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40
Backtracking in distributed constraint networks
 International Journal on Artificial Intelligence Tools
, 1998
"... The adaptation of software technology to distributed environments is an important challenge today. In this work we combine parallel and distributed search. By this way we add the potential speedup of a parallel exploration in the processing of distributed problems. This paper extends DIBT, a distri ..."
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Cited by 82 (15 self)
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The adaptation of software technology to distributed environments is an important challenge today. In this work we combine parallel and distributed search. By this way we add the potential speedup of a parallel exploration in the processing of distributed problems. This paper extends DIBT, a distributed search procedure operating in distributed constraint networks [6]. The extension is twofold. First the procedure is updated to face delayed information problems upcoming in heterogeneous systems. Second, the search is extended to simultaneously explore independent parts of a distributed search tree. By this way we introduce parallelism into distributed search, which brings to Interleaved Distributed Intelligent BackTracking (IDIBT). Our results show that 1) insoluble problems do not greatly degrade performance over DIBT and 2) superlinear speedup can be achieved when the distribution of solution is nonuniform.
Parallel BestFirst Search of StateSpace Graphs: A Summary of Results
 in Proc. 10th Nat. Conf. AI, AAAI
, 1988
"... This paper presents many different parallel formulations of the A*/BranchandBound search algorithm. The parallel formulations primarily differ in the data structures used. Some formulations are suited only for sharedmemory architectures, whereas others are suited for distributedmemory architectur ..."
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Cited by 42 (4 self)
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This paper presents many different parallel formulations of the A*/BranchandBound search algorithm. The parallel formulations primarily differ in the data structures used. Some formulations are suited only for sharedmemory architectures, whereas others are suited for distributedmemory architectures as well. These parallel formulations have been implemented to solve the vertex cover problem and the TSP problem on the BBN Butterfly parallel processor. Using appropriate data structures, we are able to obtain fairly linear speedups for as many as 100 processors. We also discovered problem characteristics that make certain formulations more (or less) suitable for some search problems. Since the bestfirst search paradigm of A*/BranchandBound is very commonly used, we expect these parallel formulations to be effective for a variety of problems. Concurrent and distributed priority queues used in these parallel formulations can be used in many parallel algorithms other than parallel A*/bra...
Interleaved and Discrepancy Based Search
 In Proceedings of ECAI98
, 1998
"... . We present a detailed experimental comparison of interleaved depthfirst search and depthbounded discrepancy search, two tree search procedures recently developed with the same goal: to reduce the cost of heuristic mistakes at the top of the tree. Our comparison uses an abstract heuristic model, ..."
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Cited by 23 (4 self)
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. We present a detailed experimental comparison of interleaved depthfirst search and depthbounded discrepancy search, two tree search procedures recently developed with the same goal: to reduce the cost of heuristic mistakes at the top of the tree. Our comparison uses an abstract heuristic model, and three different concrete problem classes: binary constraint satisfaction, quasigroup completion and number partitioning problems. Results indicate that both search strategies often reduce search. In addition, they show that their efficiency depends on a tradeoff between the number of discrepancies (branch points against the heuristic) considered at the top of the tree, and the overhead of expanding branches from these discrepancies. If the number of discrepancies is large, the overhead can outweigh the benefits. 1 INTRODUCTION By definition, heuristics sometimes make mistakes. When searching a tree with depthfirst search (Dfs), mistakes made at the top of the tree can be very costly ...
Parallel Processing of Discrete Optimization Problems
 IN ENCYCLOPEDIA OF MICROCOMPUTERS
, 1993
"... Discrete optimization problems (DOPs) arise in various applications such as planning, scheduling, computer aided design, robotics, game playing and constraint directed reasoning. Often, a DOP is formulated in terms of finding a (minimum cost) solution path in a graph from an initial node to a goa ..."
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Cited by 19 (6 self)
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Discrete optimization problems (DOPs) arise in various applications such as planning, scheduling, computer aided design, robotics, game playing and constraint directed reasoning. Often, a DOP is formulated in terms of finding a (minimum cost) solution path in a graph from an initial node to a goal node and solved by graph/tree search methods such as branchandbound and dynamic programming. Availability of parallel computers has created substantial interest in exploring the use of parallel processing for solving discrete optimization problems. This article provides an overview of parallel search algorithms for solving discrete optimization problems.
Asynchronous partial overlay: A new algorithm for solving distributed constraint satisfaction problems
 Journal of Artificial Intelligence Research (JAIR
, 2006
"... Distributed Constraint Satisfaction (DCSP) has long been considered an important problem in multiagent systems research. This is because many realworld problems can be represented as constraint satisfaction and these problems often present themselves in a distributed form. In this article, we pres ..."
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Cited by 18 (0 self)
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Distributed Constraint Satisfaction (DCSP) has long been considered an important problem in multiagent systems research. This is because many realworld problems can be represented as constraint satisfaction and these problems often present themselves in a distributed form. In this article, we present a new complete, distributed algorithm called asynchronous partial overlay (APO) for solving DCSPs that is based on a cooperative mediation process. The primary ideas behind this algorithm are that agents, when acting as a mediator, centralize small, relevant portions of the DCSP, that these centralized subproblems overlap, and that agents increase the size of their subproblems along critical paths within the DCSP as the problem solving unfolds. We present empirical evidence that shows that APO outperforms other known, complete DCSP techniques. 1.
Solving Large FPT Problems On Coarse Grained Parallel Machines
"... Fixedparameter tractability(FPT) techniques have recently been successful in solving NPcomplete problem instances of practical importance which were too large to be solved with previous methods. In this paper we show how to enhance this approach through the addition of parallelism, thereby allowin ..."
