In this article we present an advanced version of Dual-PECCS, a cognitively-inspired knowledge re... more 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 cat-egorization (prototypical and exemplars-based categorization) with standard monotonic cate-gorization 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.
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015), Jul 20, 2015
In this article we present DUAL-PECCS, an in- tegrated Knowledge Representation system aimed at e... more In this article we present DUAL-PECCS, an in- tegrated 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 reason- ing: prototypical reasoning and exemplars-based reasoning. Furthermore, it is grounded on the the- oretical tenets coming from the dual process the- ory of the mind, and on the hypothesis of heteroge- neous proxytypes, developed in the area of the bio- logically 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 lin- guistic description had to be identified by resort- ing to a mix of categorization strategies. Compared to human-level categorization, the obtained results suggest that our proposal can be helpful in extend- ing the representational and reasoning conceptual capabilities of standard cognitive artificial systems.
In this article we present an advanced version of Dual-PECCS, a cognitively-inspired knowledge re... more 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 cat-egorization (prototypical and exemplars-based categorization) with standard monotonic cate-gorization 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.
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015), Jul 20, 2015
In this article we present DUAL-PECCS, an in- tegrated Knowledge Representation system aimed at e... more In this article we present DUAL-PECCS, an in- tegrated 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 reason- ing: prototypical reasoning and exemplars-based reasoning. Furthermore, it is grounded on the the- oretical tenets coming from the dual process the- ory of the mind, and on the hypothesis of heteroge- neous proxytypes, developed in the area of the bio- logically 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 lin- guistic description had to be identified by resort- ing to a mix of categorization strategies. Compared to human-level categorization, the obtained results suggest that our proposal can be helpful in extend- ing the representational and reasoning conceptual capabilities of standard cognitive artificial systems.
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Papers by Valentina Rho