9,544 research outputs found
Asynchronous Partial Overlay: A New Algorithm for Solving Distributed Constraint Satisfaction Problems
Distributed Constraint Satisfaction (DCSP) has long been considered an
important problem in multi-agent systems research. This is because many
real-world 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
The impact of the conflict on solving distributed constraint satisfaction problems
Distributed Constraint Satisfaction Problems (DCSPs) involve a vast number of AI andMulti-Agent problems. Many important efforts have been recen accomplished for solving these kinds of problems using both backtracking-based and mediation-based methods. One of the most successful mediation based algorithms in this field is Asynchronous Partial Overlay (APO) algorithm. By choosing some agents as mediators, APO tries to centralize portions of the distributed problem, and then each mediator tries to solve its centralized sub-problem. This work continues until the whole problem is solved. This paper presents a new strategy to select mediators. The main idea behind this strategy is that the number of mediators conflicts (violated constraints) impacts directly on its performance. Experimental results show that choosing the mediators with the most number of conflicts not only leads to considerable decrease in APO complexity, but also it can decrease the complexity of the other extensions of the APO such as IAPO algorithm. MaxCAPO and MaxCIAPO are two new expansions of APO which introduce this idea and are presented in this article. The results of using this mediator selection strategy show a rapid and desirable improvement over various parameters in comparison with APO and IAP
Spatio-Temporal Context in Agent-Based Meeting Scheduling
Meeting scheduling is a common task for organizations of all sizes. It involves searching for a time and place when and where all the participants can meet. However, scheduling a meeting is generally difficult in that it attempts to satisfy the preferences of all participants. Negotiation tends to be an iterative and time consuming task. Proxy agents can handle the negotiation on behalf of the individuals without sacrificing their privacy or overlooking their preferences. This thesis examines the implications of formalizing meeting scheduling as a spatiotemporal negotiation problem. The âChildren in the Rectangular Forestâ (CRF) canonical model is applied to meeting scheduling. By formalizing meeting scheduling within the CRF model, a generalized problem emerges that establishes a clear relationship with other spatiotemporal distributed scheduling problems. The thesis also examines the implications of the proposed formalization to meeting scheduling negotiations. A protocol for meeting location selection is presented and evaluated using simulations
A new model for solution of complex distributed constrained problems
In this paper we describe an original computational model for solving
different types of Distributed Constraint Satisfaction Problems (DCSP). The
proposed model is called Controller-Agents for Constraints Solving (CACS). This
model is intended to be used which is an emerged field from the integration
between two paradigms of different nature: Multi-Agent Systems (MAS) and the
Constraint Satisfaction Problem paradigm (CSP) where all constraints are
treated in central manner as a black-box. This model allows grouping
constraints to form a subset that will be treated together as a local problem
inside the controller. Using this model allows also handling non-binary
constraints easily and directly so that no translating of constraints into
binary ones is needed. This paper presents the implementation outlines of a
prototype of DCSP solver, its usage methodology and overview of the CACS
application for timetabling problems
Optimisation sous contraintes de problÚmes distribués par auto-organisation coopérative
Quotidiennement, divers problÚmes d'optimisation : minimiser un coût de production, optimiser le parcours d'un véhicule, etc sont à résoudre. Ces problÚmes se caractérisent par un degré élevé de complexité dû à l'hétérogénéité et la diversité des acteurs en jeu, à la masse importante des données ainsi qu'à la dynamique des environnements dans lesquels ils sont plongés.
Face à la complexité croissante de ces applications, les approches de résolution classiques ont montré leurs limites. Depuis quelques années, la communauté scientifique s'intéresse aux développements de nouvelles solutions basées sur la distribution du calcul et la décentralisation du contrÎle plus adaptées à ce genre de problÚme. La théorie des AMAS (Adaptive Multi-Agents Systems) propose le développement de solutions utilisant des systÚmes multi-agents auto-adaptatifs par auto-organisation coopérative. Cette théorie a montré son adéquation pour la résolution de problÚmes complexes et dynamiques, mais son application reste à un niveau d'abstraction assez élevé.
