A mathematical treatment of defeasible reasoning and its implementation
Artificial Intelligence
AGENTS '97 Proceedings of the first international conference on Autonomous agents
ACM Computing Surveys (CSUR)
Introduction to Multiagent Systems
Introduction to Multiagent Systems
Reasoning agents in dynamic domains
Logic-based artificial intelligence
Defeasible logic programming: an argumentative approach
Theory and Practice of Logic Programming
A logic programming framework for possibilistic argumentation with vague knowledge
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
On the comparison of theories: preferring the most specific explanation
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
A logic programming framework for possibilistic argumentation with vague knowledge
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Argument-based critics and recommenders: a qualitative perspective on user support systems
Data & Knowledge Engineering - Special issue: WIDM 2004
A review of current defeasible reasoning implementations
The Knowledge Engineering Review
Argument Theory Change: Revision Upon Warrant
Proceedings of the 2008 conference on Computational Models of Argument: Proceedings of COMMA 2008
Expert Systems with Applications: An International Journal
Applied Computational Intelligence and Soft Computing
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One of the most difficult problems in multiagent systems involves representing knowledge and beliefs of agents in dynamic environments. New perceptions modify an agent’s current knowledge about the world, and consequently its beliefs. Such revision and updating process should be performed efficiently by the agent, particularly in the context of real time constraints. This paper introduces an argument-based logic programming language called Observation-based Defeasible Logic Programming (ODeLP). An ODeLP program is used to represent an agent’s knowledge in the context of a multiagent system. The beliefs of the agent are modeled with warranted goals computed on the basis of the agent’s program. New perceptions from the environment result in changes in the agent’s knowledge handled by a simple but effective updating strategy. The process of computing beliefs in a changing environment is made computationally attractive by integrating a “dialectical database” with the agent’s program, providing precompiled information about inferences. We present algorithms for creation and use of dialectical databases.