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133
Experimental analysis of privacy loss in dcop algorithms
 in AAMAS
, 2006
"... Abstract.Distributed Constraint Optimization (DCOP) is rapidly emerging as a prominent technique for multiagent coordination. Unfortunately, rigorous quantitative evaluations of privacy loss in DCOP algorithms have been lacking despite the fact that agent privacy is a key motivation for applying DCO ..."
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Abstract.Distributed Constraint Optimization (DCOP) is rapidly emerging as a prominent technique for multiagent coordination. Unfortunately, rigorous quantitative evaluations of privacy loss in DCOP algorithms have been lacking despite the fact that agent privacy is a key motivation for applying DCOPs in many applications. Recently, Maheswaran et al. [1, 2] introduced a framework for quantitative evaluations of privacy in DCOP algorithms, showing that early DCOP algorithms lose more privacy than purely centralized approaches and questioning the motivation for applying DCOPs. Do stateofthe art DCOP algorithms suffer from a similar shortcoming? This paper answers that question by investigating several of the most efficient DCOP algorithms, including both DPOP and ADOPT. Furthermore, while previous work investigated the impact on efficiency of distributed contraint reasoning design decisions, e.g. constraintgraph topology, asynchrony, messagecontents, this paper examines the privacy aspect of such decisions, providing an improved understanding of privacyefficiency tradeoffs. Finally, this paper augments previous work on systemwide privacy loss, by investigating inequities in individual agents ’ privacy loss. 1
Distributed Constraint Optimization with Structured Resource Constraints
"... Distributed constraint optimization (DCOP) provides a framework for coordinated decision making by a team of agents. Often, during the decision making, capacity constraints on agents ’ resource consumption must be taken into account. To address such scenarios, an extension of DCOP Resource Constrai ..."
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Distributed constraint optimization (DCOP) provides a framework for coordinated decision making by a team of agents. Often, during the decision making, capacity constraints on agents ’ resource consumption must be taken into account. To address such scenarios, an extension of DCOP Resource Constrained DCOP has been proposed. However, certain type of resources have an additional structure associated with them and exploiting it can result in more efficient algorithms than possible with a general framework. An example of these are distribution networks, where the flow of a commodity from sources to sinks is limited by the flow capacity of edges. We present a new model of structured resource constraints that exploits the acyclicity and the flow conservation property of distribution networks. We show how this model can be used in efficient algorithms for finding the optimal flow configuration in distribution networks, an essential problem in managing power distribution networks. Experiments demonstrate the efficiency and scalability of our approach on publicly available benchmarks and compare favorably against a specialized solver for this task. Our results extend significantly the effectiveness of distributed constraint optimization for practical multiagent settings.
Heterogeneous multirobot coordination with spatial and temporal constraints
 In Proc. of the National Conf. on Artificial Intelligence
, 2005
"... Existing approaches to multirobot coordination separate scheduling and task allocation, but finding the optimal schedule with joint tasks and spatial constraints requires robots to simultaneously solve the scheduling, task allocation, and path planning problems. We present a formal description of th ..."
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Existing approaches to multirobot coordination separate scheduling and task allocation, but finding the optimal schedule with joint tasks and spatial constraints requires robots to simultaneously solve the scheduling, task allocation, and path planning problems. We present a formal description of the multirobot joint task allocation problem with heterogeneous capabilities and spatial constraints and an instantiation of the problem for the search and rescue domain. We introduce a novel declarative framework for modeling the problem as a mixed integer linear programming (MILP) problem and present a centralized anytime algorithm with error bounds. We demonstrate that our algorithm can outperform standard MILP solving techniques, greedy heuristics, and a market based approach which separates scheduling and task allocation.
Distributed problem solving
 AI Magazine
, 2012
"... Broadly, distributed problem solving is a subfield withinmultiagent systems, where the focus is to enable multipleagents to work together to solve a problem. These agents are often assumed to be cooperative, that is, they are part of a team or they are selfinterested but incentives or disincentives ..."
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Cited by 17 (13 self)
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Broadly, distributed problem solving is a subfield withinmultiagent systems, where the focus is to enable multipleagents to work together to solve a problem. These agents are often assumed to be cooperative, that is, they are part of a team or they are selfinterested but incentives or disincentives have been applied such that the individual agent rewards are aligned with the team reward. We illustrate the motivations for distributed problem solving with an example. Imagine a decentralized channelallocation problem in a wireless local area network (WLAN), where each access point (agent) in the WLAN needs to allocate itself a channel to broadcast such that no two access points with overlapping broadcast regions (neighboring agents) are allocated the same channel to avoid interference. Figure 1 shows example mobile WLAN access points, where each access point is a Create robot fitted with a wireless CenGen radio card. Figure 2a shows an illustration of such a problem with three access points in a WLAN, where each oval ring represents the broadcast region of an access point. This problem can, in principle, be solved with a centralized approach by having each and every agent transmit all the relevant information, that is, the set of possible channels that the agent can allocate itself and its set of neighboring agents, to a centralized server. However, this centralized approach may incur unnecessary communication cost compared to a distrib
A family of graphicalgamebased algorithms for distributed constraint optimization problems
 In Coordination of LargeScale Multiagent Systems
, 2005
"... Summary. This paper addresses the application of distributed constraint optimization problems (DCOPs) to largescale dynamic environments. We introduce a decomposition of DCOP into a graphical game and investigate the evolution of various stochastic and deterministic algorithms. We also develop tech ..."
