Explanation-Based Learning with Plausible Inferencing
ILLINOIS UNIV AT URBANA DEPT OF COMPUTER SCIENCE
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This paper represents a synthesis of ideas from qualitative reasoning and explanation-based learning. Taken together they form a novel approach to planning that relies on plausible inferencing and applies to continuously varying rather than discrete world states. Interestingly, the frame problem skirted and the approach admits some forms of planning under uncertainty. Planning in a domain is very efficient, although learning about the domain can be time consuming. The approach possess a kind of natural reactivity. Keywords Explanation based learning, Planning, Learning to plan, Continuous domains, Knowledge level learning.