Generating Diverse Opponents with Multi-Objective Evolution

Alexandros Agapitos, Julian Togelius, Simon M. Lucas, J¨urgen Schmidhuber and Andreas Konstantinidis

Presented byPatoka Amir

Overview

Introduction Objectives Multi-objective evolutionary

algorithms Results Future work

Introduction

Easy construction of game AI (F.E.A.R. winer of GameSpot Best Artificial Intelligence)

Industry shifts focus to building interesting, divers and believable CI.

Introduction (Cont) DIFFICULTY LEVELS:

Barbarians Free Units Research Maintenance Costs Health and Happiness Artificial Intelligence Penalties AI Freebies Tribal Villages

Introduction (Cont) Predictable AI

When Priorities Go Wrong!:NPC investigates a burning barrel that was thrown by the player and landed nearby. The barrel subsequently explodes while the NPC is nearby looking at it.

Introduction (Cont)

Propose a general approach to creating diverse and interesting NPC behaviors using Multi-objective evolutionary algorithms (MOEA) in combination with a number of partly conflicting behavioral fitness measures.

Overview Introduction Objectives Multi-objective evolutionary

algorithms Results Future work

Objectives Optimize a genetically programmed car controller

to exhibit:

Aggressiveness.

Opponent weakness exploitation.

Objectives (Cont.) Environment:

A 2D simulator, modeling a radio controlled toy car (three possible drive and steering modes).

A track consisting of walls, a chain of waypoints and a set of staring points and directions (subject to random alteration).

A reasonable model of car dynamics, collisions.

A competitor (with an incrementally evolved general controller).

Objectives (Cont.) Controller employ two expression trees

representation (driving and steering) containing: Standard arithmetic and trigonometric

functions. Formal parameters representing car state as

viewed by first person sensors.

Objectives (Cont.) Behavioral fitness measures:

Absolute progress. Relative progress. Maximum speed. Progress variance. # Steering changes. # Driving changes. Wall collisions. Competitor proximity. Max Car collisions. Min Car Collisions.

Objectives (Cont.) Algorithm:

Non-Dominated Sorting Genetic Algorithem (NSGA-II).

Tournament selection (starting with size 7 during final 10 generations increases by 20% each generation).

50 generations. 500 individuals. Expression trees are limited to depth of 17 and

created with a maximum depth of 8 through Ramped-half-and-half.

Overview Introduction Objectives Multi-objective evolutionary

algorithms Results Future work

MOEANon-Dominated Sorting Genetic Algorithem Pareto frontier:

Overview

Introduction Objectives Multi-objective evolutionary

algorithms Results Future work

ResultsAggressiveness – wall collisions avoidance

Fitness = max absolute progress + min wall collisions.

ResultsAggressiveness – max speed & min steering

Fitness = max absolute progress + min wall collisions + min # steering changes.

ResultsAggressiveness – max speed & min steering

Fitness = max absolute progress + min wall collisions + min # steering changes.

Results Aggressiveness – max speed & min driving

Fitness = max absolute progress + min wall collisions + min # driving changes.

ResultsAggressiveness – max speed & min driving

Fitness = max absolute progress + min wall collisions + min # driving changes.

ResultsAggressiveness – smoothness, avoidance and low speed

Fitness = max absolute progress + min wall collisions + max # driving changes.

ResultsAggressiveness – smoothness, avoidance and low speed

Fitness = max absolute progress + min wall collisions + max # driving changes.

ResultsAggressiveness – max car collisions

Fitness = max absolute progress + max car collisions + min car closeness + min # driving changes.

ResultsAggressiveness – Car collisions

Fitness = max absolute progress + max car collisions + min car closeness + min # steering & driving changes.

ResultsAggressiveness – Opponent weakness Exploitation

Fitness = max absolute progress + max speed + min car closeness + min # steering & driving changes.

Overview Introduction Objectives Multi-objective evolutionary

algorithms Results Future work

Future work Prove concept on other game genres.

The End

Any Questions ?

The End

Thank you ;)