Artificial Intelligence
Artificial Intelligence
An abstract, argumentation-theoretic approach to default reasoning
Artificial Intelligence
The logical foundations of goal-regression planning in autonomous agents
Artificial Intelligence
Semantics for a theory of defeasible reasoning
Annals of Mathematics and Artificial Intelligence
ABA: argumentation based agents
ArgMAS'11 Proceedings of the 8th international conference on Argumentation in Multi-Agent Systems
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OSCAR is a fully implemented architecture for a cognitive agent, based largely on the author's work in philosophy concerning epistemology and practical cognition. The seminal idea is that a generally intelligent agent must be able to function in an environment in which it is ignorant of most matters of fact. The architecture incorporates a general-purpose defeasible reasoner, built on top of an efficient natural deduction reasoner for first-order logic. It is based upon a detailed theory about how the various aspects of epistemic and practical cognition should interact, and many of the details are driven by theoretical results concerning defeasible reasoning. The architecture is easily extensible by changing the set of inference schemes supplied to the reasoner. Existing inference schemes handle many kinds of epistemic cognition, including reasoning from perceptual input, causal reasoning and the frame problem, and reasoning defeasibly about probabilities. Work is underway to implement a system of defeasible decision-theoretic planning.