Game AI versus AI: An Introduction to AI Game Programming

1. Game AI versus AI: An Introduction to AI Game Programming Héctor Muñoz-Avila

2. My General Area of Interest • Research on AI Techniques: case-based reasoning (CBR), automated planning, and machine learning Correctness/Expressiveness of learned constructs Theory Systems Teaching Complexity of case reuse Learning hierarchical knowledge Continuous Problem solving Framework for analysis of case reuse & Retrieval Goal-Driven Autonomy plan adaptation Intelligent Decision Support Systems Reinforcement Learning. AI Game Programming

3. My General Area of Interest • Research on AI Techniques: case-based reasoning (CBR), automated planning, and machine learning Correctness/Expressiveness of learned constructs Theory Systems Teaching Complexity of case reuse Learning hierarchical knowledge Continuous Problem solving Framework for analysis of case reuse & Retrieval Goal-Driven Autonomy plan adaptation Intelligent Decision Support Systems Reinforcement Learning. AI Game Programming

4. Game AI Do you know what is attack Kung-Fu style? http://www.youtube.com/watch?v=-aTbfxwdYQo

5. Half-Life: Gordon Freeman’s First Encounter with the Marines Do they attack Kung-Fu style?

6. Half-Life Kung-Fu Attackhttp://www.youtube.com/watch?v=rZUe9FSRnd8&feature=autoplay&list=PLD0067F4CFAE431A0&lf=results_main&playnext=1 • Actually no more than 2 marines are attacking at any time • The other marines take cover, move around etc. • When one of the attacking marines run out of ammo, is wounded, dies, etc., one of the others take his place • Some reactions are hard-coded and scenario-dependent

7. Game AI • Term refers to the algorithms controlling: • The computer-controlled units/opponents • Gaming conditions (e.g., weather) • Path finding • Attack Kung-Fu style is an example of game AI for the computer opponent • Programming intentional mistakes is also part of controlling the computer opponent “AI”

8. Programming “Good” AI Opponent(according to Lars Liden; Ch. 2; AIWS1) • Enemies move before firing • Make mob/enemy visible • Announce enemy presence by sound or other means http://www.youtube.com/watch?v=vL8YfqyU4Fo&feature=related

9. Challenge • Games have increasingly larger content • E.g., Elders Scrolls V: Skyrim: It will take an avatar months to walk the complete game environment • “dirty” little secret: frequently they cheat! • Any fans of the Civilization game series? • Making good Game AI is difficult • Players are sometimes willing to forgive obvious short-comings (because of “Lusory Attitude”) • In non-gaming context (e.g., simulation for military training) • Users will loose confidence in the simulation • Requires high-fidelity environment • including AI of NPCs

10. What is AI? Categories for definitions of AI

11. The Turing Test: Preliminaries • Designed by Alan Turing (1950) • The Turing test provides a satisfactory operational definition of AI • It’s a behavioral test (i.e., test if a system acts like a human) • Problem: it is difficult to make a mathematical analysis of it

12. ? machine ? machine The Turing Test The Turing Test: a computer is programmed well enough to have a conversation with an interrogator (for example through a computer terminal) and passes the test if the interrogator cannot discern if there is a computer or a human at the other end

13. The Turing Test vs. AI Fields For a program to pass the Turing Test, it needs to pass the exhibit the following capabilities: • Natural language processing • Knowledge representation • Automated reasoning • Machine learning

14. Loebner Prize • Each year (since 1994) a competition is made to see if a computer passes the Turing Test • The first program to pass it will receive 100k • Controversial: Minsky offer 100 if anyone finish it • Still, it is interesting to observe capabilities • Machines seems to have come close to fulfill Turing’s prediction (5 minutes)

15. Yes/No test • No incremental goals • … Main drawbacks: Loebner Prize (II) ENTRANT: Welcome judge 4JUDGE: Hello. What's the weather outside?ENTRANT: I haven't been outside much today. I am fond of asparagus myself.JUDGE: How old are you?ENTRANT: I'm about 30.JUDGE: About?ENTRANT: I can talk about Star Trek.JUDGE: No let's talk about you. Exactly how old are you?ENTRANT: Right you are batman.

