A Moving Target Search Algorithm and Its Performance Improvement

Abstract

We consider the case of heuristic search where the location of the goal may change during the course of the search. For example, the goal may be a target that is actively avoiding the problem solver. We present a moving target search algorithm (MTS) to solve this problem. MTS is the first search algorithm concerned with problem solving in a dynamically changing environment. We prove that the algorithm is complete, i.e., if the average speed of the target is slower than that of the problem solver, then the problem solver is guaranteed to eventually reach the target. However, since we constructed the algorithm with the minimum operations necessary for guaranteeing its completeness, the algorithm as proposed is neither efficient nor intelligent. We then introduce innovative notions created in the area of resource-bounded planning into the formal search algorithm, MTS, to improve its efficiency. Notions that are introduced are (i) commitment to goals, and (ii) deliberation for selecting Plans. We will show how these notions effectively overcome the bottleneck of MTS performance. The improved algorithm behaves like a predator. In clear situations, the problem solver is always sensitive to the target's moves and reactively moves toward the target's current position, while in uncertain situations, the problem solver ignores the target's moves, commits to its current goal, and deliberates to find a promising direction to reach the goal. Evaluation results demonstrate that the improved MTS is 10 to 20 times more efficient than the original MTS in uncertain situations.