Dynamic Ordering for Asynchronous Backtracking on DisCSPs

Zivan, Roie; Meisels, Amnon

doi:10.1007/s10601-006-8062-0

Cited by 25 publications

(31 citation statements)

References 19 publications

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Order By: Relevance

“…A heuristic which achieved a significant improvement was inspired by Dynamic Backtracking [1,8] in which the agent which sends a Nogood is advanced in the new order to be immediately after the agent to whom the Nogood was sent. The explanation for the success of this heuristic is that it does not cause the removal of relevant Nogoods as do other heuristics [29].…”

mentioning

confidence: 97%

“…An agent can propose an order change each time it replaces its assignment. Each order is timestamped according to the assignments performed by agents [29]. The method of timestamping for defining the most updated order is the same that was used in Nguyen et al [15] and Meisels and Zivan [13] for choosing the most updated partial assignment.…”

mentioning

confidence: 99%

“…The results presented in Zivan and Meisels [29] show that the performance of ABT_DO is highly dependent on the selected heuristic. The classic Min-domain heuristic was implemented by including the current domain size of agents in the messages they send.…”

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confidence: 99%

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Min-domain retroactive ordering for Asynchronous Backtracking

2008

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Ordering heuristics are a powerful tool in CSP search algorithms. Among the most successful ordering heuristics are heuristics which enforce a fail first strategy by using the Min-domain property (Haralick and Elliott, Artif Intel 14:263-313, 1980; Bessiere and Regin, Mac and combined heuristics: two reasons to forsake FC (and CBJ?) on hard problems. . Ordering heuristics have been introduced recently to asynchronous backtracking (ABT), for distributed constraints satisfaction (DisCSP) (Zivan and Meisels, Dynamic ordering for asynchronous backtracking on discsps. In CP-2005, pp. 32-46, Sigtes (Barcelona), Spain, 2005). However, the pioneering study of dynamically ordered ABT, ABT_DO, has shown that a straightforward implementation of the Mindomain heuristic does not produce the expected improvement over a static ordering. The present paper proposes an asynchronous dynamic ordering which does not follow the standard restrictions on the position of reordered agents in ABT_DO. Agents can be moved to a position that is higher than that of the target of the backtrack. Combining the Nogood-triggered heuristic and the Min-domain property in this new class of heuristics results in the best performing version of ABT_DO.The new version of retroactively ordered ABT is faster by a large factor than the best form of ABT.

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confidence: 99%

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Min-domain retroactive ordering for Asynchronous Backtracking

2008

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“…Moreover, another dynamic priority-related perspective for our approach may improve global performance. Research on DisCSP shows that considerable performance enhancements can be obtained by using dynamic priority for agents and appropriate heuristics [10,44,70]. Finally, an interesting improvement for DBS would involve investigating DCOP problems (i.e., a DisCSP with a function to optimize) and seeking the optimal solution (instead of just one solution) [23,25,39,48].…”

Section: Resultsmentioning

confidence: 99%

Multi-variable distributed backtracking with sessions

et al. 2014

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The Constraint Satisfaction Problem (CSP) formalism is used to represent many combinatorial decision problems instances simply and efficiently. However, many such problems cannot be solved on a single, centralized computer for various reasons (e.g., their excessive size or privacy). The Distributed CSP (DisCSP) extends the CSP model to allow such combinatorial decision problems to be modelled and handled. In this paper, we propose a complete DisCSP-solving algorithm, called Distributed Backtracking with Sessions (DBS), which can solve DisCSP so that each agent encapsulates a whole "complex" problem with many variables and constraints. We prove that the algorithm is sound and complete, and generates promising experimental results.

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“…Therefore, various improvements to ABT have been proposed [12][13][14][15] which either reduce the number of obsolete messages or the space required for storing nogoods. In addition there is a version of ABT which uses just one nogood per domain value [15] which is of interest to us.…”

Section: Asynchronous Backtracking Algorithm (Abt)mentioning

confidence: 99%

DynABT: Dynamic Asynchronous Backtracking for Dynamic DisCSPs

Omomowo

Arana

Ahriz

Artificial Intelligence: Methodology, Systems, and Applications

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Abstract. Constraint Satisfaction has been widely used to model static combinatorial problems. However, many AI problems are dynamic and take place in a distributed environment, i.e. the problems are distributed over a number of agents and change over time. Dynamic Distributed Constraint Satisfaction Problems (DDisCSP) [1] are an emerging field for the resolution problems that are dynamic and distributed in nature. In this paper, we propose DynABT, a new Asynchronous algorithm for DDisCSPs which combines solution and reasoning reuse i.e. it handles problem changes by modifying the existing solution while re-using knowledge gained from solving the original(unchanged) problem. The benefits obtained from this approach are two-fold: (i) new solutions are obtained at a lesser cost and; (ii) resulting solutions are stable i.e. close to previous solutions. DynABT has been empirically evaluated on problems of varying difficulty and several degrees of changes has been found to be competitive for the problem classes tested.

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Dynamic Ordering for Asynchronous Backtracking on DisCSPs

Cited by 25 publications

References 19 publications

Min-domain retroactive ordering for Asynchronous Backtracking

Min-domain retroactive ordering for Asynchronous Backtracking

Multi-variable distributed backtracking with sessions

DynABT: Dynamic Asynchronous Backtracking for Dynamic DisCSPs

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