Suppose some robot has the task of stacking blocks and after partly completing the task, the blocks are toppled. In this case a strategy of back-tracking may be sufficient, but not all cases are this straight-forward. There could be many state-dependent decisions made which could be invalidated by the changes to the world but not easily detected since the state in which the decision was made could be far in the past.
STRIPS and STRIPS-like systems approach this problem by building goal and state trees that attempt to track the dependencies of knowledge on other knowledge so that when a particular information is invalidated, its antecedents can be efficiently and completely traced and adjusted. However, this represents only the knowledge-maintenance aspect of the problem. The problem of determining what to do next may or may not still be clear. If not clear, a strategy for reanalyzing goals must be present.
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