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Game AI versus AI: An Introduction to AI Game Programming

Game AI versus AI: An Introduction to AI Game Programming

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Game AI versus AI: An Introduction to AI Game Programming

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

  2. Game AI Do you know what is attack Kung-Fu style?

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

  4. Half-Life Kung-Fu Attack • 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

  5. 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”

  6. 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

  7. Programming “Good” AI Opponent (II)(according to Lars Liden; Ch. 2) • Have horrible aim (rather than doing less damage) • Miss the first time • Warn the player (e.g., music, sound) • Kung-Fu attacks

  8. What is AI? Categories for definitions of AI

  9. 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

  10. ? 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

  11. 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

  12. 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)

  13. 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.

  14. 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

  15. 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?

  16. 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

  17. 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

  18. 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)

  19. 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

  20. 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)

  21. 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

  22. 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: • Applications of machine learning to data-mining • Applications of various AI techniques to computer games

  23. 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

  24. 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

  25. Game AI Topics (0) Pathfinding with the A* algorithm (Joseph Siefers)(1) Squad tactics (Bryan Auslander)(2) Scripting languages (Karneeb, Justin T)(3) Real-Time Strategy games (Dave Heefner)(4) General Pathfinding: tables and navigation (Jeremy David Christman)(5) Finite State Machines in Games (Stanislav Tsanev)(6) First Person Shooters (Xavier Otero Keil)(7) Racing games (Emily Cohen)(8) Role Playing Games (Nick Moukhine)(9) Story line, drama (Brander, Gregory M,)(10) Sports Games (Chris Wojciechowski)(11) General Pathfinding: waypoints (Sarunac, Alexandar D.)(12) Individual NPC behavior (Jonathan Michael Ziegler)(13) Achieving gaming goals (Kellen J. Gillespie)(14) Player Modeling (Jim Morrison)(15) Crowd/swarm simulations (Kristen Gardner)(16) Camera control (Christopher M. Hinkey)(17) Map, World, Wall generation  (Mercer, Jonathan C.)(18) TBS (Matt Dilts)