scholarly journals An Adaptive Framework for Large-Scale State Space Search

2011 ◽  
Author(s):  
Yanhua Sun ◽  
Gengbin Zheng ◽  
Pritish Jetley ◽  
Laxmikant V. Kale
Keyword(s):  
State Space ◽  
Large Scale ◽  
State Space Search ◽  
Adaptive Framework

PARSSSE: AN ADAPTIVE PARALLEL STATE SPACE SEARCH ENGINE

2011 ◽  
Vol 21 (03) ◽  
pp. 319-338 ◽  
Author(s):  
YANHUA SUN ◽  
GENGBIN ZHENG ◽  
PRITISH JETLEY ◽  
LAXMIKANT V. KALE
Keyword(s):  
State Space ◽  
Search Engine ◽  
Large Scale ◽  
Parallel Machines ◽  
Broad Class ◽  
Search Problems ◽  
State Space Search ◽  
Parallel Performance ◽  
General Search ◽  
Brute Force Approach

State space search problems abound in the artificial intelligence, planning and optimization literature. Solving such problems is generally NP-hard, so that a brute-force approach to state space search must be employed. Given the exponential amount of work that state space search problems entail, it is desirable to solve them on large parallel machines with significant computational power. In this paper, we analyze the parallel performance of several classes of 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 corresponding techniques that are used to scale such applications to large scale. Moreover, we tackle the problem of programmer productivity by incorporating these techniques into a general search engine framework 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 32,768 processors.

Heuristics for Domain-Independent Planning: A Survey

2011 ◽  
Vol 135-136 ◽  
pp. 573-577 ◽  
Author(s):  
Rui Shi Liang ◽  
Min Huang
Keyword(s):  
State Space ◽  
Heuristic Search ◽  
Research Work ◽  
Future Research ◽  
Survey Analysis ◽  
Work Related ◽  
State Space Search ◽  
Survey Results ◽  
Domain Independent ◽  
Intense Research

Increasing interest has been devoted to Planning as Heuristic Search over the years. Intense research has focused on deriving fast and accurate heuristics for domain-independent planning. This paper reports on an extensive survey and analysis of research work related to heuristic derivation techniques for state space search. Survey results reveal that heuristic techniques have been extensively applied in many efficient planners and result in impressive performances. We extend the survey analysis to suggest promising avenues for future research in heuristic derivation and heuristic search techniques.

Multiobjective State Space Search

1999 ◽  
pp. 19-48
Author(s):  
Pallab Dasgupta ◽  
P. P. Chakrabarti ◽  
S. C. DeSarkar
Keyword(s):  
State Space ◽  
State Space Search
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