Recent real-time heuristic search algorithms have demonstrated outstanding performance in video-game pathfinding. However, their applications have been thus far limited to that domain. We proceed with the aim of facilitating wider applications of real-time search by fostering a greater understanding of the performance of recent algorithms. We first introduce eight algorithm-independent complexity measures for search spaces and correlate their values with algorithm performance. The complexity measures are statistically shown to be significant predictors of algorithm performance across a set of commercial video-game maps. We then extend this analysis to a wider variety of search spaces in the first application of database-driven real-time search to domains outside of video-game pathfinding. In doing so, we gain insight into algorithm performance and possible enhancement as well as into search space complexity.
Previous work has been conducted to predict the performance of simpler real-time heuristic search algorithms. Citing important applications in planning (e.g., as part of the Heuristic Search Planner @cite_14 and the Fast-Forward Planner @cite_15 ), L o pez sought to model the efficiency of heuristic hill-climbing by modelling the algorithm as a Markov process @cite_33 . Using several sizes of the sliding tile puzzle, the model could reasonably predict the likelihood that a hill-climbing agent reaches a target state in a number of moves equal to the initial heuristic value. L o pez stated that this model is useful not only as a predictor of simple real-time search performance, but as a gauge of heuristic accuracy.
@cite_15 @cite_14 @cite_33
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The purpose of this paper is twofold: on one hand, modelling the hill-climbing heuristic search algorithm as a stochastic process serves for deriving interesting properties about its expected performance; on the other hand, the probability that a hill-climbing search algorithm ever fails when approaching the target node (i.e., it does not find a descendant with a heuristic value strictly lower than the current one) can be considered as a pesimistic measure of the accuracy of the heuristic function guiding it. Thus, in this work, it is suggested to model heuristic hill-climbing search algorithms with Markov chains in order to fulfill these goals. Empirical results obtained in various sizes of the (n,m)-Puzzle domain prove that this model leads to very accurate predictions. Real-time heuristic search algorithms are used for creating agents that rely on local information and move in a bounded amount of time making them an excellent candidate for video games as planning time can be controlled. Path finding on video game maps has become the de facto standard for evaluating real-time heuristic search algorithms. Over the years researchers have worked to identify areas where these algorithms perform poorly in an attempt to mitigate their weaknesses. Recent work illustrates the benefits of tailoring algorithms for a given problem as performance is heavily dependent on the search space. In order to determine which algorithm to select for solving the search problems on a map the developer would have to run all the algorithms in consideration to obtain the correct choice. Our work extends the previous algorithm selection approach to use a deep learning classifier to select the algorithm to use on new maps without having to evaluate the algorithms on the map. To do so we select a portfolio of algorithms and train a classifier to predict which portfolio member to use on a unseen new map. Our empirical results show that selecting algorithms dynamically can outperform the single best algorithm from the portfolio on new maps, as well provide the lower bound for potential improvements to motivate further work on this approach. In the recent AIPS98 Planning Competition, the hsp planner, based on a forward state search and a domain-independent heuristic, showed that heuristic search planners can be competitive with state of the art Graphplan and Satisfiability planners. hsp solved more problems than the other planners but it often took more time or produced longer plans. The main bottleneck in hsp is the computation of the heuristic for every new state. This computation may take up to 85 of the processing time. In this paper, we present a solution to this problem that uses a simple change in the direction of the search. The new planner, that we call hspr, is based on the same ideas and heuristic as hsp , but searches backward from the goal rather than forward from the initial state. This allows hspr to compute the heuristic estimates only once. As a result, hspr can produce better plans, often in less time. For example, hspr solves each of the 30 logistics problems from Kautz and Selman in less than 3 seconds. This is two orders of magnitude faster than blackbox. At the same time, in almost all cases, the plans are substantially smaller. hspr is also more robust than hsp as it visits a larger number of states, makes deterministic decisions, and relies on a single adjustable parameter than can be fixed for most domains. hspr, however, is not better than hsp accross all domains and in particular, in the blocks world, hspr fails on some large instances that hsp can solve. We discuss also the relation between hspr and Graphplan, and argue that Graphplan can also be understood as a heuristic search planner with a precise heuristic function and search algorithm. In the AIPS98 Planning Contest, the HSP planner showed that heuristic search planners can be competitive with state-of-the-art Graphplan and SAT planners. Heuristic search planners like HSP transform planning problems into problems of heuristic search by automatically extracting heuristics from Strips encodings. They differ from specialized problem solvers such as those developed for the 24-Puzzle and Rubik’s Cube in that they use a general declarative language for stating problems and a general mechanism for extracting heuristics from these representations. In this paper, we study a family of heuristic search planners that are based on a simple and general heuristic that assumes that action preconditions are independent. The heuristic is then used in the context of best-first and hill-climbing search algorithms, and is tested over a large collection of domains. We then consider variations and extensions such as reversing the direction of the search for speeding node evaluation, and extracting information about propositional invariants for avoiding dead-ends. We analyze the resulting planners, evaluate their performance, and explain when they do best. We also compare the performance of these planners with two state-of-the-art planners, and show that the simplest planner based on a pure best-first search yields the most solid performance over a large set of problems. We also discuss the strengths and limitations of this approach, establish a correspondence between heuristic search planning and Graphplan, and briefly survey recent ideas that can reduce the current gap in performance between general heuristic search planners and specialized solvers.  2001 Elsevier Science B.V. All rights reserved.