RECAP CSE 397/497 Topics on AI and Computer Game Programming Prerequisites: CSE 327 or CSE 340 or instructor consent Instructor consent will be immediately granted for: CSE Graduate Students CS/CSE/CSB undergraduates that will be senior on the Fall/2005 All other cases will be granted depending on the particular case Héctor Muñoz-Avila

Course Goal AI research A C B Our goal is to understand the connections and the misconceptions from both sides “AI” as game practitioners implemented it

Path-Finding A*: minimize f(n) = g(n) + h(n) (Moll) Search Representations Grid Graphs Meshes D*: dynamic A* (Hoang) Path look-up matrix Indexed path look-up matrix Area-based look-up table Precomputed Pathfinding

Navigation (Mansley) (Lindner) Qualitative Spatial Analysis Navigation data can be used beyond navigation Low level navigation Jumping, climbing Hunting the player (Mansley) Qualitative Spatial Analysis Spatial databases Terrain analysis Search layer Occupancy layer Openness layer 1 2 3 4 5 6 7 8 9

Real-Time Strategy games (Schmid) Multi-tier AI Maps and AI Control Wall Building (Lee-Urban) Random map generation AI transport Units HTN planning T

Racing Games & Sports (Misra) (Gundevia) Contention system Steering Throttle Braking Brooks subsumption architecture (Gundevia) FSM implementation of offensive and defensive states (mirror) Dead reckoning

Controlling The “AI” Opponent (1) (Raim) FSM: States, Events and Actions Stack Based FSM’s Polymorphic FSM Multi-tier FSM (Hogg) Data-driven FSM Goal: separate program control logic from FSM logic Scripted FSM Spawn D Wander ~E,~S,~D ~E Attack E,~D E ~S Chase S,~E,~D S Robocode … ( Technologies ) Advanced Flight, 4,-2, Rad, Too, 3, 4 ; Alphabet, 5, 1, nil, nil, 0, 3 ; … ( Personalities / Goals ) Caesar, Livia, 0, 1, 1, Romans, Roman, 0, 1, 1 Montezuma, Nazca, 0, 4, 0, Aztecs, Aztec, 0,-1, 1 Soldier Rifleman Officer British Soviet American German Machine Gunner HTN planning

Controlling The “AI” Opponent (2) (Hookway) Ideal AI Behavior Coordinating behavior Blackboard: Deal with obstruction Synthetic Adversaries Competence Taskability Observational fidelity Behavior variability (Grabowski ) Movement, fire coordination Hierarchy of plan tactics Finding an Available NPC Availability = (1+N)(1+O)(1+P)+(Q∞) E A B 1 50 HTN planning N = # of enemies in covering area O = # of enemies within range P = # of enemies threatening team

Controlling The “AI” Opponent (3) (Grabowski ) Goal-Oriented Action Planning Alternative to FSM Define actions Inter-relations are found dynamically (planning) Various speed-up strategies are used (Xu) HTN: Plan on level of tasks not actions HTNs can be used to encode game strategies Multi-Tier AI Explode the target Plant the bomb Get a better weapon Secure the plant site Approach to the plant site Guard the Task A resulting plan: Patrol patrolled Fight No Monster Monster in sight SI OI TI IU Wargus AI planning

Controlling the “AI” opponent (4) (Warfield) Several advantages of using scripts Modularity Entice players Main drawback: developing time for the scripting system Levels of scripting Hardcoded (typical console game) … Full modularity (Neverwinter nights) Languages Declarative Imperative

Adaptive “AI” (Janneck) (Creswell) Player modeling Simple model Model = {(attribute, value),…)} Hierarchical model Higher-level node value combination of children’s values Abstract node combination of concrete traits Issues Model complexity-time tradeoff Decouple model from game (Creswell) Decision trees are a simple representation form Decision trees can be learned automatically (ID3) One of the landmarks applications of Machine learning to Games

Adaptive “AI” (2) (Ponsen) (Chan) Limitations of machine learning Reinforcement learning to find “right” script But sometimes the problem resides in the scripts not the ordering Use evolutionary computation to improve scripts (Chan) Limitations of machine learning Information stored Pattern recognition During development time Terrain analysis Pattern recognition as optimization Pattern recognition as adaptation Dynamic environments Combat team controlled by human player b y computer A B Evolve a population (each member is a candidate solution) …

Hall of Fame

Acknowledgements Jarret Raim: 5 programming projects Marc Ponsen: last programming project All of you: Presentations were very good