Approximating the independence number via the j -function
Mathematical Programming: Series A and B
Graphical Models for Game Theory
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Coding Theory: A First Course
Solving Distributed Constraint Optimization Problems Using Cooperative Mediation
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
A distributed framework for solving the Multiagent Plan Coordination Problem
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Adopt: asynchronous distributed constraint optimization with quality guarantees
Artificial Intelligence - Special issue: Distributed constraint satisfaction
The DEFACTO system: training tool for incident commanders
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
A scalable method for multiagent constraint optimization
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
On k-optimal distributed constraint optimization algorithms: new bounds and algorithms
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Centralized, distributed or something else? making timely decisions in multi-agent systems
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Quality guarantees on k-optimal solutions for distributed constraint optimization problems
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Balancing local resources and global goals in multiply-constrained DCOP
Multiagent and Grid Systems
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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 k-optimality, a metric that captures the desirable properties of diversity and relative quality of a set of locally-optimal 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 k-optimal joint action sets. These bounds are computable in constant time if we ignore the graph structure, but tighter, graph-based bounds are feasible with higher computation cost. Bounds help choose the appropriate level of k-optimality for settings with fixed resources and help determine appropriate resource allocation for settings where a fixed level of k-optimality is desired. In addition, our bounds for a 1-optimal joint action set for a DCOP also apply to the number of pure-strategy Nash equilibria in a graphical game of noncooperative agents.