Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Using genetic algorithms to determine near-optimal pricing, investment and operating strategies in the electric power industry
Communications of the ACM
Decision Support Systems - From information retrieval to knowledge management: enabling technologies and best practices
Genetic Algorithms and Simulated Annealing
Genetic Algorithms and Simulated Annealing
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Scheduling Planned Maintenance of the National Grid
Selected Papers from AISB Workshop on Evolutionary Computing
Optimal Bidding and Contracting Strategies in the Deregulated Electric Power Market: Part I
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 4 - Volume 4
Constraint Programming Applications in Designing Electronic Agents: An Experimental Study
HICSS '99 Proceedings of the Thirty-second Annual Hawaii International Conference on System Sciences-Volume 5 - Volume 5
Journal of Management Information Systems - Special section: Information technology and its organizational impact
Artificial agents learn policies for multi-issue negotiation
International Journal of Electronic Commerce - Special issue: Systems for computer-mediated digital commerce
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
Cooperation in multi-agent bidding
Decision Support Systems - Special issue: Formal modeling and electronic commerce
DNA 7 Revised Papers from the 7th International Workshop on DNA-Based Computers: DNA Computing
A principled approach for building and evaluating neural network classification models
Decision Support Systems
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This study explores the use of artificial agents to discover "good" pricing, investment, and operating strategies for network industries. It models the first-best pricing, investment, and operating problems for general network industries, applies this theoretical framework to the electric power industry, and uses artificial agents to obtain computational results on realistic problems. Artificial agents can discover optimal or near-optimal pricing, investment, and operating strategies when the optimal solution is known. For problems with unknown optimal solutions, they can match the "best-known solutions." The near-optimal solutions provided by artificial agents can sometimes only be tested by pushing the limits of currently available nonlinear optimization software. Artificial agents, if carefully designed and controlled, seem very promising for solving difficult problems that are intractable by traditional analytic methods, such as discovering business strategies for network industries.