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56
DCOPs meet the real world: Exploring unknown reward matrices with applications to mobile sensor networks
, 2009
"... Abstract Buoyed by recent successes in the area of distributed constraint optimization problems (DCOPs), this paper addresses challenges faced when applying DCOPs to realworld domains. Three fundamental challenges must be addressed for a class of realworld domains, requiring novel DCOP algorithms ..."
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Cited by 26 (6 self)
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Abstract Buoyed by recent successes in the area of distributed constraint optimization problems (DCOPs), this paper addresses challenges faced when applying DCOPs to realworld domains. Three fundamental challenges must be addressed for a class of realworld domains, requiring novel DCOP algorithms. First, agents may not know the payoff matrix and must explore the environment to determine rewards associated with variable settings. Second, agents may need to maximize total accumulated reward rather than instantaneous final reward. Third, limited time horizons disallow exhaustive exploration of the environment. We propose and implement a set of novel algorithms that combine decisiontheoretic exploration approaches with DCOPmandated coordination. In addition to simulation results, we implement these algorithms on robots, deploying DCOPs on a distributed mobile sensor network.
Scaling Up Multiagent Planning: A BestResponse Approach
 In Procs. ICAPS 2011
, 2011
"... Multiagent planning is computationally hard in the general case due to the exponential blowup in the action space induced by concurrent action of different agents. At the same time, many scenarios require the computation of plans that are strategically meaningful for selfinterested agents, in order ..."
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Cited by 19 (2 self)
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Multiagent planning is computationally hard in the general case due to the exponential blowup in the action space induced by concurrent action of different agents. At the same time, many scenarios require the computation of plans that are strategically meaningful for selfinterested agents, in order to ensure that there would be sufficient incentives for those agents to participate in a joint plan. In this paper, we present a multiagent planning and plan improvement method that is based on conducting iterative bestresponse planning using standard singleagent planning algorithms. In constrained types of planning scenarios that correspond to congestion games, this is guaranteed to converge to a plan that is a Nash equilibrium with regard to agents ’ preference profiles over the entire plan space. Our empirical evaluation beyond these restricted scenarios shows, however, that the algorithm has much broader applicability as a method for plan improvement in general multiagent planning problems. Extensive empirical experiments in various domains illustrate the scalability of our method in both cases.
On Koptimal distributed constraint optimization algorithms: new bounds and algorithms
 In AAMAS ’08
, 2008
"... Distributed constraint optimization (DCOP) is a promising approach to coordination, scheduling and task allocation in multi agent networks. In largescale or lowbandwidth networks, finding the global optimum is often impractical. Koptimality is a promising new approach: for the first time it provi ..."
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Cited by 16 (7 self)
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Distributed constraint optimization (DCOP) is a promising approach to coordination, scheduling and task allocation in multi agent networks. In largescale or lowbandwidth networks, finding the global optimum is often impractical. Koptimality is a promising new approach: for the first time it provides us a set of locally optimal algorithms with quality guarantees as a fraction of global optimum. Unfortunately, previous work in koptimality did not address domains where we may have prior knowledge of reward structure; and it failed to provide quality guarantees or algorithms for domains with hard constraints (such as agents ’ local resource constraints). This paper addresses these shortcomings with three key contributions. It provides: (i) improved lowerbounds on koptima quality incorporating available prior knowledge of reward structure; (ii) lower bounds on koptima quality for problems with hard constraints; and (iii) koptimal algorithms for solving DCOPs with hard constraints and detailed experimental results on largescale networks.
Solution sets for DCOPs and graphical games
 In AAMAS
, 2006
"... A distributed constraint optimization problem (DCOP) is a formalism that captures the rewards and costs of local interactions within a team of agents, each of whom is choosing an individual action. When rapidly selecting a single joint action for a team, we typically solve DCOPs (often using locally ..."
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Cited by 12 (8 self)
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A distributed constraint optimization problem (DCOP) is a formalism that captures the rewards and costs of local interactions within a team of agents, each of whom is choosing an individual action. When rapidly selecting a single joint action for a team, we typically solve DCOPs (often using locally optimal algorithms) to generate a single solution. However, in scenarios where a set of joint actions (i.e. a set of assignments to a DCOP) is to be generated, metrics are needed to help appropriately select this set and efficiently allocate resources for the joint actions in the set. To address this need, we introduce koptimality, a metric that captures the desirable properties of diversity and relative quality of a set of locallyoptimal solutions using a parameter that can be tuned based on the level of these properties required. To achieve effective resource allocation for this set, we introduce several upper bounds on the cardinalities of koptimal joint action sets. These bounds are computable in constant time if we ignore the graph structure, but tighter, graphbased bounds are feasible with higher computation cost. Bounds help choose the appropriate level of koptimality for settings with fixed resources and help determine appropriate resource allocation for settings where a fixed level of koptimality is desired. In addition, our bounds for a 1optimal joint action set for a DCOP also apply to the number of purestrategy Nash equilibria in a graphical game of noncooperative agents.
