Best-first heuristic search for multicore machines

To harness modern multicore processors, it is imperative to develop parallel versions of fundamental algorithms. In this paper, we compare different approaches to parallel best-first search in a shared-memory setting. We present a new method, PBNF, that uses abstraction to partition the state space and to detect duplicate states without requiring frequent locking. PBNF allows speculative expansions when necessary to keep threads busy. We identify and fix potential livelock conditions in our approach, proving its correctness using temporal logic. Our approach is general, allowing it to extend easily to suboptimal and anytime heuristic search. In an empirical comparison on STRIPS planning, grid pathfinding, and sliding tile puzzle problems using 8-core machines, we show that A*, weighted A* and Anytime weighted A* implemented using PBNF yield faster search than improved versions of previous parallel search proposals

On-line planning and scheduling: an application to controlling modular printers

We present a case study of artificial intelligence techniques applied to the control of production printing equipment. Like many other real-world applications, this complex domain requires high-speed autonomous decision-making and robust continual operation. To our knowledge, this work represents the first successful industrial application of embedded domain-independent temporal planning. Our system handles execution failures and multi-objective preferences. At its heart is an on-line algorithm that combines techniques from state-space planning and partial-order scheduling. We suggest that this general architecture may prove useful in other applications as more intelligent systems operate in continual, on-line settings. Our system has been used to drive several commercial prototypes and has enabled a new product architecture for our industrial partner. When compared with state-of-the-art off-line planners, our system is hundreds of times faster and often finds better plans. Our experience demonstrates that domain-independent AI planning based on heuristic search can flexibly handle time, resources, replanning, and multiple objectives in a high-speed practical application without requiring hand-coded control knowledge

Experimental Real-time Heuristic Search Results in a Video Game

In real-time domains such as video games, a planning algorithm has a strictly bounded time before it must return the next action for the agent to execute. We introduce a realistic video game benchmark domain that is useful for evaluating real-time heuristic search algorithms. Unlike previous benchmarks such as grid pathfinding and the sliding tile puzzle, this new domain includes dynamics and induces a directed graph. Using both the previous and new domains, we investigate several enhancements to a leading real-time search algorithm, LSS-LRTA*. We show experimentally that 1) it is not difficult to outperform A * when optimizing goal achievement time, 2) it is better to plan after each action than to commit to multiple actions or to use a dynamically sized lookahead, 3) A*-based lookahead can cause undesirable actions to be selected, and 4) on-line de-biasing of the heuristic can lead to improved performance. We hope that this new domain and results will stimulate further research on applying real-time search to dynamic real-time domains

Easily searched encodings for number partitioning

Can stochastic search algorithms outperform existing deterministic heuristics for the NP-hard problem Number Partitioning if given a sufficient, but practically realizable amount of time? In a thorough empirical investigation using a straightforward implementation of one such algorithm, simulated annealing, Johnson et al. (Ref. 1) concluded tentatively that the answer is negative. In this paper, we show that the answer can be positive if attention is devoted to the issue of problem representation (encoding). We present results from empirical tests of several encodings of Number Partitioning with problem instances consisting of multiple-precision integers drawn from a uniform probability distribution. With these instances and with an appropriate choice of representation, stochastic and deterministic searches can—routinely and in a practical amount of time—find solutions several orders of magnitude better than those constructed by the best heuristic known (Ref. 2), which does not employ searching. We thank David S. Johnson of AT&T Bell Labs for generously and promptly sharing his test instances. For stimulating discussions, we thank members of the Harvard Animation/Optimization Group (especially Jon Christensen), the Computer Science Department at the University of New Mexico, the Santa Fe Institute, and the Berkeley CAD Group. The anonymous referees made numerous constructive suggestions. We thank Rebecca Hayes for comments concerning the figures. The second author is grateful for a Graduate Fellowship from the Fannie and John Hertz Foundation. We thank the Free Software Foundation for making the GNU Multiple Precision package available. The research described in this paper was conducted mostly while the third author was at Digital Equipment Corporation Cambridge Research Lab. This work was supported in part by the National Science Foundation, principally under Grants IRI-9157996 and IRI-9350192 to the fourth author, and by matching grants from Digital Equipment Corporation and Xerox Corporation.Engineering and Applied Science

Easily searched encodings for number partitioning

Can stochastic search algorithms outperform existing deterministic heuristics for the NP-hard problem Number Partitioning if given a sufficient, but practically realizable amount of time? In a thorough empirical investigation using a straightforward implementation of one such algorithm, simulated annealing, Johnson et al. (Ref. 1) concluded tentatively that the answer is negative. In this paper, we show that the answer can be positive if attention is devoted to the issue of problem representation (encoding). We present results from empirical tests of several encodings of Number Partitioning with problem instances consisting of multiple-precision integers drawn from a uniform probability distribution. With these instances and with an appropriate choice of representation, stochastic and deterministic searches can—routinely and in a practical amount of time—find solutions several orders of magnitude better than those constructed by the best heuristic known (Ref. 2), which does not employ searching. We thank David S. Johnson of AT&T Bell Labs for generously and promptly sharing his test instances. For stimulating discussions, we thank members of the Harvard Animation/Optimization Group (especially Jon Christensen), the Computer Science Department at the University of New Mexico, the Santa Fe Institute, and the Berkeley CAD Group. The anonymous referees made numerous constructive suggestions. We thank Rebecca Hayes for comments concerning the figures. The second author is grateful for a Graduate Fellowship from the Fannie and John Hertz Foundation. We thank the Free Software Foundation for making the GNU Multiple Precision package available. The research described in this paper was conducted mostly while the third author was at Digital Equipment Corporation Cambridge Research Lab. This work was supported in part by the National Science Foundation, principally under Grants IRI-9157996 and IRI-9350192 to the fourth author, and by matching grants from Digital Equipment Corporation and Xerox Corporation.Engineering and Applied Science

A seed-growth heuristic for graph bisection

We present a new heuristic algorithm for graph bisection, based on an implicit notion of clustering. We describe how the heuristic can be combined with stochastic search procedures and a postprocess application of the Kernighan-Lin algorithm. In a series of time-equated comparisons with large-sample runs of pure Kernighan-Lin, the new algorithm demonstrates significant superiority in terms of the best bisections found.Engineering and Applied Science