1. Survey of AI for games

2. AI vs. AI for games Traditional AI: – Made to handle unseen inputs, large state space – Too many options possible to compute an exact optimal solution – Engineering criteria: best possible performance Game AI: – The game world is known, though it can still be large – In a known world, optimal solutions can be precomputed – Entertainment criteria: smart enough to pose a challenge, but not smart enough to be undefeatable

3. Game AI related courses CSE 3902 Project: Design, Development, and Documentation of Interactive Systems – State machines CSE 3541 Computer Game and Animation Techniques – Agents and 3D spatial movement CSE 3521 Survey of Artificial Intelligence I: Basic Techniques – Search, logic, knowledge representation CSE 5522 Survey of Artificial Intelligence II – Probabilities and research topics CSE 5524 Computer Vision for Human-Computer Interaction – Computing with images as input

4. Adding AI into a Game Friend – Autonomous, intelligent NPC helpmates – Configurable (scripted) behaviors: different characters solve a problem in different ways – Player may trade places with NPC: automation Foe – Opponents get better with time – Opponents are less predictable because the individuals’ behavior is not uniform Scene Clutter – Provides a richness to your environment. – Animals grazing, birds flying, people milling about, automobiles driving, etc.

5. Goal-driven behavior Multiple steps required to achieve a desired effect Useful in – Action-adventure type games - puzzles to solve – RPG - task underlings with a multi-step job Good discussion in Programming Game AI by Example Ch. 9 – Each ‘goal’ is an instance of a composite class – Many different goals can be created with minimal coding

6. Goal-driven agents Different classes of agents solve problem in different ways, based on their abilities – “Block door”: Strong trolls move boulders in the way Small hobbits shovel sand into the opening – Each agent’s response to the goal depends on his abilities, available tools, etc. – Goal object contains alternative recipes – One goal at a time is active for each agent In more complex games, might have goal queue

7. Other AI principles: observability Don’t let the agents have perfect knowledge: they have to operate in the environment like the players do – Sense and remember events in their sensory horizon, memories can have a timestamp – Perhaps in more advanced levels, they can communicate with each other about what they know See article on “adding stupidity to AI”

8. Example Problem: Tic-Tac-Toe X X O

9. Example Problem: Natural language processing “Time flies like an arrow” Grammatically valid interpretations: – 1. time passes quickly like an arrow – 2. command: time the flies the way an arrow times the flies – 3. command: only time those flies which are like an arrow – 4. “time-flies” are fond of an arrow