Computational theory techniques in interactive video games

Video games began with “Tennis for Two” in 1958 In the 1970’s Atari produced the console game Pong Pac Man was released in 1980 Since then the video game industry has grown to a 20+ billion dollar a year industry (Dec 2008, World of Warcraft hits 11.5 million subscriptions)

Today’s games have become increasingly complex, engaging and intelligent Computer controlled Artificial Intelligence has had to evolve in order for this to happen What is “Artificial Intelligence” or “AI” in terms of a video game?

Academic AI vs. Game AI Academic AI is trying to create a real intelligence through artificial means Game AI seeks to be believable and fun Academic AI focuses on solving a problem optimally Game AI is actually written to be sub-optimal (Who wants to play a game that is impossible to win?)

Goals of Game AI then are: Simulate intelligent behavior Provide a believable challenge (that can then be overcome by the player) Be fun! Two areas that deal directly with computational theory Movement – The A* Algorithm Behavior – Finite State Machines No →

The A* algorithm NPC (Non-Player Characters) need to be able to move through their environment in an intelligent way i.e. don’t get stuck on a tree, take the shortest path to their destination Highly limited computational resources

The Search Area Divide the search area into a square grid This method uses a 2-dimensional array Record the status of each square as walkable or un-walkable Path is found by figuring out which squares we should take to get from A to B

Starting the Search Begin at A, add it to an “open list”. This is a list of squares that need to be checked Find all reachable squares adjacent to the start point, ignore un-walkable squares. Add each to open list and save point A as its “parent square” Drop A from open list and add to closed list

Choose adjacent square on the open list and repeat Which square to choose? – Lowest F cost Path Scoring F = G + H G = cost to move from A to a given square following the path to get there ex. 10 for ↕ or ↔, 14 for diagonal H = estimated cost to move from a given square to the final destination

H is an estimate that can be calculated several ways. This example uses the “Manhattan Method” which calculates the total number of squares moved horizontally or vertically to reach the target square and multiplies that number by 10 (G value)

Choose the lowest F score square from the open list Drop chosen square from open list and add to closed Check adjacent squares, add squares to open list if they are not there already. Make selected square the parent of new squares If adjacent square is already on open list, re- compute G value to see if it is lower. If it is, re-compute F

Once the target square is added to the closed list we are ready to determine the path Just have to start at the red target square and work backwards moving from one square to its parent following the arrows

Finite State Machine Provide illusion of intelligence Powerful tool for modeling NPC behavior Quick to code Easy to debug Little computational overhead Intuitive Very flexable

The ghosts in Pac-Man could easily be implemented as a finite state machine Evade state common to all ghosts Chase state unique to each ghost i.e. One takes all lefts, one rights, one goes straight, and the last heads for the player Player eating a power pill is the transition from the Chase state to the Evade state. A simple timer running down is used to trigger the transition back

Code example: enum StateType{state_RunAway, state_Patrol, state_Attack}; void Agent::UpdateState(StateType CurrentState) { switch(CurrentState) { case state_RunAway: EvadeEnemy(); if (Safe()) { ChangeState(state_Patrol); } break; case state_Patrol: FollowPatrolPath(); if (Threatened()) { if (StrongerThanEnemy()) { ChangeState(state_Attack); } else { ChangeState(state_RunAway); } break; case state_Attack: if (WeakerThanEnemy()) { ChangeState(state_RunAway); } else { BashEnemyOverHead(); } break; }//end switch }

FSM’s can easily be created for more complex behavior

Questions

References Buckland, Mat. Programming game AI by example. Wordware, Print. Funge, John. Artificial intelligence for computer games. A K Peters, Ltd., Print. Kehoe, Donald. "Designing Artificial Intelligence for Games (Part 1)." Intel Software Network (2009): n. pag. Web. 29 Apr Lester, Patrick. "A* Pathfinding for Beginners." Policy Almanac. N.p., Jul Web. 29 Apr Nareyek, Alexander. "AI in Computer Games." Queue (2004): Web. 29 Apr