Authors:
Yoshiaki Goto
;
Takeshi Hagiwara
and
Hajime Sawamura
Affiliation:
Niigata University, Japan
Keyword(s):
Argumentation, Semantics, Neural network.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Higher Level Artificial Neural Network Based Intelligent Systems
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Multi-Agent Intelligent Systems and Applications
;
Neural Network Software and Applications
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
Argumentation is a leading principle foundationally and functionally for agent-oriented computing where reasoning accompanied by communication plays as essential role in agent interaction. In the work of (Makiguchi and Sawamura, 2007a) (Makiguchi and Sawamura, 2007b), they constructed a simple but versatile neural network for the grounded semantics (the least fixed point semantics) in the Dung’s abstract argumentation framework (Dung, 1995). This paper further develop its theory so that it can decide which argumentation semantics (admissible, stable, complete semantics) a given set of arguments falls into. In doing so, we construct a more simple but versatile neural network that can compute all extensions of the argumentation semantics. The result leads to a neural-symbolic system for argumentation.