Distributed Constraint Satisfaction Research Articles

Concurrent search for distributed CSPs

A distributed concurrent search algorithm for distributed constraint satisfaction problems ( DisCSPs) is presented. Concurrent search algorithms are composed of multiple search processes ( SPs) that operate concurrently and scan non-intersecting parts of the global search space. Each SP is represented by a unique data structure, containing a current partial assignment ( CPA), that is circulated among the different agents. Search processes are generated dynamically, started by the initializing agent, and by any number of agents during search. In the proposed, ConcDB, algorithm, all search processes perform dynamic backtracking. As a consequence of backjumping, a search space can be found unsolvable by a different search process. This enhances the efficiency of the ConcDB algorithm. Concurrent Dynamic Backtracking is an asynchronous distributed algorithm and is shown to be faster than former algorithms for solving DisCSPs. Experimental evaluation of ConcDB, on randomly generated DisCSPs demonstrates that the network load of ConcDB is similar to the network load of synchronous backtracking and is much lower than that of asynchronous backtracking. The advantage of Concurrent Search is more pronounced in the presence of imperfect communication, when messages are randomly delayed.

Research and Development of Distributed Constraint Satisfaction Problems

With the rapid development and wide application of the Internet technology, many problems of Artificial Intelligence, for example scheduling, planning, resource allocation etc., are formally distributed now, which turn into a kind of multi-agent system problems. Accordingly, the standard constraint satisfaction problems turn into distributed constraint satisfaction problems, which become the general architecture for resolving multi-agent system. This paper first briefly introduces the basic concepts of distributed CSPs, and then summarizes the basic and the improved algorithms. Their efficiency and performance are analyzed and the typical applications of distributed CSPs in recent years are discussed. Finally, this paper presents the extensions of the basic formalization and the research trends in this area. Recent related work indicates that the future work will focus on the theoretical research to present the solid theoretical foundation for the practical problems.

Peer‐based automatic configuration of pervasive applications

Pervasive computing envisions seamless support for user tasks through cooperating devices that are present in an environment. Fluctuating availability of devices, induced by mobility and failures, requires mechanisms and algorithms that allow applications to adapt to their ever‐changing execution environments without user intervention. To ease the development of adaptive applications, Becker et al. (3) have proposed the peer‐based component system PCOM. This system provides fundamental mechanisms to support the automated composition of applications at runtime. In this article, we discuss the requirements on algorithms that enable automatic configuration of pervasive applications. Furthermore, we show how finding a configuration can be interpreted as Distributed Constraint Satisfaction Problem. Based on this, we present an algorithm that is capable of finding an application configuration in the presence of strictly limited resources. To show the feasibility of this algorithm, we present an evaluation based on simulations and real‐world measurements and we compare the results with a simple greedy approximation.

An Arc-Consistency Algorithm for Dynamic and Distributed Constraint Satisfaction Problems

This paper presents the new DDAC4 algorithm for dynamic arc consistency enforcement in distributed constraint satisfaction problems (CSP). The algorithm is an adaptation of the well-known AC-4 algorithm to system settings where constraints can be added and deleted in concurrent processes. It is the first algorithm for arc-consistency enforcement in this system setting. Arc-consistency is achieved whenever the overall system reaches quiescence after a constraint is added or deleted.

A case for adaptation of the distributed environment layout in multiagent applications

A multiagent application consists of an environment and a number of agents. The environment contains information that the agents use and manipulate to do their work. When a multiagent system is distributed over a number of different hosts (i.e. more than one execution platform is used), the environment has to be distributed as well. The distributed layout of the environment can influence the performance of agents and of the system.In this paper we discuss when a distributed environment can dynamically change its distribution layout. Our focus is on a distributed environment in which mobile agents move around and are aware of the distributed nature of the system.Changes to the layout of the distribution are not only triggered by the agents (like other, application-specific actions), but they can also be triggered by external events, and the environment itself has the ability to pro-actively change its distribution layout over the different hosts when this improves the behavior and efficiency of the application.Using a specific application (solving distributed constraint satisfaction problems) as an example, we indicate the usefulness of changes to the distribution layout and how they can be incorporated easily into a multiagent application design. It turns out that for some problems, the improvement in efficiency can be more than 30%.

Recurrent distributed constraint satisfaction, virtual constraints, and meta-evolutionary protocols

The distributed constraint satisfaction problem (DisCSP) can be viewed as a 4-tuple (X , D , C , A ), where X is a set of n variables, D is a set of n domains (one domain for each of the n variables), C is a set of constraints that constrain the valu

Distributed Asymmetric Constraint Satisfaction, the Phase Transition, and Evolutionary Protocols

Abstract The distributed constraint satisfaction problem (DisCSP) can be viewed as a 4-tuple (X, D, C, A), where X is a set of n variables, D is a set of n domains (one domain for each of the n vaziables), C is a set of constraints that constrain the values that can be assigned to the n variables, and A is a set of agents for which the variables and constraints are distributed. The objective in solving a DisCSP is to allow the agents in A to develop a consistent distributed solution by means of message passing. The constraints are typically considered private and are not allowed to be communicated to fellow agents. When constraint privacy is enforced, inter-agent constraints that exist between the variables of multiple agents will most likely be asymmetric. In this paper, we develop an equation for predicting where the phase transition might be in randomly generated distributed asymmetric CSPs (DisACSPs). We then test this equation by compazing the performances of six evolutionazy protocols referred to as...

