Induction: processes of inference, learning, and discovery
Induction: processes of inference, learning, and discovery
Models of strategic rationality
Models of strategic rationality
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming and emergent intelligence
Advances in genetic programming
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
On automated discovery of models using genetic programming in game-theoretic contexts
HICSS '95 Proceedings of the 28th Hawaii International Conference on System Sciences
The Application of Genetic Programming in Milk Yield Prediction for Dairy Cows
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
DNA 7 Revised Papers from the 7th International Workshop on DNA-Based Computers: DNA Computing
An Internet-based negotiation server for e-commerce
The VLDB Journal — The International Journal on Very Large Data Bases
Formal aspects of electronic commerce: research issues and challenges
International Journal of Electronic Commerce - Special issue: Systems for computer-mediated digital commerce
Artificial agents learn policies for multi-issue negotiation
International Journal of Electronic Commerce - Special issue: Systems for computer-mediated digital commerce
Metadata and its impact on libraries: Book Reviews
Journal of the American Society for Information Science and Technology
Genetic Programming-Based Discovery of Ranking Functions for Effective Web Search
Journal of Management Information Systems
Artificial Agents for Discovering Business Strategies for Network Industries
International Journal of Electronic Commerce
A Method for Generation of Alternatives by Decision Support Systems
Journal of Management Information Systems
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
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The creation of mathematical, as well as qualitative (or rule-based), models is difficult, time-consuming, and expensive. Recent developments in evolutionary computation hold out the prospect that, for many problems of practical import, machine learning techniques can be used to discover useful models automatically. The prospects are particularly bright, we believe, for such automated discoveries in the context of game theory. This paper reports on a series of successful experiments in which we used a genetic programming regime to discover high-quality negotiation policies. The game-theoretic context in which we conducted these experiments-- a three-player coalitions game with sidepayments--is considerably more complex and subtle than any reported in the previous literature on machine learning applied to game theory.