Evolutionary algorithm for game difficulty control

  • Authors:

  • Sang-Won Um;Jong-Soo Choi;Jin-Tae Kim;Ho-Keun Song;Hasung Koo

  • Affiliations:

  • Department of Image Engineering, GSAIM, Chung-Ang University, Korea;Department of Image Engineering, GSAIM, Chung-Ang University, Korea;Department of Computer & Information Science, Hanseo University, Korea;Department of Image Engineering, GSAIM, Chung-Ang University, Korea and Department of Computer & Information Science, Hanseo University, Korea;Department of Computer & Information Science, Hanseo University, Korea

  • Venue:
  • CIMMACS'05 Proceedings of the 4th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
  • Year:
  • 2005

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Abstract

In this paper we propose an evolutionary algorithm against player (EAP) for game that controls the difficulty of a game based on the player's propensity and proficiency fundamental using the Genetic Algorithm (GA). This paper describes how we use the GA to control the level of difficulty in a game based on a user's skill. Most game AI techniques so far have been focused on the realistic and smart behavior of game units or game appearance. It a player competes with exciting opponents in a game, game AI is involved in not game appearance or game environments but exciting opponents. AI techniques make game-play richly, but unfortunately they have rarely been used in games. We suggest a game algorithm that enables a game to change the difficulties by itself based on the player's suitability to the game using the GA.