Computational Markets to Regulate Mobile-Agent Systems

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 bid for computation from host machines. \par We represent applications as collections of mobile agents and introduce a distributed mechanism for allocating general computational priority to mobile agents. We derive a bidding strategy for an agent that plans expenditures given a budget and a series of tasks to complete. We also show that a unique Nash equilibrium exists between the agents under our allocation policy. We present simulation results to show that the use of our resource-allocation mechanism and expenditure-planning algorithm results in shorter mean job completion times compared to traditional mobile-agent resource allocation. We also observe that our resource-allocation policy adapts favorably to allocate overloaded resources to higher priority agents, and that agents are able to effectively plan expenditures even when faced with network delay and job-size estimation error

Towards Efficient Computation of Quality Bounded Solutions in POMDPs: Expected Value Approximation and Dynamic Disjunctive Beliefs

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 efficiency. To address this challenge, we present two new techniques. The first approximates in the value space to obtain solutions efficiently for a pre-specified error bound. Unlike existing techniques, our technique guarantees the resulting policy will meet this bound. Furthermore, it does not require costly computations to determine the quality loss of the policy. Our second technique prunes large tracts of belief space that are unreachable, allowing faster policy computation without any sacrifice in optimality. The combination of the two techniques, which are complementary to existing optimal policy generation algorithms, provides solutions with tight error bounds efficiently in domains where competing algorithms fail to provide such tight bounds. 1

Taking DCOP to the Real World: Efficient Complete Solutions for Distributed Event Scheduling

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 congruent DCOP formulations with binary constraints which are proven to yield the optimal solution. To approach real-world efficiency requirements, we obtain immense speedups by improving communication structure and precomputing best case bounds. Heuristics for generating better communication structures and calculating bound in a distributed manner are provided and tested on systematically developed domains for meeting scheduling and sensor networks, exemplifying the viability of complete algorithms. 1

A Game Theoretic Analysis of Agent-Mediated Resource Allocation

Developments in information technology have necessitated dynamic distributed real-time allocation of computational and network resources. We consider the use of market mechanisms to regulate a set of autonomous agents that are responsible for obtaining services. By applying game-theoretic analysis to a proportionally fair divisible auction, we show the existence of a unique Nash equilibrium in both single and multiple resource settings. Locally stable decentralized negotiation algorithms are developed for both cases. We also investigate the effects of coalition formation and show that the standard assumptions from classical cooperative game theory for determining the value of a team do not apply. Finally, we examine a larger space of mechanisms and optimize with respect to revenue generation and social welfare. This leads to the design of transparent and maximally efficient resource allocation schemes which have the minimum costs for signaling and computation.National Science Foundation / NSF CCR 00-85917 ITRAir Force Office of Scientific Research / AF DC 5-36128Ope