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Open constraint programming
 Artifitial Intelligence
"... Constraint satisfaction and optimization problems often involve multiple participants. For example, producing an automobile involves a supply chain of many companies. Scheduling production, delivery and assembly of the different parts would best be solved as a constraint optimization problem ([35]). ..."
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Cited by 38 (5 self)
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Constraint satisfaction and optimization problems often involve multiple participants. For example, producing an automobile involves a supply chain of many companies. Scheduling production, delivery and assembly of the different parts would best be solved as a constraint optimization problem ([35]). A more familiar task for most of us is meeting scheduling: arrange a set of meetings with varying participants such that no two meetings involving the same person are scheduled at the same time, while respecting order and deadline constraints ([18, 22]). Another application that has been studied in detail is coordinating a network of distributed sensors ([2]). Such problems can of course be solved by gathering all constraints and optimization criteria into a single large CSP, and then solving this problem using a centralized algorithm. In practice there are many cases where this is not feasible, because it is impossible to bound the problem to a manageable set of variables. For example, in meeting scheduling, once two people are planning a common meeting, this meeting is potentially in conflict with many other meetings either of them are planning and whose times are decided in parallel. A centralized solver does not know beforehand
A general, fully distributed multiagent planning algorithm
 In Proceedings of AAMAS’10
, 2010
"... We present a fully distributed multiagent planning algorithm. Our methodology uses distributed constraint satisfaction to coordinate between agents, and local planning to ensure the consistency of these coordination points. To solve the distributed CSP efficiently, we must modify existing methods t ..."
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Cited by 31 (6 self)
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We present a fully distributed multiagent planning algorithm. Our methodology uses distributed constraint satisfaction to coordinate between agents, and local planning to ensure the consistency of these coordination points. To solve the distributed CSP efficiently, we must modify existing methods to take advantage of the structure of the underlying planning problem. In multiagent planning domains with limited agent interaction, our algorithm empirically shows scalability beyond state of the art centralized solvers. Our work also provides a novel, realworld setting for testing and evaluating distributed constraint satisfaction algorithms in structured domains and illustrates how existing techniques can be altered to address such structure. Categories and Subject Descriptors
PrivacyPreserving Multiagent Constraint Satisfaction
 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND ENGINEERING
, 2009
"... Constraint satisfaction has been a very successful paradigm for solving problems such as resource allocation and planning. Many of these problems pose themselves in a context involving multiple agents, and protecting privacy of information among them is often desirable. Secure multiparty computation ..."
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Cited by 15 (6 self)
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Constraint satisfaction has been a very successful paradigm for solving problems such as resource allocation and planning. Many of these problems pose themselves in a context involving multiple agents, and protecting privacy of information among them is often desirable. Secure multiparty computation (SMC) provides methods that in principle allow such computation without leaking any information. However, it does not consider the issue of keeping agents’ decisions private from one another. In this paper, we show an algorithm that uses SMC in distributed computation to satisfy this objective.
Dynamic distributed backjumping
 In Proc. 5th workshop on distributed constraints reasoning DCR04
, 2004
"... Abstract. We consider Distributed Constraint Satisfaction Problems (DisCSP) when control of variables and constraints is distributed among a set of agents. This paper presents a distributed version of the centralized BackJumping algorithm, called the Dynamic Distributed BackJumping DDBJ algorithm. ..."
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Cited by 11 (1 self)
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Abstract. We consider Distributed Constraint Satisfaction Problems (DisCSP) when control of variables and constraints is distributed among a set of agents. This paper presents a distributed version of the centralized BackJumping algorithm, called the Dynamic Distributed BackJumping DDBJ algorithm. The advantage is twofold: DDBJ inherits the strength of synchronous algorithms that enables it to easily combine with a powerful dynamic ordering of variables and values, and still it maintains some level of autonomy for the agents. Experimental results show that DDBJ outperforms the DiDB and AFC algorithms by a factor of one to two orders of magnitude on hard instances of randomly generated DisCSPs. Keywords: Search, Constraint Satisfaction, Distributed Systems, MultiAgent Systems.
Mindomain retroactive ordering for asynchronous backtracking
, 2008
"... 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 Mindomain property (Haralick and ..."
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Cited by 5 (1 self)
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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 Mindomain property (Haralick and
Completeness and Performance of the APO Algorithm
"... Asynchronous Partial Overlay (APO) is a search algorithm that uses cooperative mediation to solve Distributed Constraint Satisfaction Problems (DisCSPs). The algorithm partitions the search into different subproblems of the DisCSP. The original proof of completeness of the APO algorithm is based on ..."
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Cited by 4 (0 self)
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Asynchronous Partial Overlay (APO) is a search algorithm that uses cooperative mediation to solve Distributed Constraint Satisfaction Problems (DisCSPs). The algorithm partitions the search into different subproblems of the DisCSP. The original proof of completeness of the APO algorithm is based on the growth of the size of the subproblems. The present paper demonstrates that this expected growth of subproblems does not occur in some situations, leading to a termination problem of the algorithm. The problematic parts in the APO algorithm that interfere with its completeness are identified and necessary modifications to the algorithm that fix these problematic parts are given. The resulting version of the algorithm, Complete Asynchronous Partial Overlay (CompAPO), ensures its completeness. Formal proofs for the soundness and completeness of CompAPO are given. A detailed performance evaluation of CompAPO comparing it to other DisCSP algorithms is presented, along with an extensive experimental evaluation of the algorithm’s unique behavior. Additionally, an optimization version of the algorithm, CompOptAPO, is presented, discussed, and evaluated. 1.
