim we support in this paper; we instead argue that there exist criteria, in regular use, to evaluate RSes. Whether a negative evaluation of an RS counts as a falsification is to be determined.
 
3
E.g., a cat can purr, or meow; and a legislator can legislate. A legislator can also conceivably purr or meow, but these are not actions we typically associate with them, and would thus likely not be members of the legislator RS.
 
4
Those familiar with object-oriented programming may notice this is a weakened version of the inheritance relationship; e.g., no multiple inheritances or polymorphisms are defined. This is because the work in this paper does not require specifying relationships any more complex than what we present here.
 
5
But it is interesting to note that this RS seems to be falling out of favor in the machine learning community. Popular implementations of neural networks, such as Google’s TensorFlow, view networks not as collections of neurons, but rather as tensors in a “computational graph” (Abadi et al. 2015). Our OO-inspired formalism would describe this as a shift in RSes, to one that views the propagations of activations and gradients to be operations defined over tensors. Such tensor operations can be easier to optimize mathematically and computationally, a clear benefit of the tensor-based RS.
 
6
We leave the proof of this discovery to the reader.
 
7
In the latter cases, it is often not clearly specified what the neural networks are supposed to be representations of.
 
8
E.g., see Gentner and Forbus (2011) for a review of models of analogical reasoning.
 
9
Though cognitive architectures are supposed to be more general than cognitive models of particular phenomena, in practice many cognitive architectures tend to focus on some subset of cognitive phenomena, rendering the authors’ claim of model generality difficult to verify. See Kotseruba et al. (2016) for a comprehensive review of cognitive architectures and their foci.
 
10
We can refer to these RSes nested within other RSes as level RSes.
 
11
It is outside of the scope of this paper to take a definitive position on the level of ontological commitment in our OO-inspired formalism, other than saying that it is typically the case that the inclusion of members or methods in an RS constitutes an ontological commitment to the realism of those elements. We have drawn some ideas from Floridi’s theory of levels of abstraction, which itself comes with a theory of ontological commitment built in. Interested readers can refer to Floridi (2011b) for more.
 
12
‘Contact’ here means something akin to theoretical or field utility, a criterion we will define in Sect. 6.
 
13
Podsakoff et al. (2016) give an example in which candidate attributes for a definition of “submarine” are compared to objects that are or are not considered submarines: diving bells, viking longboats, fishing boats, etc.
 
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Metadaten
Titel
Evaluating representational systems in artificial intelligence
verfasst von
John Licato
Zhitian Zhang
Publikationsdatum
09.12.2017
Verlag
Springer Netherlands
Erschienen in
Artificial Intelligence Review / Ausgabe 2/2019
Print ISSN: 0269-2821
Elektronische ISSN: 1573-7462
DOI
https://doi.org/10.1007/s10462-017-9598-7

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