Abstract
Belief revision systems are AI programs that deal with contradictions. They work with a knowledge base, performing reasoning from the propositions in the knowledge base, “filtering” those propositions so that only part of the knowledge base is perceived - the set of propositions that are under consideration. This set of propositions is called the set of believed propositions. Typically, belief revision systems explore alternatives, make choices, explore the consequences of their choices, and compare results obtained when using different choices. If during this process a contradiction is detected, then the belief revision system will revise the knowledge base, “erasing” some propositions so that it gets rid of the contradiction.
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