In this paper we identify and characterize an analysis of two problematic aspects affecting the representational level of cognitive architectures (CAs): the size and the typology of the encoded knowledge. We argue that such aspects may constitute not only a technological problem that, in our opinion, should be addressed in order to build artificial agents able to exhibit intelligent behaviours in general scenarios, but also an epistemological one, since they limit the plausibility of the comparison of CAs's knowledge representation and processing mechanisms with those executed by humans in their everyday activities. In the final part of the paper further directions of research will be explored, trying to address current limitations and future challenges. in their ability of heuristically identify, retrieve, and process, from the general knowledge stored in their artificial Long Term Memory (LTM), that one which is synthetically and contextually relevant. This requirement, however, is often neglected. Currently, artificial cognitive systems and architectures are not able, de facto, to deal with complex knowledge structures that can be even slightly comparable to the knowledge heuristically managed by humans. In this paper we will argue that this is not only a technological problem but also, in the light of the distinction between functionalist and structuralist models of cognition, an epistemological one. The rest of the paper is organised as follows: Section 2 introduces the two main problematic aspects concerning the knowledge level in cognitive architectures, namely the size and the homogeneous typology of the encoded knowledge. Section 3 provides a focused review of the Knowledge Level of four of the most well known and widely used cognitive architectures (namely SOAR, ACT-R, CLARION and Vector-LIDA) by pointing out the respective differences and, in the light of our axis of analysis, their problematic issues 1 . In doing so we will illustrate the main attempts that have been proposed to address such problems and we will highlight the current limitations of such proposals. In the final sections, we present an overview of three different alternative approaches that can provide a possibile solution for dealing with, jointly, both the size and the knowledge homogeneity problems: namely the Semantic Pointer Perspective (section 4), the idea of Conceptual Space as intermediate level of representation connecting connectionist and symbolic approaches (section 5) and the novel versions of the Hybrid Neuro Symbolic Approaches currently developed in the field of CAs (section 6). Interestingly all such proposals converge in suggesting that the neural level of representation can be considered irrelevant for attacking the above mentioned problems, and suggest to address these issue by operating at more transparent and abstract levels of representation. Section 7, finally, considers the dual process approach as a possible reference 1 In the present paper we will leave aside many other aspects (e.g. those related to the knowledge acquisition problems) which are related to, and also affect, the problems into focus.
In the cognitive science domain, the work most related to ours is in the direction of capturing knowledge in cognitive architectures REF .
The knowledge level in cognitive architectures: Current limitations and possible developments
Cognitive Systems Research
Cognitive Systems Research
Computer Science