14,865 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
MAA*: A Heuristic Search Algorithm for Solving Decentralized POMDPs
We present multi-agent A* (MAA*), the first complete and optimal heuristic
search algorithm for solving decentralized partially-observable Markov decision
problems (DEC-POMDPs) with finite horizon. The algorithm is suitable for
computing optimal plans for a cooperative group of agents that operate in a
stochastic environment such as multirobot coordination, network traffic
control, `or distributed resource allocation. Solving such problems efiectively
is a major challenge in the area of planning under uncertainty. Our solution is
based on a synthesis of classical heuristic search and decentralized control
theory. Experimental results show that MAA* has significant advantages. We
introduce an anytime variant of MAA* and conclude with a discussion of
promising extensions such as an approach to solving infinite horizon problems.Comment: Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005
Batch Informed Trees (BIT*): Sampling-based Optimal Planning via the Heuristically Guided Search of Implicit Random Geometric Graphs
In this paper, we present Batch Informed Trees (BIT*), a planning algorithm
based on unifying graph- and sampling-based planning techniques. By recognizing
that a set of samples describes an implicit random geometric graph (RGG), we
are able to combine the efficient ordered nature of graph-based techniques,
such as A*, with the anytime scalability of sampling-based algorithms, such as
Rapidly-exploring Random Trees (RRT).
BIT* uses a heuristic to efficiently search a series of increasingly dense
implicit RGGs while reusing previous information. It can be viewed as an
extension of incremental graph-search techniques, such as Lifelong Planning A*
(LPA*), to continuous problem domains as well as a generalization of existing
sampling-based optimal planners. It is shown that it is probabilistically
complete and asymptotically optimal.
We demonstrate the utility of BIT* on simulated random worlds in
and and manipulation problems on CMU's HERB, a
14-DOF two-armed robot. On these problems, BIT* finds better solutions faster
than RRT, RRT*, Informed RRT*, and Fast Marching Trees (FMT*) with faster
anytime convergence towards the optimum, especially in high dimensions.Comment: 8 Pages. 6 Figures. Video available at
http://www.youtube.com/watch?v=TQIoCC48gp
Multiple-Goal Heuristic Search
This paper presents a new framework for anytime heuristic search where the
task is to achieve as many goals as possible within the allocated resources. We
show the inadequacy of traditional distance-estimation heuristics for tasks of
this type and present alternative heuristics that are more appropriate for
multiple-goal search. In particular, we introduce the marginal-utility
heuristic, which estimates the cost and the benefit of exploring a subtree
below a search node. We developed two methods for online learning of the
marginal-utility heuristic. One is based on local similarity of the partial
marginal utility of sibling nodes, and the other generalizes marginal-utility
over the state feature space. We apply our adaptive and non-adaptive
multiple-goal search algorithms to several problems, including focused
crawling, and show their superiority over existing methods
Heuristic search under a deadline
In many heuristic search problems of practical interest, insufficient time is available to find a provably optimal solution. The currently accepted methods of finding a best possible sub-optimal solution within a time deadline are the anytime methods which do not directly consider the time remaining in the search. My thesis is that a deadline-cognizant approach, one which attempts to expend all available search effort towards a single final solution, has the potential for outperforming these methods.
To support this thesis I introduce two new deadline-cognizant algorithms: Deadline Aware Search and Deadline Decision Theoretic Search. These approaches use on-line measurements of search behavior to guide the search towards the best possible solution reachable before the deadline. An empirical analysis illustrates that DAS is capable of outperforming the current incumbent methods across a wide variety of domains, the first deadline-cognizant heuristic search algorithm to do so
- …