Proceedings of the AAAI Conference on Artificial Intelligence, 5
The popular use of backtracking as a control strategy for theorem proving in PROLOG and in Truth-Maintenance-Systems (TMS) led to increased interest in various schemes for enhancing the efficiency of backtrack search. Researchers have referred to these enhancement schemes by the names "Intelligent Backtracking" (in PROLOG), "Dependency-directed-backtracking" (in TMS) and others. Those improvements center on the issue of "jumping-back" to the source of the problem in front of dead-end situations. This paper examines another issue (much less explored) which arises in dead-ends. Specifically, we concentrate on the idea of constraint recording, namely, analyzing and storing the reasons for the dead-ends, and using them to guide future decisions, so that the same conflicts will not arise again. We view constraint recording as a process of learning, and examine several possible learning schemes studying the tradeoffs between the amount of learning and the improvement in search efficiency.