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158
Multiagent Systems: Algorithmic, GameTheoretic, and Logical Foundations
, 2009
"... formatted differently than the book—and in particular has different page numbering—and has not been fully copy edited. Please treat the printed book as the definitive version. You are invited to use this electronic copy without restriction for onscreen viewing, but are requested to print it only un ..."
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Cited by 106 (11 self)
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formatted differently than the book—and in particular has different page numbering—and has not been fully copy edited. Please treat the printed book as the definitive version. You are invited to use this electronic copy without restriction for onscreen viewing, but are requested to print it only under one of the following circumstances: You live in a place that does not offer you access to the physical book; The cost of the book is prohibitive for you; You need only one or two chapters. Finally, we ask you not to link directly to the PDF or to distribute it electronically. Instead, we invite you to link to
Mdpop: Faithful distributed implementation of efficient social choice problems
 In AAMAS’06  Autonomous Agents and Multiagent Systems
, 2006
"... In the efficient social choice problem, the goal is to assign values, subject to side constraints, to a set of variables to maximize the total utility across a population of agents, where each agent has private information about its utility function. In this paper we model the social choice problem ..."
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Cited by 41 (15 self)
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In the efficient social choice problem, the goal is to assign values, subject to side constraints, to a set of variables to maximize the total utility across a population of agents, where each agent has private information about its utility function. In this paper we model the social choice problem as a distributed constraint optimization problem (DCOP), in which each agent can communicate with other agents that share an interest in one or more variables. Whereas existing DCOP algorithms can be easily manipulated by an agent, either by misreporting private information or deviating from the algorithm, we introduce MDPOP, the first DCOP algorithm that provides a faithful distributed implementation for efficient social choice. This provides a concrete example of how the methods of mechanism design can be unified with those of distributed optimization. Faithfulness ensures that no agent can benefit by unilaterally deviating from any aspect of the protocol, neither informationrevelation, computation, nor communication, and whatever the private information of other agents. We allow for payments by agents to a central bank, which is the only central authority that we require. To achieve faithfulness, we carefully integrate the VickreyClarkeGroves (VCG) mechanism with the DPOP algorithm, such that each agent is only asked to perform computation, report
Distributed constraint satisfaction and optimization with privacy enforcement
 In 3rd IC on Intelligent Agent Technology
, 2004
"... Several naturally distributed negotiation/cooperation problems with privacy requirements can be modeled within the distributed constraint satisfaction framework, where the constraints are secrets of the participants. Most of the existing techniques aim at various tradeoffs between complexity and pri ..."
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Cited by 40 (9 self)
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Several naturally distributed negotiation/cooperation problems with privacy requirements can be modeled within the distributed constraint satisfaction framework, where the constraints are secrets of the participants. Most of the existing techniques aim at various tradeoffs between complexity and privacy guarantees, while others aim to maximize privacy first [12, 7, 3, 4, 11]. In [7] we introduced a first technique allowing agents to solve distributed constraint problems (DisCSPs), without revealing anything and without trusting each other or some server. The technique we propose now is a dm times improvement for m variables of domain size d. On the negative side, the fastest versions of the new technique require storing of O(d m) big integers. From a practical point of view, we improve the privacy with which these problems can be solved, and improve the efficiency with which ⌊n−1/2⌋privacy can be achieved, while it remains inapplicable for larger problems. The technique of [7] has a simple extension to optimization for distributed weighted CSPs. However, that obvious extension leaks to everybody sensitive information concerning the quality of the computed solution. We found a way to avoid this leak, which constitutes another contribution of this paper. 1.
Planning the Project Management Way: Efficient Planning by Effective Integration of Causal and Resource Reasoning in RealPlan
 Artificial Intelligence
, 2000
"... In most realworld reasoning problems, planning and scheduling phases are loosely coupled. For example, in project planning, the user comes up with a task list and schedules it with a scheduling tool like Microsoft Project. One can view automated planning in a similar way in which there is an action ..."
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Cited by 34 (9 self)
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In most realworld reasoning problems, planning and scheduling phases are loosely coupled. For example, in project planning, the user comes up with a task list and schedules it with a scheduling tool like Microsoft Project. One can view automated planning in a similar way in which there is an action selection phase where actions are selected and ordered to reach the desired goals, and a resource allocation phase where enough resources are assigned to ensure the successful execution of the chosen actions. On the other hand, most existing automated planners studied in Artificial Intelligence do not exploit this loosecoupling and perform both action selection and resource assignment employing the same algorithm. The current work shows that the above strategy severely curtails the scaleup potential of existing state of the art planners which can be overcome by leveraging the loose coupling. Specifically, a novel planning framework called RealPlan is developed in which resource allocatio...
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 31 (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
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 ..."
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Cited by 28 (5 self)
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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
Synchronous vs asynchronous search on DisCSPs
 In Proc. EUMAS’03
, 2003
"... Abstract. Distributed constraint satisfaction problems (DisCSP s) are composed of agents, each holding its variables, that are connected by constraints to variables of other agents. There are two known measures of performance for distributed search the computational effort which represents the tota ..."
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Cited by 26 (11 self)
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Abstract. Distributed constraint satisfaction problems (DisCSP s) are composed of agents, each holding its variables, that are connected by constraints to variables of other agents. There are two known measures of performance for distributed search the computational effort which represents the total search time and the number of messages sent which represents the network load. Due to the distributed nature of the problem, the behavior of the experimental environment is extremely important. However, most experimental studies have used a perfect simulator with instantaneous message delivery. The present paper investigates two families of distributed search algorithms on DisCSPs, Synchronous and Asynchronous search. Improved versions of the two families of algorithms are presented and investigated. The performance of the algorithms of these two extended families is measured on randomly generated instances of DisCSPs. The results of the investigation are twofold. First, the delay of messages is found to deteriorate the performance of asynchronous search by a large margin. This shows that a correct (and realistic) experimental scenario is important. Second, when messages are delayed, synchronous search performs better than asynchronous search in terms of computational effort as well as in network load. It turns out that asynchronous search fails to use its multiple computing power to an advantage. 1
Bumping strategies for the multiagent agreement problem
 In Proceedings of Autonomous Agents and MultiAgent Systems, (AAMAS
, 2005
"... We introduce the Multiagent Agreement Problem (MAP) to represent a class of multiagent scheduling problems. MAP is based on the Distributed Constraint Reasoning (DCR) paradigm and requires agents to choose values for variables to satisfy not only their own constraints, but also equality constraints ..."
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Cited by 25 (0 self)
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We introduce the Multiagent Agreement Problem (MAP) to represent a class of multiagent scheduling problems. MAP is based on the Distributed Constraint Reasoning (DCR) paradigm and requires agents to choose values for variables to satisfy not only their own constraints, but also equality constraints with other agents. The goal is to represent problems in which agents must agree on scheduling decisions, for example, to agree on the start time of a meeting. We investigate a challenging class of MAP – private, incremental MAP (piMAP) in which agents do incremental scheduling of activities and there exist privacy restrictions on information exchange. We investigate a range of strategies for piMAP, called “bumping ” strategies. We empirically evaluate these strategies in the domain of calendar management where a personal assistant agent must schedule meetings on behalf of its human user. Our results show that bumping decisions based on scheduling difficulty models of other agents can significantly improve performance over simpler bumping strategies.
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 ..."
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Cited by 24 (8 self)
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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.