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Summary. This paper addresses the application of distributed constraint optimization problems (DCOPs) to largescale dynamic environments. We introduce a decomposition of DCOP into a graphical game and investigate the evolution of various stochastic and deterministic algorithms. We also develop techniques that allow for coordinated negotiation while maintaining distributed control of variables. We prove monotonicity properties of certain approaches and detail arguments about equilibrium sets that offer insight into the tradeoffs involved in leveraging efficiency and solution quality. The algorithms and ideas were tested and illustrated on several graph coloring domains. 1
Electric Elves: What went wrong and why
 AI Magazine
"... Software personal assistants continue to be a topic of significant research interest. This paper outlines some of the important lessons learned from a successfullydeployed team of personal assistant agents (Electric Elves) in an office environment. These lessons have important implications for simi ..."
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Cited by 12 (1 self)
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Software personal assistants continue to be a topic of significant research interest. This paper outlines some of the important lessons learned from a successfullydeployed team of personal assistant agents (Electric Elves) in an office environment. These lessons have important implications for similar ongoing research projects. The Electric Elves project was a team of almost a dozen personal assistant agents which were continually active for seven months. Each elf (agent) represented one person and assisted in daily activities in an actual office environment. This project led to several important observations about privacy, adjustable autonomy, and social norms in office environments. This paper outlines some of the key lessons learned and, more importantly, outlines our continued research to address some of the concerns raised.
Caching schemes for DCOP search algorithms
 In Proceedings of AAMAS
, 2009
"... Distributed Constraint Optimization (DCOP) is useful for solving agentcoordination problems. Anyspace DCOP search algorithms require only a small amount of memory but can be sped up by caching information. However, their current caching schemes do not exploit the cached information when deciding w ..."
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Cited by 10 (6 self)
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Distributed Constraint Optimization (DCOP) is useful for solving agentcoordination problems. Anyspace DCOP search algorithms require only a small amount of memory but can be sped up by caching information. However, their current caching schemes do not exploit the cached information when deciding which information to preempt from the cache when a new piece of information needs to be cached. Our contributions are threefold: (1) We frame the problem as an optimization problem. (2) We introduce three new caching schemes (MaxPriority, MaxEffort and MaxUtility) that exploit the cached information in a DCOPspecific way. (3) We evaluate how the resulting speed up depends on the search strategy of the DCOP search algorithm. Our experimental results show that, on all tested DCOP problem classes, our MaxEffort and MaxUtility schemes speed up ADOPT (which uses bestfirst search) more than the other tested caching schemes, while our MaxPriority scheme speeds up BnBADOPT (which uses depthfirst branchandbound search) at least as much as the other tested caching schemes.
Directed Soft Arc Consistency in Pseudo Trees
"... We propose an efficient method that applies directed soft arc consistency to a Distributed Constraint Optimization Problem (DCOP) which is a fundamental framework of multiagent systems. With DCOPs a multiagent system is represented as a set of variables and a set of constraints/cost functions. We ..."
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We propose an efficient method that applies directed soft arc consistency to a Distributed Constraint Optimization Problem (DCOP) which is a fundamental framework of multiagent systems. With DCOPs a multiagent system is represented as a set of variables and a set of constraints/cost functions. We focus on DCOP solvers that employ pseudotrees. A pseudotree is a graph structure for a constraint network that represents a partial ordering of variables. Most pseudotreebased search algorithms perform optimistic searches using explicit/implicit backtracking in parallel. However, for cost functions taking a wide range of cost values, such exact algorithms require many search iterations, even if the constraint density is relatively low. Therefore additional improvements are necessary to reduce the search process. A previous study used a dynamic programmingbased preprocessing technique that estimates the lower
Hierarchical variable ordering for multiagent agreement problems
 In AAMAS
, 2006
"... The Multiagent Agreement Problem (MAP) is a special form of Distributed Constraint Optimization (DCOP) that requires agents to choose values for variables to satisfy not only their own constraints, but also equality constraints with other agents. For solving MAPs, we introduce the AdoptMVA algorithm ..."
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Cited by 9 (0 self)
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The Multiagent Agreement Problem (MAP) is a special form of Distributed Constraint Optimization (DCOP) that requires agents to choose values for variables to satisfy not only their own constraints, but also equality constraints with other agents. For solving MAPs, we introduce the AdoptMVA algorithm which is an extension of the existing Adopt algorithm designed to take advantage of the partial centralization that exists in MAP domains where agents control multiple variables. Second, while existing solution approaches to DCOP require variables to be prioritized in some fashion in order to guarantee optimality, it is unclear how to order variables effectively when agents own multiple variables. We investigate a hierarchical approach which leverages known ordering techniques from the sequential constraint satisfaction literature by combining ordering at the agent level with orderings at the variable level to obtain efficient global orderings. Finally, we identify a promising technique for converting known effective variable orderings into effective agent orderings and identify an intraagent variable ordering heuristic for MAP that is the most efficient of the ones tested. While the contributions presented in this paper are applicable to general DCOPs, we focus our discussion on MAPs because we feel it is a significant problem class worthy of specific attention. 1.