Planning for conjunctive goals
Artificial Intelligence
The ecology of computation
Simulated annealing: theory and applications
Simulated annealing: theory and applications
Negotiating task decomposition and allocation using partial global planning
Distributed Artificial Intelligence (Vol. 2)
Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
Spawn: A Distributed Computational Economy
IEEE Transactions on Software Engineering
Market-based control: a paradigm for distributed resource allocation
Market-based control: a paradigm for distributed resource allocation
A computational market model based on individual action
Market-based control
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The WALRAS Algorithm: A Convergent Distributed Implementation of General Equilibrium Outcomes
Computational Economics
An Economic Paradigm for Query Processing and Data Migration in Mariposa
PDIS '94 Proceedings of the Third International Conference on Parallel and Distributed Information Systems
Journal of Artificial Intelligence Research
Mechanisms for automated negotiation in state oriented domains
Journal of Artificial Intelligence Research
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The 0-1 multiple knapsack problem appears in many domains from financial portfolio management to cargo ship stowing. Algorithms for solving it range from approximate, with no lower bounds on performance, to exact, which suffer from worst case exponential time and space complexities. This paper introduces a market model based on agent decomposition and market auctions for approximating the 0-1 multiple knapsack problem, and an algorithm that implements the model (M(x)). M(x) traverses the solution space, much like simulated annealing, overcoming an inherent problem of many greedy algorithms. The use of agents ensures infeasible solutions are not considered while traversing the solution space and traversal of the solution space is both random and directed. M(x) is compared to a bound and bound algorithm and a simple greedy algorithm with a random shuffle. The results suggest M(x) is a good algorithm for approximating the 0-1 Multiple Knapsack problem.