Partial constraint satisfaction

Freuder, Eugene C.; Wallace, Richard J.

doi:10.1007/3-540-61479-6_18

Cited by 124 publications

(147 citation statements)

References 17 publications

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“…or "evaluate!" message from all of the agents that it has sent a request to, it computes a solution using a Branch and Bound search [3]. The goal of the search is to find a conflict-free solution for the variables in the session and to minimize the number of conflicts for variables outside the session (like the minconflict heuristic [13]).…”

Section: Mediationmentioning

confidence: 99%

Solving Distributed CSPs Using Dynamic, Partial Centralization without Explicit Constraint Passing

Mailler

Graves

2012

Principles and Practice of Multi-Agent Systems

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Dynamic, partial centralization has received a considerable amount of attention in the distributed problem solving community. As the name implies, this technique works by dynamically identifying portions of a shared problem to centralize in order to speed the problem solving process. Currently, a number of algorithms have been created which employ this simple, yet powerful technique to solve problems such as distributed constraint satisfaction (DCSP), distributed constraint optimization (DCOP), and distributed resource allocation.In fact, one such algorithm, Asynchronous Partial Overlay (APO), was shown to outperform the Asynchronous Weak Commitment (AWC) protocol, which is one of the best known methods for solving DCSPs. One of the key differences between these two algorithms is that APO, as part of the centralization process, uses explicit constraint passing. AWC, on the other hand, passed nogoods because it tries to provide security and privacy. Because of these differences in underlying assumptions, a number of researchers have criticized the comparison between these two protocols.This paper attempts to resolve this disparity by introducing a new AWC/APO algorithm called Nogood-APO that like AWC uses nogood passing to provide security and privacy and like APO uses dynamic partial centralization to speed the problem solving process. Like its parent algorithms, this new protocol is sound and complete and performs nearly as well as APO, while still outperforming AWC, on distributed 3-coloring problems. In addition, this paper shows that Nogood-APO provides more privacy to the agents than both APO and AWC on all but the sparsest problems. These findings demonstrate that a dynamic, partial centralization-based protocol can provide privacy and that even when operating with the same assumptions as AWC still solves problems in fewer cycles using less computation and communication..

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Section: Mediationmentioning

confidence: 99%

Solving Distributed CSPs Using Dynamic, Partial Centralization without Explicit Constraint Passing

Mailler

Graves

2012

Principles and Practice of Multi-Agent Systems

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“…A further extension of CSPs considering also preferences among solutions is Soft CSPs: preferences are expressed as soft constraints and a solution has to satisfy all hard constraints and as much as possible of soft constraints (preferences) [4]. Depending on the approach, the most important ones (hierarchical CSP [39]) can be satisfied, or the number of violated constraints (Partial CSP [13]) can be minimized or some satisfaction level (semiring-based CSP [4]) can be maximized. Our approach is more similar to the semiring-based one, however in such an approach only a partial order between preferences can be modeled and no conditional preference can be expressed, even if some attempts have been done by Domshlak et al [9] to mix hard and soft constraints with CP-nets [5], which express qualitative preferences (like conditional ones) over the values of a single property of the outcomes.…”

Section: Constraint Satisfaction Problems (Csp)mentioning

confidence: 99%

Logic-based automated multi-issue bilateral negotiation in peer-to-peer e-marketplaces

Ragone

Noia

Sciascio

et al. 2008

Auton Agent Multi-Agent Syst

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Abstract. We present a novel logic-based framework to automate multi-issue bilateral negotiation in e-commerce settings. The approach exploits logic as communication language among agents, and optimization techniques in order to find Pareto-efficient agreements. We introduce P(N ), a propositional logic extended with concrete domains, which allows one to model relations among issues (both numerical and non-numerical ones) via logical entailment, differently from wellknown approaches that describe issues as uncorrelated. Through P(N ) it is possible to represent buyer's request, seller's supply and their respective preferences as formulas endowed with a formal semantics, e.g., "if I spend more than 30000 e for a sedan then I want more than a two-years warranty and a GPS system included". We mix logic and utility theory in order to express preferences in a qualitative and quantitative way. We illustrate the theoretical framework, the logical language, the one-shot negotiation protocol we adopt, and show we are able to compute Pareto-efficient outcomes, using a mediator to solve an optimization problem. We prove the computational adequacy of our method by studying the complexity of the problem of finding Pareto-efficient solutions in our setting.

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“…The difference of our approach to that of the artificial intelligence community is that we try to maximise the number of variables (sites) with a conflict-free assignment, while their objective is to either list all assignment tuples without conflicts [MF85], to minimise the number of conflicts [FW92], or to find the maximum weighted subset of constraints which still allows an assignment.…”

Section: The Label Number Maximisation Problemmentioning

confidence: 99%

“…In such systems one has to be content with imperfect solutions. Most effort in the CSP community has been directed to finding solutions that violate as few constraints as possible [FW92,Jam96,JFM96]. When labeling maps, such violations would result in label overplots and thus poor legibility.…”

Section: Frameworkmentioning

confidence: 99%

A Combinatorial Framework for Map Labeling

Wagner

Wolff

1998

Graph Drawing

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Abstract. The general map labeling problem consists in labeling a set of sites (points, lines, regions) given a set of candidates (rectangles, circles, ellipses, irregularly shaped labels) for each site. A map can be a classical cartographical map, a diagram, a graph or any other figure that needs to be labeled. A labeling is either a complete set of non-conflicting candidates, one per site, or a subset of maximum cardinality. Finding such a labeling is NP-hard. We present a combinatorial framework to attack the problem in its full generality. The key idea is to separate the geometric from the combinatorial part of the problem. The latter is captured by the conflict graph of the candidates and by rules which successively simplify this graph towards a near-optimal solution. We exemplify this framework at the problem of labeling point sets with axis-parallel rectangles as candidates, four per point. We do this such that it becomes clear how our concept can be applied to other cases. We study competing algorithms and do a thorough empirical comparison. The new algorithm we suggest is fast, simple and effective.

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Partial constraint satisfaction

Cited by 124 publications

References 17 publications

Solving Distributed CSPs Using Dynamic, Partial Centralization without Explicit Constraint Passing

Solving Distributed CSPs Using Dynamic, Partial Centralization without Explicit Constraint Passing

Logic-based automated multi-issue bilateral negotiation in peer-to-peer e-marketplaces

A Combinatorial Framework for Map Labeling

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