L'objectif de ce travail est de spécialiser cette théorie pour la résolution de ce genre de problÚmes. Ainsi, son utilisation en sera facilitée. Pour cela, le modÚle d'agents AMAS4Opt avec des comportements et des interactions coopératifs et locaux a été défini. La validation s'est effectuée sur deux problÚmes clés d'optimisation : le contrÎle manufacturier et la conception de produit complexe. De plus, afin de montrer la robustesse et l'adéquation des solutions développées, un ensemble de critÚres d'évaluation permettant de souligner les points forts et faibles des systÚmes adaptatifs et de les comparer à des systÚmes existants a été défini.We solve problems and make decisions all day long. Some problems and decisions are very challenging: What is the best itinerary to deliver orders given the weather, the traffic and the hour? How to improve product manufacturing performances? etc. Problems that are characterized by a high level of complexity due to the heterogeneity and diversity of the participating actors, to the increasing volume of manipulated data and to the dynamics of the applications environments.
Classical solving approaches have shown their limits to cope with this growing complexity. For the last several years, the scientific community has been interested in the development of new solutions based on computation distribution and control decentralization. The AMAS (Adaptive Multi-Agent-Systems) theory proposes to build solutions based on self-adaptive multi-agent systems using cooperative self-organization. This theory has shown its adequacy to solve different complex and dynamic problems, but remains at a high abstraction level.
This work proposes a specialization of this theory for complex optimization problem solving under constraints. Thus, the usage of this theory is made accessible to different non-AMAS experts' engineers. Thus, the AMAS4Opt agent model with cooperative, local and generic behaviours and interactions has been defined.This model is validated on two well-known optimization problems: scheduling in manufacturing control and complex product design. Finally, in order to show the robustness and adequacy of the developed solutions, a set of evaluation criteria is proposed to underline the advantages and limits of adaptive systems and to compare them with already existing systems
Coalition Formation For Distributed Constraint Optimization Problems
This dissertation presents our research on coalition formation for Distributed Constraint Optimization Problems (DCOP). In a DCOP, a problem is broken up into many disjoint sub-problems, each controlled by an autonomous agent and together the system of agents have a joint goal of maximizing a global utility function. In particular, we study the use of coalitions for solving distributed k-coloring problems using iterative approximate algorithms, which do not guarantee optimal results, but provide fast and economic solutions in resource constrained environments. The challenge in forming coalitions using iterative approximate algorithms is in identifying constraint dependencies between agents that allow for effective coalitions to form. We first present the Virtual Structure Reduction (VSR) Algorithm and its integration with a modified version of an iterative approximate solver. The VSR algorithm is the first distributed approach for finding structural relationships, called strict frozen pairs, between agents that allows for effective coalition formation. Using coalition structures allows for both more efficient search and higher overall utility in the solutions. Secondly, we relax the assumption of strict frozen pairs and allow coalitions to form under a probabilistic relationship. We identify probabilistic frozen pairs by calculating the propensity between two agents, or the joint probability of two agents in a k-coloring problem having the same value in all satisfiable instances. Using propensity, we form coalitions in sparse graphs where strict frozen pairs may not exist, but there is still benefit to forming coalitions. Lastly, we present a cooperative game theoretic approach where agents search for Nash stable coalitions under the conditions of additively separable and symmetric value functions
A Parameterisation of Algorithms for Distributed Constraint Optimisation via Potential Games
This paper introduces a parameterisation of learning algorithms for distributed constraint optimisation problems (DCOPs). This parameterisation encompasses many algorithms developed in both the computer science and game theory literatures. It is built on our insight that when formulated as noncooperative games, DCOPs form a subset of the class of potential games. This result allows us to prove convergence properties of algorithms developed in the computer science literature using game theoretic methods. Furthermore, our parameterisation can assist system designers by making the pros and cons of, and the synergies between, the various DCOP algorithm components clear
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