Distributed Reasoning for Multiagent Simple Temporal Problems
"... This research focuses on building foundational algorithms for scheduling agents that assist people in managing their activities in environments where tempo and complex activity interdependencies outstrip people’s cognitive capacity. We address the critical challenge of reasoning over individuals ’ i ..."
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Cited by 4 (4 self)
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This research focuses on building foundational algorithms for scheduling agents that assist people in managing their activities in environments where tempo and complex activity interdependencies outstrip people’s cognitive capacity. We address the critical challenge of reasoning over individuals ’ interacting schedules to efficiently answer queries about how to meet scheduling goals while respecting individual privacy and autonomy to the extent possible. We formally define the Multiagent Simple Temporal Problem for naturally capturing and reasoning over the distributed but interconnected scheduling problems of multiple individuals. Our hypothesis is that combining bottomup and topdown approaches will lead to effective solution techniques. In our bottomup phase, an agent externalizes constraints that compactly summarize how its local subproblem affects other agents ’ subproblems, whereas in our topdown phase an agent proactively constructs and internalizes new local constraints that decouple its subproblem from others’. We confirm this hypothesis by devising distributed algorithms that calculate summaries of the joint solution space for multiagent scheduling problems, without centralizing or otherwise redistributing the problems. The distributed algorithms permit concurrent execution to achieve significant speedup over the current art and also increase the level of privacy and independence in individual agent reasoning. These algorithms are most advantageous for problems where interactions between the agents are sparse compared to the complexity of agents ’ individual problems. 1.
2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology CompAPO: A complete version of the APO Algorithm ∗
"... Asynchronous Partial Overlay (APO) is a search algorithm that uses cooperative mediation to solve Distributed Constraint Satisfaction Problems (DisCSPs). The algorithm partitions the search into different subproblems of the DisCSP. The proof of completeness of the APO algorithm is based on the growt ..."
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Asynchronous Partial Overlay (APO) is a search algorithm that uses cooperative mediation to solve Distributed Constraint Satisfaction Problems (DisCSPs). The algorithm partitions the search into different subproblems of the DisCSP. The proof of completeness of the APO algorithm is based on the growth of the size of the subproblems. We have discovered that this expected growth of groups does not occur in some situations, leading to a termination problem of the algorithm. The present paper identifies the problematic parts in the algorithm that interfere with its completeness. Some necessary modifications are given to the algorithm to fix these problematic parts. The resulting version of the algorithm, Complete Asynchronous Partial Overlay (CompAPO), ensures its completeness. Formal proofs for the soundness and completeness of CompAPO are given. 1.
Constraint Reasoning DCR’2004 In conjunction with the Tenth International Conference on Principles and Practice of Constraint Programming (CP’2004), Toronto, Canada Dynamic Distributed BackJumping
"... Abstract. We consider Distributed Constraint Satisfaction Problems (DisCSP) when control of variables and constraints is distributed among a set of agents. This paper presents a distributed version of the centralized BackJumping algorithm, called the Dynamic Distributed BackJumping DDBJ algorithm. ..."
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Abstract. We consider Distributed Constraint Satisfaction Problems (DisCSP) when control of variables and constraints is distributed among a set of agents. This paper presents a distributed version of the centralized BackJumping algorithm, called the Dynamic Distributed BackJumping DDBJ algorithm. The advantage is twofold: DDBJ inherits the strength of synchronous algorithms that enables it to easily combine with a powerful dynamic ordering of variables and values, and still it maintains some level of autonomy for the agents. Experimental results show that DDBJ outperforms the DiDB and AFC algorithms by a factor of one to two orders of magnitude on hard instances of randomly generated DisCSPs. Keywords: Search, Constraint Satisfaction, Distributed Systems, MultiAgent Systems.
The impact of wireless communication on distributed constraint satisfaction
"... Abstract. Distributed constraint satisfaction (DisCSP) models decision problems where physically distributed agents control different decision variables, but must communicate with each other to agree on a global solution. Most DisCSP research assumes an abstract communication layer based on a peer ..."
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Abstract. Distributed constraint satisfaction (DisCSP) models decision problems where physically distributed agents control different decision variables, but must communicate with each other to agree on a global solution. Most DisCSP research assumes an abstract communication layer based on a peertopeer wired network. However, many practical applications of distributed reasoning require to be implemented over wireless networks, which impose different communication costs, and may affect the performance of DisCSP algorithms. We study the impact of wireless network topology and routing on two leading DisCSP algorithms – ABT and AFCng. We introduce a new framework for experiments which models different communication layers. We show that the communication layer has a significant impact on the messaging costs, which can vary by over an order of magnitude. We also show the impact on computation time, where the equivalent nonconcurrent constraint checks can vary by a factor of 6. Finally, we show that given a fixed agent ordering, changing the communications topology can increase the number of messages by up to 50%. 1