Unidirectional and bidirectional search algorithms

Nelson, Peter; Toptsis, Anestis A.

doi:10.1109/52.120605

Cited by 16 publications

(5 citation statements)

References 5 publications

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“…Good surveys of the field have been provided by Grama and Kumar [15] and Nelson and Toptsis [16] and, in the context of parallel logic programming, by Gupta et al [17]. Particular paradigms in parallel combinatorial search have also been explored extensively -see [18], [19] for discussions of techniques and heuristics in branch-andbound, [4] for parallel iterative deepening A * , [20] for game-tree search and [21] for parallel search of AND-OR trees.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Framework for Large-Scale State Space Search

Sun

Zheng

Jetley

et al. 2011

2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and PHD Forum

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Abstract-State space search problems abound in the artificial intelligence, planning and optimization literature. Solving such problems is generally NP-hard. Therefore, a brute-force approach to state space search must be employed. It is instructive to solve them on large parallel machines with significant computational power. However, writing efficient and scalable parallel programs has traditionally been a challenging undertaking. In this paper, we analyze several performance characteristics common to all parallel state space search applications. In particular, we focus on the issues of grain size, the prioritized execution of tasks and the balancing of load among processors in the system. We demonstrate the techniques that are used to scale such applications to large scale. We have incorporated these techniques into a general search engine framework that is designed to solve a broad class of state space search problems. We demonstrate the efficiency and scalability of our design using three example applications, and present scaling results up to 16,384 processors.

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Section: Related Workmentioning

confidence: 99%

An Adaptive Framework for Large-Scale State Space Search

Sun

Zheng

Jetley

et al. 2011

2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and PHD Forum

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“…A unidirectional breadth-first search in a graph with branching factor b and length l of the solution path has a complexity of O(b l ). The bi-directional search has a complexity of O(b l/2 ) if both search fronts meet approximately in the middle of the path [Nelson92]. This corresponds to an exponential run-time reduction.…”

Section: Goal Switchingmentioning

confidence: 99%

Multi-directional search with goal switching for robot path planning

Henrich

Wurll

Wörn

1998

Tasks and Methods in Applied Artificial Intelligence

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We present a parallel path planning method that is able to automatically handle multiple goal configurations as input. There are two basic approaches, goal switching and bidirectional search, which are combined in the end. Goal switching dynamically selects a favourite goal depending on some distance function. The bi-directional search supports the backward search direction from the goal to the start configuration, which is probably faster. The multi-directional search with goal switching combines the advantages of goal switching and bi-directional search. Altogether, the planning system is enabled to select one of the preferable goal configuration by itself. All concepts are experimentally validated for a set of benchmark problems consisting of an industrial robot arm with six degrees of freedom in a 3D environment.

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“…In that paper, domain specific knowledge is used to determine the sequence of nodes and the bounds on the costs. A recent review by Nelson and Toptsis Nelson and Toptsis (1992) describes different Al search algorithms that can be implemented both in serial and parallel machines.…”

Section: Solution Of the Optimal Resource Allocation Problemmentioning

confidence: 99%