Logical foundations of artificial intelligence
Logical foundations of artificial intelligence
Nonmonotonic inference based on expectations
Artificial Intelligence
PRICAI '98 Proceedings of the 5th Pacific Rim International Conference on Artificial Intelligence: Topics in Artificial Intelligence
Revisions of knowledge systems using epistemic entrenchment
TARK '88 Proceedings of the 2nd conference on Theoretical aspects of reasoning about knowledge
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In most accounts of common-sense reasoning, only the most preferred among models supplied by the evidence are retaiined (and the rest eliminated) in order to enhance the inferential prowess. One problem with this strategy is that the agent's working set of models shrinks quickly in the process. We argue that instead of rejecting all the nonbest models, the reasoner should reject only the worst models and then examine the consequences of adopting this principle in the context of abductive reasoning. Apart from providing the releveint representation results, we indicate why an iterated account of abduction is feasible in this framework.