Choices Without Backtracking

Abstract

Artificial Intelligence problem solvers are frequently confronted with the necessity to make choices among equally plausible alternatives. These may concern the choice of which goal to try to achieve next, which priority to assign to a task or which plausible inference to draw from incomplete data. Choices can be wrong: later problem solving may determine that an earlier choice was incorrect. In such cases most problem solvers invoke some form of backtracking to undo the faulty choice, retract the inferences that were made from that choice and make some other choice. This paper discusses a method of dealing with choice that does not involve any backtracking yet explores no more alternatives than the best backtracking schemes. It has an additional advantage over backtracking schemes that it is possible to easily compare two alternative incompatible choices - this cannot be done in backtracking schemes because of their necessity of requiring a globally consistent set of assertions.