CONTROLLING BOTS IN A FIRST-PERSON SHOOTER GAME USING GENETIC ALGORITHMS

Anna I Esparcia-Alcázar1, Anaís Martínez-García1, Antonio M. Mora2, J.J. Merelo2, Pablo García-Sánchez2

1Instituto Tecnológico de Informática - Universidad Politécnica de Valencia, Spain2Dept. of Architecture and Computer Technology - University of Granada, Spain

THE ENGINE

Unreal Tournament 2004

• A First-Person Shooter (FPS) game

• Bots controlled by very advanced AI

• Use of open language and environment

• Several kinds of games → we focus on DeathMatch

AI IN UT2004

Each bot is controlled by a Fuzzy Finite State Machine (FFSM), for which

• Transitions depend on:

• Bot personality, e.g. aggressiveness.

• Environment situation, such as distance to enemy.

• Bot condition, for example, health

• States represent situations and objectives. For instance, Attacking or Hunting

EVOLVING BOTS

We use a GA to evolve the values that influence transitions in the FFSM

A chromosome consists of 56 genes.

For evaluation, the bot controlled by each chromosome is given 7 lives.

We test four fitness functions:

RESULTS & CONCLUSIONS

• Great differences between evolution stage and at play time.

• Incorporating the lifespan into the fitness provides better results.

• Aiming for only one objective does not produce interesting behaviours.

• MultiBots develop a picking behaviour, which they are not aimed for.

NoHNESProject(ref. TIN2007-68083-C02)

Partially funded by:

OBJECTIVE To evolve interesting bot behaviours using simple fitness functions

KillerBot maximise kills

PickerBotmaximise items collected

MultiBotcombination of kills

and lifespan (*)

(*) using NSGAII

SurvivorBot maximise lifespan

FUTURE WORK

• Develop teams of bots, with cooperating behaviours.

• Imitate humans!