Abstract not found

This article describes an approach to solving distributed constraint optimization problems (DCOP) with n-ary constraints. A key instance of this problem is distributed resource-constrained task scheduling, in which limited resource capacities implicitly imply n-ary relations among the start-times of the tasks. We describe ADOPT-N, an extension of ADOPT [16], a recent successful algorithm for DCOP. ADOPT-N is an optimal asynchronous distributed n-ary constraint optimization algorithm in which specific agents are empowered with n-ary constraint evaluation capabilities. We show how the algorithm’s correctness and optimality relies on (1) the choice of which agents to dedicate to constraint evaluation, and (2) an admissible (partial) variable ordering. Moreover, we demonstrate empirically how ADOPT-N’s performance depends on how much knowledge about the n-ary violations that can arise during resolution can be provided to the algorithm. 1

Conflict resolution is a critical problem in distributed and collaborative multi-agent systems. Negotiation via argumentation (NVA), where agents provide explicit arguments (justifications) for their proposals to resolve conflicts, is an effective approach to resolve conflicts. Indeed, we are applying argumentation in some real-world multi-agent applications.

The Distributed Constraint Optimization Problem (DCOP) is able to model a wide variety of real-world distributed problems in multiagent systems. Unfortunately, existing methods for DCOP are not able to provide theoretical guarantees on global solution quality while operating both eciently and asynchronously. We present a new polynomial-space algorithm, named Adopt, for DCOP that is guaranteed to find an optimal solution, or a solution within a user-specified distance from the optimal, while allowing agents to execute asynchronously and in parallel. Adopt obtains these properties via a novel distributed search strategy where agents are able to make local decisions without necessarily having global certainty. Instead, local decisions are based on conservative cost estimates which become increasingly more accurate as asynchronous messages are received from neighbors in the constraint network. A detailed theoretical analysis shows that Adopt is guaranteed to terminate with the globally optimal solution. Detailed experimental results show that Adopt provides speedups of several orders of magnitude over the only existing optimal DCOP algorithm. We also provide data on the number of messages required by Adopt to nd the optimal solution. For time-limited situations, Adopt can also perform bounded-error approximation - the ability to quickly find approximate solutions and, unlike heuristic or local search methods, still maintain a theoretical guarantee on global solution quality.