In the present work we study evolution of both high-level and low-level behaviour of agents in the environment of the commercial game Unreal Tournament 2004. For optimization of high-level behaviour in Deathmatch and Capture the flag game modes a new functional architecture for description of player's behaviour was designed and implemented. Then a genetic programming technique was used to optimise it. Experiments with both standard evolution schema and with coevolution are presented. In second series of experiments the NEAT algo- rithm was used to evolve low-level missile avoidance behaviour (so called "dodging")
It is widely accepted that the difficulty and expense involved in acquiring the knowledge behind tac...
We demonstrate how a first-person shooter (FPS) video game can be made more fun and challenging by ...
In this paper we discuss the need to extend the standard types of character behaviours found in game...
This thesis deals with agent behavior evolution for the environment of a real computer game using ev...
AbstractThe aim of this paper is to attest the improvement on strategies of intelligent adaptive age...
In this paper, we describe an architecture for evolving tactics for teams of agents in single-player...
This paper presents an interactive genetic algorithm for generating a human-like autonomous player ...
This paper defends the use of evolutionary algorithms to generate (and evolve) strategies that manag...
The aim of this thesis is to explore the possibility of using Genetic Programming to create agents t...
Many training simulations can benefit from increased levels of reality obtained through the use of i...
Automated Game Programming is an attempt to conjure computer game-playing agents from random bits th...
This paper describes the EvoTanks research project, a continuing attempt to develop strong AI player...
This paper describes the EvoTanks research project, a continuing attempt to develop strong AI player...
Genetic programming is a powerful methodology for automatically producing solutions to problems in a...
This paper uses genetic programming (GP) to evolve a variety of reactive agents for a simulated vers...
It is widely accepted that the difficulty and expense involved in acquiring the knowledge behind tac...
We demonstrate how a first-person shooter (FPS) video game can be made more fun and challenging by ...
In this paper we discuss the need to extend the standard types of character behaviours found in game...
This thesis deals with agent behavior evolution for the environment of a real computer game using ev...
AbstractThe aim of this paper is to attest the improvement on strategies of intelligent adaptive age...
In this paper, we describe an architecture for evolving tactics for teams of agents in single-player...
This paper presents an interactive genetic algorithm for generating a human-like autonomous player ...
This paper defends the use of evolutionary algorithms to generate (and evolve) strategies that manag...
The aim of this thesis is to explore the possibility of using Genetic Programming to create agents t...
Many training simulations can benefit from increased levels of reality obtained through the use of i...
Automated Game Programming is an attempt to conjure computer game-playing agents from random bits th...
This paper describes the EvoTanks research project, a continuing attempt to develop strong AI player...
This paper describes the EvoTanks research project, a continuing attempt to develop strong AI player...
Genetic programming is a powerful methodology for automatically producing solutions to problems in a...
This paper uses genetic programming (GP) to evolve a variety of reactive agents for a simulated vers...
It is widely accepted that the difficulty and expense involved in acquiring the knowledge behind tac...
We demonstrate how a first-person shooter (FPS) video game can be made more fun and challenging by ...
In this paper we discuss the need to extend the standard types of character behaviours found in game...