The distributed breakout algorithms

We present a new series of distributed constraint satisfaction algorithms, the distributed breakout algorithms, which is inspired by local search algorithms for solving the constraint satisfaction ...

Communicating sequential processes for distributed constraint satisfaction

CSPCONS is a programming language that supports program execution over multiple Prolog processes with constraints. The language is an extended version of C sp-ii, a version of Prolog that supports channel-based communicating processes and TCP/IP communication, that is based on the CSP model introduced by Hoare. C spcons inherits all the advanced features of C sp-ii and extends it by introducing constraint solving capabilities to the processes. In C spcons each Prolog process has one or more solvers attached and each solver is independent from the others, following the original C sp-ii model, thus resulting to a communicating sequential constraint logic programming system. Such a model can facilitate greatly the implementation of distributed CLP applications. This paper describes the original C sp-ii system along with details of the extensions that resulted to the C spcons system and presents an example demonstrating the applicability of the system to distributed constraint satisfaction problems.

Asynchronous aggregation and consistency in distributed constraint satisfaction

Constraint Satisfaction Problems (CSP) have been very successful in problem-solving tasks ranging from resource allocation and scheduling to configuration and design. Increasingly, many of these tasks pose themselves in a distributed setting where variables and constraints are distributed among different agents. A variety of asynchronous search algorithms have been proposed for addressing this setting. We show how two techniques commonly used in centralized constraint satisfaction, value aggregation and maintaining arc consistency can be applied to increase efficiency in an asynchronous, distributed context as well, and report on experiments that quantify the gains.

Secure distributed constraint satisfaction: reaching agreement without revealing private information

This paper develops a secure distributed constraint satisfaction algorithm. A Distributed Constraint Satisfaction Problem (DisCSP) is a constraint satisfaction problem in which variables and constraints are distributed among multiple agents. A major motivation for solving a DisCSP without gathering all information in one server is the concern about privacy/security. However, existing DisCSP algorithms leak some information during the search process, and privacy/security issues are not dealt with formally. Our newly developed algorithm utilizes a public key encryption scheme. In this algorithm, multiple servers, which receive encrypted information from agents, cooperatively perform a search process that is equivalent to a standard chronological backtracking algorithm. This algorithm does not leak any private information on the obtained solution, i.e., neither agents nor servers can obtain any additional information on the value assignment of variables that belong to other agents.

Introduction: Special Issue on Distributed Constraint Satisfaction

Reference LIA-ARTICLE-2005-002View record in Web of Science Record created on 2006-12-13, modified on 2017-05-12

Distributed stochastic search and distributed breakout: properties, comparison and applications to constraint optimization problems in sensor networks

This research is motivated by some distributed scheduling problems in distributed sensor networks, in which computational and communication resources are scarce. We first cast these problems as distributed constraint satisfaction problems (DisCSPs) and distributed constraint optimization problems (DisCOPs) and model them as distributed graph coloring. To cope with limited resources and restricted real-time requirement, it is imperative to use distributed algorithms that have low overhead on resource consumption and high-quality anytime performance. To meet these requirements, we study two existing DisCSP algorithms, distributed stochastic search algorithm (DSA) and distributed breakout algorithm (DBA), for solving DisCOPs and the distributed scheduling problems. We experimentally show that DSA has a phase-transition or threshold behavior, in that its solution quality degenerates abruptly and dramatically when the degree of parallel executions of distributed agents increases beyond some critical value. We also consider the completeness and complexity of DBA for distributed graph coloring. We show that DBA is complete on coloring acyclic graphs, coloring an acyclic graph of n nodes in O ( n 2 ) steps. However, on a cyclic graph, DBA may never terminate. To improve DBA's performance on coloring cyclic graphs, we propose two stochastic variations. Finally, we directly compare DSA and DBA for solving distributed graph coloring and distributed scheduling problems in sensor networks. The results show that DSA is superior to DBA when controlled properly, having better or competitive solution quality and significantly lower communication cost than DBA. Therefore, DSA is the algorithm of choice for our distributed scheduling problems and other distributed problems of similar properties.

Constraint-based reasoning and privacy/efficiency tradeoffs in multi-agent problem solving

Because of privacy concerns, agents may not want to reveal information that could be of use in problem solving. As a result, there are potentially important tradeoffs between maintaining privacy and enhancing search efficiency in these situations. In this work we show how quantitative assessments of privacy loss can be made within the framework of distributed constraint satisfaction. We also show how agents can make inferences about other agents' problems or subproblems from communications that carry no explicit private information. This can be done using constraint-based reasoning in a framework consisting of an ordinary CSP, which is only partly known, and a system of “shadow CSPs” that represent various forms of possibilistic knowledge. This kind of reasoning in combination with arc consistency processing can speed up search under conditions of limited communication, at the same time potentially undermining privacy. These effects are demonstrated in a simplified meeting scheduling problem where agents propose meetings consistent with their existing schedules while responding to other proposals by accepting or rejecting them. In this situation, we demonstrate some of the conditions under which privacy/efficiency tradeoffs emerge, as well as complications that arise when agents can reason effectively under conditions of partial ignorance.

