Incremental heuristic search

About: Incremental heuristic search is a research topic. Over the lifetime, 2376 publications have been published within this topic receiving 89502 citations.

The LAMA planner: guiding cost-based anytime planning with landmarks

Abstract: LAMA is a classical planning system based on heuristic forward search. Its core feature is the use of a pseudo-heuristic derived from landmarks, propositional formulas that must be true in every solution of a planning task. LAMA builds on the Fast Downward planning system, using finite-domain rather than binary state variables and multi-heuristic search. The latter is employed to combine the landmark heuristic with a variant of the well-known FF heuristic. Both heuristics are cost-sensitive, focusing on high-quality solutions in the case where actions have non-uniform cost. A weighted A* search is used with iteratively decreasing weights, so that the planner continues to search for plans of better quality until the search is terminated. LAMA showed best performance among all planners in the sequential satisficing track of the International Planning Competition 2008. In this paper we present the system in detail and investigate which features of LAMA are crucial for its performance. We present individual results for some of the domains used at the competition, demonstrating good and bad cases for the techniques implemented in LAMA. Overall, we find that using landmarks improves performance, whereas the incorporation of action costs into the heuristic estimators proves not to be beneficial. We show that in some domains a search that ignores cost solves far more problems, raising the question of how to deal with action costs more effectively in the future. The iterated weighted A* search greatly improves results, and shows synergy effects with the use of landmarks.

D*lite

Abstract: Incremental heuristic search methods use heuristics to focus their search and reuse information from previous searches to find solutions to series of similar search tasks much faster than is possible by solving each search task from scratch. In this paper, we apply Lifelong Planning A* to robot navigation in unknown terrain, including goal-directed navigation in unknown terrain and mapping of unknown terrain. The resulting D* Lite algorithm is easy to understand and analyze. It implements the same behavior as Stentz' Focussed Dynamic A* but is algorithmically different. We prove properties about D* Lite and demonstrate experimentally the advantages of combining incremental and heuristic search for the applications studied. We believe that these results provide a strong foundation for further research on fast replanning methods in artificial intelligence and robotics.

A Tabu Search Heuristic for the Static Multi-Vehicle Dial-a-Ride Problem

Abstract: This article describes a tabu search heuristic for the dial-a-ride problem with the following characteristics. Users specify transportation requests between origins and destinations. They may provide a time window on their desired departure or arrival time. Transportation is supplied by a fleet of vehicles based at a common depot. The aim is to design a set of least cost vehicle routes capable of accommodating all requests. Side constraints relate to vehicle capacity, route duration and the maximum ride time of any user. Extensive computational results are reported on randomly generated and real-life data sets.