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Cited by 17 (1 self)
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Fixedparameter tractability(FPT) techniques have recently been successful in solving NPcomplete problem instances of practical importance which were too large to be solved with previous methods. In this paper we show how to enhance this approach through the addition of parallelism, thereby allowing even larger problem instances to be solved in practice. More precisely, we demonstrate the potential of parallelism when applied to the bounded tree search phase of FPT algorithms. We apply our methodology to the kVertex Cover problem which has important applications, e.g., in multiple sequence alignments for computational biochemistry. We have implemented our parallel FPT method and application specific "plugin" code for the kVertex Cover problem using C and the MPI communication library, and tested it on a network of 10 Sun SPARC workstations. This is the first experimental examination of parallel FPT techniques. In our experiments, we obtain excellent speedup results. Not only do we achieve a speedup of p in most cases, many cases even exhibit a super linear speedup. The latter result implies that our parallel methods, when simulated on a single processor, also yield a significant improvement over existing sequential methods.
Parallel cooperative propositional theorem proving
 Annals of Mathematics and Artificial Intelligence
, 1998
"... A parallel satis ability testing algorithm called Parallel Modoc is presented. Parallel Modoc is based on Modoc, which is based on propositional Model Elimination with an added capability to prune away certain branches that cannot lead to a successful subrefutation. The pruning information is encod ..."
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Cited by 13 (3 self)
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A parallel satis ability testing algorithm called Parallel Modoc is presented. Parallel Modoc is based on Modoc, which is based on propositional Model Elimination with an added capability to prune away certain branches that cannot lead to a successful subrefutation. The pruning information is encoded in a partial truth assignment called an autarky. Parallel Modoc executes multiple instances of Modoc as separate processes and allows processes to cooperate by sharing lemmas and autarkies as they are found. When a Modoc process nds a new autarky or a new lemma, it makes the information available to other Modoc processes via a \blackboard". Combining autarkies generally is not straightforward because two autarkies found by two separate processes may have con icting assignments. The paper presents an algorithm to combine two arbitrary autarkies to form a larger autarky. Experimental results show that for many of the formulas, Parallel Modoc achieves speedup greater than the number of processors. Formulas that could not be solved in an hour by Modoc were often solved by Parallel Modoc in the order of minutes, and in some cases, in seconds.
KBFS: KBestFirst Search
 Annals of Mathematics and Artificial Intelligence
, 2003
"... We introduce a new algorithm, Kbestfirst search (KBFS), which is a generalization of the well known bestfirst search. In KBFS, each iteration simultaneously expands the K best nodes from the openlist (rather than just the best as in BFS). We claim that KBFS outperforms BFS in domains where t ..."
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Cited by 13 (1 self)
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We introduce a new algorithm, Kbestfirst search (KBFS), which is a generalization of the well known bestfirst search. In KBFS, each iteration simultaneously expands the K best nodes from the openlist (rather than just the best as in BFS). We claim that KBFS outperforms BFS in domains where the heuristic function has large errors in estimation of the real distance to the goal state or does not predict deadends in the search tree. We present empirical results that confirm this claim and show that KBFS outperforms BFS by a factor of 15 on random trees with deadends, and by a factor of 2 and 7 on the Fifteen and TwentyFour tile puzzles, respectively. KBFS also finds better solutions than BFS and hillclimbing for the number partitioning problem. KBFS is only appropriate for finding approximate solutions with inadmissible heuristic functions.
Solving large FPT problems on coarsegrained parallel machines
 JOURNAL OF COMPUTER AND SYSTEM SCIENCES
, 2003
"... Fixedparamd er tractability (FPT) techniques have recently been successful in solving NPcomplete problem instances of practicalimct tance which were too large to be solved with previousmevio s. In this paper, we show how to enhance this approach through the addition of parallelism thereby allowing ..."
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Cited by 12 (1 self)
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Fixedparamd er tractability (FPT) techniques have recently been successful in solving NPcomplete problem instances of practicalimct tance which were too large to be solved with previousmevio s. In this paper, we show how to enhance this approach through the addition of parallelism thereby allowing even larger problem instances to be solved in practice. More precisely, wedem nstrate the potential of parallelism when applied to the boundedtree search phase of FPT algorithmr We apply ourmrFWWqN ogy to the kVertex Cover problem which hasimFI tant applications in, forexamV e, the analysis ofmFWBNWW sequence align mgnF forcomN tational biochemchFI . We have ime emeF ed our parallel PTmFWWfi for using C and the MPI comFI4 cation library, and tested it on a 32node Beowulf cluster. This is the first experimqBVB exam nation of parallel PT techniques. As part of our experi mperi we solved larger instances of kVertex Cover than in any previously reported imported ations. orexamfi e, our code can solve problem instances with kX400 in less than 1.5h.
Strategies for distributed constraint satisfaction problems
 In Proc. of the 13th International Workshop on DAI
, 1994
"... Constraint satisfaction problems are important in AI. Various distributed and parallel computing strategies have been proposed to solve these problems. In this paper, these strategies are classified as distributedagentbased, parallelagentbased, and functionagentbased distributed problemsolvin ..."
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Cited by 12 (0 self)
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Constraint satisfaction problems are important in AI. Various distributed and parallel computing strategies have been proposed to solve these problems. In this paper, these strategies are classified as distributedagentbased, parallelagentbased, and functionagentbased distributed problemsolving strategies. These different strategies are presented and discussed. Parallelagentbased strategies are found to be very versatile. Computational experience is presented. 1