Results 1  10
of
90
Adopt: asynchronous distributed constraint optimization with quality guarantees
 ARTIFICIAL INTELLIGENCE LABORATORY, MASSACHUSETTS INSTITUTE OF TECHNOLOGY
, 2005
"... ..."
(Show Context)
A Scalable Method for Multiagent Constraint Optimization
"... We present in this paper a new, complete method for distributed constraint optimization, based on dynamic programming. It is a utility propagation method, inspired by the sumproduct algorithm, which is correct only for treeshaped constraint networks. In this paper, we show how to extend that algor ..."
Abstract

Cited by 179 (18 self)
 Add to MetaCart
We present in this paper a new, complete method for distributed constraint optimization, based on dynamic programming. It is a utility propagation method, inspired by the sumproduct algorithm, which is correct only for treeshaped constraint networks. In this paper, we show how to extend that algorithm to arbitrary topologies using a pseudotree arrangement of the problem graph. Our algorithm requires a linear number of messages, whose maximal size depends on the induced width along the particular pseudotree chosen. We compare our algorithm with backtracking algorithms, and present experimental results. For some problem types we report orders of magnitude fewer messages, and the ability to deal with arbitrarily large problems. Our algorithm is formulated for optimization problems, but can be easily applied to satisfaction problems as well.
An Asynchronous Complete Method for Distributed Constraint Optimization
 In AAMAS
, 2003
"... We present a new polynomialspace algorithm, called Adopt, for distributed constraint optimization (DCOP). DCOP is able to model a large class of collaboration problems in multiagent systems where a solution within given quality parameters must be found. Existing methods for DCOP are not able to pr ..."
Abstract

Cited by 132 (30 self)
 Add to MetaCart
We present a new polynomialspace algorithm, called Adopt, for distributed constraint optimization (DCOP). DCOP is able to model a large class of collaboration problems in multiagent systems where a solution within given quality parameters must be found. Existing methods for DCOP are not able to provide theoretical guarantees on global solution quality while operating both efficiently and asynchronously. Adopt is guaranteed to find an optimal solution, or a solution within a userspecified distance from the optimal, while allowing agents to execute asynchronously and in parallel. Adopt obtains these properties via a distributed search algorithm with several novel characteristics including the ability for each agent to make local decisions based on currently available information and without necessarily having global certainty. Theoretical analysis shows that Adopt provides provable quality guarantees, while experimental results show that Adopt is significanfly more efficient than synchronous methods. The speedups are shown to be partly due to the novel search strategy employed and partly due to the asynchrony of the algorithm.
Asynchronous backtracking without adding links: a new member in the ABT family
, 2005
"... ..."
(Show Context)
Distributed Dynamic Backtracking
 In International Joint Conference on AI Workshop on Distributed Constraint Reasoning
, 2001
"... In the scope of distributed constraint reasoning, the main algorithms presented so far have a feature in common: the addition of links between previously unrelated agents, before or during search. This paper presents a new search procedure for finding a solution in a distributed constraint satisfact ..."
Abstract

Cited by 62 (2 self)
 Add to MetaCart
In the scope of distributed constraint reasoning, the main algorithms presented so far have a feature in common: the addition of links between previously unrelated agents, before or during search. This paper presents a new search procedure for finding a solution in a distributed constraint satisfaction problem. This algorithm makes use of some of the good properties of centralised dynamic backtracking. It ensures the completeness of search, and allows a high level of asynchronism by sidestepping the unnecessary addition of links. 1.
Propositional Satisfiability and Constraint Programming: a Comparative Survey
 ACM Computing Surveys
, 2006
"... Propositional Satisfiability (SAT) and Constraint Programming (CP) have developed as two relatively independent threads of research, crossfertilising occasionally. These two approaches to problem solving have a lot in common, as evidenced by similar ideas underlying the branch and prune algorithms ..."
Abstract

Cited by 38 (4 self)
 Add to MetaCart
Propositional Satisfiability (SAT) and Constraint Programming (CP) have developed as two relatively independent threads of research, crossfertilising occasionally. These two approaches to problem solving have a lot in common, as evidenced by similar ideas underlying the branch and prune algorithms that are most successful at solving both kinds of problems. They also exhibit differences in the way they are used to state and solve problems, since SAT’s approach is in general a blackbox approach, while CP aims at being tunable and programmable. This survey overviews the two areas in a comparative way, emphasising the similarities and differences between the two and the points where we feel that one technology can benefit from ideas or experience acquired
Message delay and DisCSP search algorithms
 ANN MATH ARTIF INTELL (2006 ) 46 : 415–439
, 2006
"... Distributed constraint satisfaction problems (DisCSPs) are composed of agents, each holding its own variables, that are connected by constraints to variables of other agents. Due to the distributed nature of the problem, message delay can have unexpected effects on the behavior of distributed searc ..."
Abstract