Logic Programming for Multiagent Planning with Negotiation
"... Abstract. Multiagent planning deals with the problem of generating plans for multiple agents. It requires formalizing ways for the agents to interact and cooperate, in order to achieve their goals. One way for the agents to interact is through negotiations. Integration of negotiation in multiagent p ..."
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Cited by 12 (5 self)
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Abstract. Multiagent planning deals with the problem of generating plans for multiple agents. It requires formalizing ways for the agents to interact and cooperate, in order to achieve their goals. One way for the agents to interact is through negotiations. Integration of negotiation in multiagent planning has not been extensively investigated and a systematic way for this task has yet to be found. We develop a generic model for negotiation in dynamic environments and apply it to generate jointplans with negotiation for multiple agents. We identify the minimal requirements for such a model and propose a general scheme for onetoone negotiations. This model of negotiation is instantiated to deal with dynamic knowledge of planning agents. We demonstrate how logic programming can be employed as a uniform platform to support both planning and negotiation, providing an ideal testbed for experimenting with multiagent planning with negotiations. 1
Multiplyconstrained distributed constraint optimization
 In AAMAS
, 2006
"... Distributed constraint optimization (DCOP) has emerged as a useful technique for multiagent coordination. While previous DCOP work focuses on optimizing a single team objective, in many domains, agents must satisfy additional constraints on resources consumed locally (due to interactions within thei ..."
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Cited by 9 (1 self)
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Distributed constraint optimization (DCOP) has emerged as a useful technique for multiagent coordination. While previous DCOP work focuses on optimizing a single team objective, in many domains, agents must satisfy additional constraints on resources consumed locally (due to interactions within their local neighborhoods). Such resource constraints may be required to be private or shared for efficiency’s sake. This paper provides a novel multiplyconstrained DCOP algorithm for addressing these domains which is based on mutuallyintervening search, i.e. using local resource constraints to intervene in the search for the optimal solution and vice versa. It is realized through three key ideas: (i) transforming nary constraints to maintain privacy; (ii) dynamically setting upper bounds on joint resource consumption with neighbors; and (iii) identifying if the local DCOP graph structure allows agents to compute exact resource bounds for additional efficiency. These ideas are implemented by modifying Adopt, one of the most efficient DCOP algorithms. Both detailed experimental results as well as proofs of correctness are presented.
A distributed control loop for autonomous recovery in a multiagent plan. In:
 Proc. of the 21st IJCAI
, 2009
"... Abstract This paper considers the execution of a MultiAgent Plan in a partially observable environment, and faces the problem of recovering from action failures. The paper formalizes a local plan repair strategy, where each agent in the system is responsible for controlling (monitoring and diagnosi ..."
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Cited by 8 (1 self)
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Abstract This paper considers the execution of a MultiAgent Plan in a partially observable environment, and faces the problem of recovering from action failures. The paper formalizes a local plan repair strategy, where each agent in the system is responsible for controlling (monitoring and diagnosing) the actions it executes, and for autonomously repairing its own plan when an action failure is detected. The paper describes also how to mitigate the impact of an action failure on the plans of other agents when the local recovery strategy fails.
Solving multiagent networks using distributed constraint optimization
, 2007
"... In many cooperative multiagent domains, the effect of local interactions between agents can be compactly represented as a network structure. Given that agents are spread across such a network, agents directly interact only with a small group of neighbors. A distributed constraint optimization proble ..."
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Cited by 7 (5 self)
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In many cooperative multiagent domains, the effect of local interactions between agents can be compactly represented as a network structure. Given that agents are spread across such a network, agents directly interact only with a small group of neighbors. A distributed constraint optimization problem (DCOP) is a useful framework to reason about such networks of agents. Given agents’ inability to communicate and collaborate in large groups in such networks, we focus on an approach called koptimality for solving DCOPs. In this approach, agents form groups of one or more agents until no group of k or fewer agents can possibly improve the DCOP solution; we define this type of local optimum, and any algorithm guaranteed to reach such a local optimum, as koptimal. The article provides an overview of three key results related to koptimality. The first set of results are worstcase guarantees on the solution quality of koptima in a DCOP. These guarantees can help determine an appropriate koptimal algorithm, or possibly an appropriate constraint graph structure, for agents to use in situations where the cost of coordination between agents must be weighed against the quality of the solution reached. The second set of results are upper bounds on the number of koptima that can exist in a DCOP. These results are useful in domains where a DCOP must generate a set of solutions rather than single solution. Finally, we sketch algorithms for koptimality and provide some experimental results for 1, 2 and 3optimal algorithms for several types of DCOPs.