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Abstract
This thesis describes a frame system similar to KL-ONE, called micro-KLONE, for representing and reasoning about knowledge which may be incomplete or inconsistent. An unusual semantics appropriate to familiar situations is proposed. It is based on probabilistic sampling to find a single plausible model of the domain in order to answer a query. Correct answering of queries is intractable, so the implementation make two approximations in order to run quickly: (1) The underlying connectionist architecture is only large enough to represent partial models of the domain, and (2) the system is only allowed to search for a limited time, so it may not even find the best partial intepretation. Lacking a provably correct implementation, the usefulness of the system becomes an empirical question. The "Ted Turner" problem is presented as an example in which the system draws an interesting common sense conclusion to a counterfactual query.
