Pareto coevolution: using performance against coevolved opponents in a game as dimensions for Pareto selection
Year: 2001
Type: conference-paper
Abstract: When using an automatic discovery method to nd a good strategy in a game, we hope to nd one that performs well against a wide variety of opponents. An appealing notion in the use of evolutionary algorithms to coevolve strategies is that the population represents a set of dierent strategies against which a player must do well. Implicit here is the idea that dierent players represent dierent \\dimensions" of the domain, and being a robust player means being good in many (preferably all) dimensions of the game. Pareto coevolution makes this idea of \\players as dimensions" explicit. By explicitly treating each player as a dimension, or objective, we may then use established multi-objective optimization techniques to nd robust strategies. In this paper, we apply Pareto coevolution to Texas Hold'em poker, a complex real-world game of imperfect information. The performance of our Pareto coevolution algorithm is compared with that of a conventional genetic algorithm and shown to be promising. 1
Authors: Jason Noble, Richard A. Watson
Institutions: University of Leeds, Brandeis University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1542968481