Neural-Symbolic Cognitive Agents : Architecture, Theory and Application (Extended Abstract)
conference paper
In real-world applications, the effective integration of learning and reasoning in a cognitive agent model is a diffcult task. However, such integration may lead to a better understanding, use and construction of more realistic multiagent models. Existing models are either oversimplified or require too much processing time, which is unsuitable for online learning and reasoning. In particular, higher-order concepts and cognitive abilities have many unknown temporal relations with the data, making it impossible to represent such relationships by hand. In this paper, we develop and apply a Neural-Symbolic Cognitive Agent (NSCA) model for online learning and reasoning that seeks to effectively represent, learn and reason in complex real-world applications.
Topics
TNO Identifier
505267
ISBN
9781634391313
Publisher
International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Source title
Proceedings of the 13th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2014), May 5-9, 2014, Paris, France
Editor(s)
Lomusco, A.
Scerri, P.
Bazzan, A.
Huhns, M.
Scerri, P.
Bazzan, A.
Huhns, M.
Pages
1621-1622
Files
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