Cited by 32 (18 self)
 Add to MetaCart
Distributed constraint satisfaction problems (DisCSPs) are composed of agents, each holding its own variables, that are connected by constraints to variables of other agents. Due to the distributed nature of the problem, message delay can have unexpected effects on the behavior of distributed search algorithms on DisCSPs. This has been recently shown in experimental studies of asynchronous backtracking algorithms (Bejar et al., Artif. Intell., 161:117–148, 2005; Silaghi and Faltings, Artif. Intell., 161:25–54, 2005). To evaluate the impact of message delay on the run of DisCSP search algorithms, a model for distributed performance measures is presented. The model counts the number of non concurrent constraints checks, to arrive at a solution, as a non concurrent measure of distributed computation. A simpler version measures distributed computation cost by the nonconcurrent number of steps of computation. An algorithm for computing these distributed measures of computational effort is described. The realization of the model for measuring performance of distributed search algorithms is a simulator which includes the cost of message delays. Two families of distributed search algorithms on DisCSPs are investigated. Algorithms that run a single search process, and multiple search processes algorithms. The two families of algorithms are described and associated with existing algorithms. The performance of three representative algorithms of these two families is measured on randomly generated instances of DisCSPs with delayed messages. The delay of messages is found to have a strong negative effect on single search process algorithms, whether synchronous or asynchronous. Multi
Odpop: An algorithm for open/distributed constraint optimization
 In AAAI
, 2006
"... Abstract. We propose ODPOP, a new distributed algorithm for open multiagent combinatorial optimization [3]. The ODOP algorithm explores the same search space as the dynamic programming algorithm DPOP [10] or the AND/OR search algorithm AOBB [2], but does so in an incremental, bestfirst fashion suit ..."
Abstract

Cited by 30 (6 self)
 Add to MetaCart
(Show Context)
Abstract. We propose ODPOP, a new distributed algorithm for open multiagent combinatorial optimization [3]. The ODOP algorithm explores the same search space as the dynamic programming algorithm DPOP [10] or the AND/OR search algorithm AOBB [2], but does so in an incremental, bestfirst fashion suitable for open problems. ODPOP has several advantages over DPOP. First, it uses messages whose size only grows linearly with the treewidth of the problem. Second, by letting agents explore values in a nonincreasing order of preference, it saves a significant amount of messages and computation over the basic DPOP algorithm. To show the merits of our approach, we report on experiments with practically sized distributed meeting scheduling problems in a multiagent system. 1
Dynamic Ordering for Asynchronous Backtracking on DisCSPs
, 2006
"... An algorithm that performs asynchronous backtracking on distributed CSPs, with dynamic ordering of agents is proposed, ABT DO. Agents propose reorderings of lower priority agents and send these proposals whenever they send assignment messages. Changes of ordering triggers a different computation of ..."
Abstract

Cited by 30 (8 self)
 Add to MetaCart
(Show Context)
An algorithm that performs asynchronous backtracking on distributed CSPs, with dynamic ordering of agents is proposed, ABT DO. Agents propose reorderings of lower priority agents and send these proposals whenever they send assignment messages. Changes of ordering triggers a different computation of Nogoods. The dynamic ordered asynchronous backtracking algorithm uses polynomial space, similarly to standard ABT. TheABT DO algorithm with three different ordering heuristics is compared to standard ABT on randomly generated DisCSPs. A Nogoodtriggered heuristic, inspired by dynamic backtracking, is found to outperform static order ABT by a large factor in runtime and improve the network load.
Distributed Constraint Satisfaction in a Wireless Sensor Tracking System
, 2001
"... This paper describes our ongoing work on an interesting distributed constraint satisfaction problem (DCSP), SensorCSP, that is based on a system of wireless sensors tracking multiple mobile nodes. We present some preliminary results showing that the source of combinatorial complexity in this problem ..."
Abstract

Cited by 23 (6 self)
 Add to MetaCart
(Show Context)
This paper describes our ongoing work on an interesting distributed constraint satisfaction problem (DCSP), SensorCSP, that is based on a system of wireless sensors tracking multiple mobile nodes. We present some preliminary results showing that the source of combinatorial complexity in this problem is closely linked to the level of communication in the system. This DCSP lends itself naturally to two models  one in which agents are associated with the sensors, and one in which agents are associated with the mobile nodes. We show that these models are duals of each other, and discuss how they di#er in the number of intra and interagent constraints and how this might a#ect the cost of finding a distributed solution. We also suggest that a careful distinction must be made between explicit and implicit interagent constraints in this problem domain as this might a#ect the communication costs and the scalability of a distributed solution. 1