In this article we present DUAL-PECCS, an integrated Knowledge Representation system aimed at extending artificial capabilities in tasks such as conceptual categorization. It relies on two different sorts of cognitively inspired common-sense reasoning: prototypical reasoning and exemplars-based reasoning. Furthermore, it is grounded on the theoretical tenets coming from the dual process theory of the mind, and on the hypothesis of heterogeneous proxytypes, developed in the area of the biologically inspired cognitive architectures (BICA). The system has been integrated into the ACT-R cognitive architecture, and experimentally assessed in a conceptual categorization task, where a target concept illustrated by a simple common-sense linguistic description had to be identified by resorting to a mix of categorization strategies. Compared to human-level categorization, the obtained results suggest that our proposal can be helpful in extending the representational and reasoning conceptual capabilities of standard cognitive artificial systems

A Common-Sense Conceptual Categorization System Integrating Heterogeneous Proxytypes and the Dual Process of Reasoning

LIETO, ANTONIO
First
;
RADICIONI, DANIELE PAOLO;RHO, VALENTINA
2015

Abstract

In this article we present DUAL-PECCS, an integrated Knowledge Representation system aimed at extending artificial capabilities in tasks such as conceptual categorization. It relies on two different sorts of cognitively inspired common-sense reasoning: prototypical reasoning and exemplars-based reasoning. Furthermore, it is grounded on the theoretical tenets coming from the dual process theory of the mind, and on the hypothesis of heterogeneous proxytypes, developed in the area of the biologically inspired cognitive architectures (BICA). The system has been integrated into the ACT-R cognitive architecture, and experimentally assessed in a conceptual categorization task, where a target concept illustrated by a simple common-sense linguistic description had to be identified by resorting to a mix of categorization strategies. Compared to human-level categorization, the obtained results suggest that our proposal can be helpful in extending the representational and reasoning conceptual capabilities of standard cognitive artificial systems
24th International Joint Conference on Artificial Intelligence (IJCAI 2015)
Buenos Aires
25-31 July 2015
Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), Buenos Aires, July 2015
AAAI Press
875
881
978-1-57735-738-4
http://ijcai.org/papers15/Papers/IJCAI15-128.pdf
knowledge representation, categorization, cognitive systems, proxytypes
Lieto, Antonio; Radicioni, Daniele P.; Rho, Valentina
File in questo prodotto:
File Dimensione Formato  
10872-49608-1-PB.pdf

Accesso riservato

Tipo di file: PDF EDITORIALE
Dimensione 377.72 kB
Formato Adobe PDF
377.72 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2318/1550454
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 22
  • ???jsp.display-item.citation.isi??? 17
social impact