Discovery and Revision via Incremental Hill Climbing
An important issue in machine learning concerns how initial discoveries learned in a domain must be revised over time to account for newly learned beliefs. When performing this task, existing truth maintenance systems, such as the TMS an ATMS, tend to keep too many alternate beliefs in memory. When searching for consistency among beliefs, and ATMS typically finds all solutions. ATMS finds only one, but beliefs not part of the current theory are still kept in memory in case they become current at a later time. Neither approach uses heuristics to decide which beliefs should be active, and thus in some domains they do too much work. In this paper I describe Revolver, a discovery system that conducts more reasoned belief revision through an incremental, hill-climbing approach. Amain assumption of the program is that all beliefs should not be stored over time; Revolver uses a heuristic evaluation function to decide how beliefs should be revised to account for new information. First, I will illustrate the program’s behavior with a detailed example from the domain of chemical discovery. An analysis of the system ‘s behaviour follows, with particular emphasis on issues pertaining to its belief revision process. Third, I compare Revolver to other belief revision approaches. Ideas are then presented for synthesizing these various methods, followed by a summary and closing remarks.
KeywordsCoherence Expense Doyle Harman Rebalance
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