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Distributed Constraint Optimization (DCOP) is an elegant formalism relevant to many areas in multiagent systems, yet complete algorithms have not been pursued for real world applications due to perceived complexity. To capably capture a rich class of complex problem domains, we introduce the Distributed Multi-Event Scheduling (DiMES) framework and design(More)
This paper addresses the application of distributed constraint optimization problems (DCOPs) to large-scale 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(More)
It is critical that agents deployed in real-world settings, such as businesses, offices, universities and research laboratories, protect their individual users' privacy when interacting with other entities. Indeed, privacy is recognized as a key motivating factor in the design of several multiagent algorithms, such as in distributed constraint reasoning(More)
For agents deployed in real-world settings, such as businesses, universities and research laboratories, it is critical that agents protect their individual users' privacy when interacting with others entities. Indeed, privacy is recognized as a key motivating factor in design of several multiagent algorithms, such as distributed constraint optimization(More)
Today within the AAMAS community, we see at least four competing approaches to building multiagent systems: belief-desire-intention (BDI), distributed constraint optimization (DCOP), distributed POMDPs, and auctions or game-theoretic approaches. While there is exciting progress within each approach, there is a lack of cross-cutting research. This paper(More)
—We address the problem of devising efficient decentralized allocation mechanisms for a divisible resource, which is critical to many technological domains such as traffic management on the Internet and bandwidth allocation to agents in ad hoc wireless networks. We introduce a class of efficient signal proportional allocation (ESPA) mechanisms that yields(More)
While POMDPs (partially observable markov decision problems) are a popular computational model with wide-ranging applications, the computational cost for optimal policy generation is prohibitive. Researchers are investigating ever-more efficient algorithms, yet many applications demand such algorithms bound any loss in policy quality when chasing(More)
The successful integration and acceptance of many multi-agent systems into daily lives crucially depends on the ability to develop effective policies for adjustable autonomy. Adjustable autonomy encompasses the strategies by which an agent selects the appropriate entity (itself, a human user, or another agent) to make a decision at key moments when an(More)
This paper considers resource allocation in a network with mobile agents competing for computational priority. We formulate this problem as a multi-agent game with the players being agents purchasing service from a common server. We show that there exists a computable Nash equilibrium when agents have perfect information into the future. We simulate a(More)
Mobile-agent systems allow applications to distribute their resource consumption across the network. By prioritizing applications and publishing the cost of actions, it is possible for applications to achieve faster performance than in an environment where resources are evenly shared. We enforce the costs of actions through markets, where user applications(More)