Paper Info
Title
Dual PECCS: a cognitive system for conceptual representation and categorization.
Abstract
In this article we present an advanced version of Dual-PECCS, a cognitively-inspired knowledge representation and reasoning system aimed at extending the capabilities of artificial systems in conceptual categorization tasks. It combines different sorts of common-sense categorization (prototypical and exemplars-based categorization) with standard monotonic categorization procedures. These different types of inferential procedures are reconciled according to the tenets coming from the dual process theory of reasoning. On the other hand, from a representational perspective, the system relies on the hypothesis of conceptual structures represented as heterogeneous proxytypes. Dual-PECCS has been experimentally assessed in a task of conceptual categorization where a target concept illustrated by a simple common-sense linguistic description had to be identified by resorting to a mix of categorization strategies, and its output has been compared to human responses. The obtained results suggest that our approach can be beneficial to improve the representational and reasoning conceptual capabilities of standard cognitive artificial systems, and - in addition - that it may be plausibly applied to different general computational models of cognition. The current version of the system, in fact, extends our previous work, in that Dual- PECCS is now integrated and tested into two cognitive architectures, ACT-R and CLARION, implementing different assumptions on the underlying invariant structures governing human cognition. Such integration allowed us to extend our previous evaluation.
Year
DOI
Venue
2017
10.1080/0952813X.2016.1198934
JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE
Keywords
Field
DocType
Knowledge representation,categorization,conceptual spaces,cognitive architectures,heterogeneous proxytypes,ACT-R,prototypes,exemplars,common-sense reasoning,CLARION
Categorization,Linguistic description,Knowledge representation and reasoning,Computer science,Cognitive systems,Dual process theory,Commonsense reasoning,Software,Natural language processing,Artificial intelligence,CLARION,Machine learning
Journal
Volume
Issue
ISSN
29.0
2
0952-813X
Citations 
PageRank 
References 
19
1.23
22
Authors
3
Name
Order
Citations
PageRank
Antonio Lieto120029.65
Daniele P. Radicioni216523.17
Valentina Rho3354.42