16. Other Predictions from Turing • Predicted that by the year 2000 a computer will have 30% chances to fool a person for 5 minutes • Anticipated the major arguments against AI: • The mathematical objection to AI • Argument from Informality

17. Answer: No (Turing, 1940’s: the set • {(P,I) : P will stop with an input I} is not Turing-computable) • Proof by contradiction (using a Universal Turing Machine -CSC318: Automata Theory-) The Mathematical Objection to AIThe Halting Problem • Can we write a program in a language L (i.e., java), that recognizes if any program written in that language ends with a given input?

18. The Mathematical Objection to AI • Argument against AI: a human can determine if a program ends or not • Thus, computers machines are inferior as humans • Argument against this argument: • If the brain is a deterministic device then it is a formal system like a computer is (though more complicated) • If the brain has some non deterministic aspects, then we can incorporate devices that has non deterministic behavior

19. Point of View in Our Course • These discussions refer to pros and cons of constructing a machine that behaves like a human • A wide range of techniques have been developed as a result of the interest in AI • In practice, some of these techniques have been effectively used to enhance computer games • Studying these successfully applied techniques for games and promising directions is one of the two aspects to be covered in this course • (the other aspect to be covered in this course is how Game AI is created nowadays) • We leave the discussion of whether a Game exhibit a human-like behavior or not to cognitive scientist or philosophers

20. AI: Genesis • Logical reasoning calculus was conceived (Leibniz, 17 century) • Leibiz’ motivation: solve intellectual arguments by calculation • Boolean logic (Boole, 1847) • Predicate Logic (Frege, 1879): Begriffsschrift • Incompleteness Theorem (Goedel, 1940’s)

21. AI: Some Historical Highlights • Turing’s article about what machines can do • Term AI is coined at the Dartmouth conference (1956) • General Problem Solver (Newell & Simon; 1958) • Period of great expectations

22. Early Stages, Great Expectations(what they thought they could achieve) Jenna: What were you just thinking? Data: In that particular moment, I was reconfiguring the warp field parameters, analyzing the collected works of Charles Dickens, calculating the maximum pressure I could safely apply to your lips, considering a new food supplement for Spot... Jenna: I'm glad I was in there somewhere. (from In Theory episode)

23. AI: Some Historical Highlights (cont’d) • Perceptrons: limits to neural networks (Minksy and Papert; 1969) • Knowledge-based systems (1970’s) • AI becomes an industry. Early successes of Expert systems

24. AI: Some Historical Highlights (cont’d) • It becomes clear that expert systems are hard to create (problem known as the Knowledge Acquisition bottle-neck) • Renaissance of neural networks as connectionism • 1990’s: more consolidated approaches to AI, more realistic expectations, fielded applications: • machine learning to data-mining • various AI techniques to computer games • prediction

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

26. Some AI Topics Some of which have been applied in commercial computer games • Search • Planning • Natural language processing • Machine learning • Case-based reasoning • Robotics • Computer vision • Neural networks

27. Game AI Topics • Real-Time Strategy games (Luis Villegas, Dulmovits, Alexander J.) • Squad tactics (John Formica, Matt Mitchell, David Pennenga ) • Scripting languages () • First Person Shooters (Michael Caffrey, Shamus Field, Jon Hardy) • Racing games () • Role Playing Games (Josh Westbrook, Ethan Harman) • Story line, drama () • Sports Games (Daniel Phillips, Dan Ruthrauff) • Individual NPC behavior (Daniel Phang, Sui Ying Teoh) • Player Modeling () • Map, World, Wall generation  () • You have until Tuesday next week to form groups for programming projects/presentations. Groups of 2-3 people • First come, first serve basis. Email at: hem4