5,165 research outputs found
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
Platform Dependent Verification: On Engineering Verification Tools for 21st Century
The paper overviews recent developments in platform-dependent explicit-state
LTL model checking.Comment: In Proceedings PDMC 2011, arXiv:1111.006
Evaluation of a Simple, Scalable, Parallel Best-First Search Strategy
Large-scale, parallel clusters composed of commodity processors are
increasingly available, enabling the use of vast processing capabilities and
distributed RAM to solve hard search problems. We investigate Hash-Distributed
A* (HDA*), a simple approach to parallel best-first search that asynchronously
distributes and schedules work among processors based on a hash function of the
search state. We use this approach to parallelize the A* algorithm in an
optimal sequential version of the Fast Downward planner, as well as a 24-puzzle
solver. The scaling behavior of HDA* is evaluated experimentally on a shared
memory, multicore machine with 8 cores, a cluster of commodity machines using
up to 64 cores, and large-scale high-performance clusters, using up to 2400
processors. We show that this approach scales well, allowing the effective
utilization of large amounts of distributed memory to optimally solve problems
which require terabytes of RAM. We also compare HDA* to Transposition-table
Driven Scheduling (TDS), a hash-based parallelization of IDA*, and show that,
in planning, HDA* significantly outperforms TDS. A simple hybrid which combines
HDA* and TDS to exploit strengths of both algorithms is proposed and evaluated.Comment: in press, to appear in Artificial Intelligenc
Planning under time pressure
Heuristic search is a technique used pervasively in artificial intelligence and automated planning. Often an agent is given a task that it would like to solve as quickly as possible. It must allocate its time between planning the actions to achieve the task and actually executing them. We call this problem planning under time pressure. Most popular heuristic search algorithms are ill-suited for this setting, as they either search a lot to find short plans or search a little and find long plans. The thesis of this dissertation is: when under time pressure, an automated agent should explicitly attempt to minimize the sum of planning and execution times, not just one or just the other.
This dissertation makes four contributions. First we present new algorithms that use modern multi-core CPUs to decrease planning time without increasing execution. Second, we introduce a new model for predicting the performance of iterative-deepening search. The model is as accurate as previous offline techniques when using less training data, but can also be used online to reduce the overhead of iterative-deepening search, resulting in faster planning. Third we show offline planning algorithms that directly attempt to minimize the sum of planning and execution times. And, fourth we consider algorithms that plan online in parallel with execution. Both offline and online algorithms account for a user-specified preference between search and execution, and can greatly outperform the standard utility-oblivious techniques. By addressing the problem of planning under time pressure, these contributions demonstrate that heuristic search is no longer restricted to optimizing solution cost, obviating the need to choose between slow search times and expensive solutions
04301 Abstracts Collection -- Cache-Oblivious and Cache-Aware Algorithms
The Dagstuhl Seminar 04301 ``Cache-Oblivious and Cache-Aware Algorithms\u27\u27 was held
in the International Conference and Research Center (IBFI), Schloss Dagstuhl, from 18.07.2004 to 23.07.2004.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
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