The distributed breakout algorithms

We present a new series of distributed constraint satisfaction algorithms, the distributed breakout algorithms, which is inspired by local search algorithms for solving the constraint satisfaction problem (CSP). The basic idea of these algorithms is for agents to repeatedly improve their tentative and flawed sets of assignments for variables simultaneously while communicating such tentative sets with each other until finding a solution to an instance of the distributed constraint satisfaction problem (DisCSP). We introduce four implementations of the distributed breakout algorithms: Single-DB, Multi-DB, Multi-DB +, and Multi-DB ++. Single-DB is a distributed breakout algorithm for solving the DisCSP, where each agent has a single local variable and its related constraints. Multi-DB, on the other hand, is another distributed breakout algorithm for solving the distributed SAT (DisSAT) problem, where each agent has multiple local variables and their related clauses. Multi-DB + and Multi-DB ++ are stochastic variations of Multi-DB. In Multi-DB +, we introduce a technique called random break into Multi-DB; in Multi-DB ++, we introduce a technique called random walk into Multi-DB +. We conducted experiments to compare these algorithms with the asynchronous type of distributed constraint satisfaction algorithm. Through these experiments, we found that Single-DB, Multi-DB, and Multi-DB + scale up better than the asynchronous type of distributed constraint satisfaction algorithms, but they sometimes show very poor performance. On the other hand, we also found that Multi-DB ++, which uses random walk, provides a clear performance improvement.

Multi-agent collaborative service and distributed problem solving

In this paper, we show that collaborative services can be formulated into distributed constraint satisfaction problems. We introduce the notion of multi-agent collaborative service (MACS), and employ a distributed discrete Lagrange multipliers (DDLM) method to automatically handle an MACS task. The DDLM method is based on a distributed multi-agent system. The behaviors of agents are guided by predefined DDLM rules. In order to make it more efficient in achieving a solution state, we incorporate strategies for tuning the Lagrange multipliers. We validate the effectiveness of the DDLM method with benchmark SAT problems. Furthermore, we provide the mathematical properties of DDLM and present the corresponding DDLM algorithms.

Distributed singleton consistency

Distributed constraint satisfaction has drawn much attention in the past years, with a number of algorithms proposed to tackle the problem. Research in the area has followed two directions: distributed search techniques and distributed filtering techniques. This paper presents a new distributed filtering algorithm, named Distributed Singleton Arc Consistency (Dis-SAC), which is based on the singleton consistency algorithm. Dis-SAC is a parallel, coarse-grain filtering algorithm aimed at improving the performance of singleton consistency by distributing the work to be done to a number of agents. The current paper presents the basic idea behind the algorithm and two versions of it that employ different communication policies along with experimental results obtained on a set of random binary CSP problems.

Application of multi-agent planning to the assignment problem

Nowadays, a globalization of national markets requires developing the flexible and demand-driven production systems with new innovative concepts of management, information processing, production scheduling and planning. The presented work focuses on the low-level planning, where the multi-agent solution towards a “job-machine” assignment is considered. The main point of the discussion is the flexibility of planning systems ensured by the concept of agent’s “roles” and “emergencies”. Depending on the state of “emergency”, the system receives stepwisely additional degrees of freedom to adapt the planning to the changing conditions of the manufacturing floor. The distributed constraint satisfaction and optimization approaches, underlying the suggested method, as well as activities of rescue agents, are described in the form of Petri networks providing both the conceptual notions and main details of implementation.

The effects of co-operation on multiagent search in task-oriented domains

This paper studies the benefits of teaming and selflessness when using multiagent search to solve task-oriented problems. A formal framework for multiagent search is presented, which forms a superset of the task-oriented domain, coalition formation, distributed constraint satisfaction and NK landscape search problems. The paper focuses on task-oriented domain problems and shows how the benefits of teaming and selflessness arise in this domain. These experimental results are compared to similar results in the NK domain—from which a predictive technique is imported. Namely, it is shown that better allocations are found when the dynamics of the multiagent system lie between order and chaos. Several other specific findings are presented such as the fact that neither absolute selfishness nor absolute selflessness result in better allocations, and the fact that the formation of small teams usually leads to better allocations.

Coordinative Operation Planning for End Milling

In the conventional end-milling research, operation planning is fragmented into partial planning such as determination of fixturing method, tool path generation and selection of cutting condition. Fragmented plans in operation planning are generated separately, although those generations are dependent each other. Advanced machining operations are directed to complicated and/or thin shape workpiece precision machining. This trend requires considering the interdependency among the partial plans. This paper presents a besic framework and procedure of coordinative generation of total operation planning. A distributed constraint satisfaction based schema is introduced in order to manage the interdependency among partial plans. A prototype system of coordinative generation is evaluated by a low-rigidity workpiece machining problem as an example problem. The results of example shows an effectiveness of the method.