Artificial Intelligence in Computer Game. #1 What is Intelligence? #2 Current Frontier/Challenges/Examples #3 Which method do computer think? #4 General Problem in AI #5 Game AI #6 more Game AI Examples. #1 What is Intelligence ?. Intelligence: — “the capacity to learn and solve problems”
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Artificial Intelligence in Computer Game • #1 What is Intelligence? • #2 Current Frontier/Challenges/Examples • #3 Which method do computer think? • #4 General Problem in AI • #5 Game AI • #6 more Game AI Examples
#1 What is Intelligence ? Intelligence: — “the capacity to learn and solve problems” (Webster dictionary) — the ability to think and act rationally Goal in Artificial Intelligence: — build and understand intelligent systems/agents — synergy between • philosophy, psychology, and cognitive science • computer science and engineering • mathematics and physics source: Thorsten Joachims [firstname.lastname@example.org],.
What’s involved in Intelligence? A) Ability to interact with the real world — to perceive, understand, and act — speech recognition, understanding, and synthesis — image understanding (computer vision) B) Reasoning and Planning — modelling the external world — problem solving, planning, and decision making — ability to deal with unexpected problems, uncertainties C) Learning and Adaptation — we are continuously learning and adapting Also: we want systems that adapt to us! — Major thrust of industry research.
What is Artificial Intelligence ? Artificial Intelligence [AI] ปัญญาประดิษฐ์ Rich and Knight: the study of how to make computers do things which, at the moment, people do better. Handbook of AI: the part of computer science concerned with designing intelligent computer systems, that is, systems that exhibit the characteristics we associate with intelligence in human behavior – understanding language, learning, reasoning, solving problems, etc. Dean, Allen and Aloimonos: the design and study of the computer programs that behave intelligently. Russell and Norvig: the study of [rational] agents that exist in an environment and perceive and act.
Goals in AI engineering goal To solve real-world problems. Build systems that exhibit intelligent behavior. scientific goal To understand what kind of computational mechanisms are needed for modeling intelligent behavior.
Classic AI Examples • Chinese Room • Turing Test • Deep Blue • Eliza
Chinese Room "Nobody just looking at my answers can tell that I don't speak a word of Chinese," he writes. The Chinese Room argument comprises a thought experiment and associated arguments by John Searle (Searle 1980), who attempts to show that a symbol-processing machine like a computer can never be properly described as having a "mind" or "understanding", regardless of how intelligently it may behave. http://en.wikipedia.org/wiki/Chinese_room
Turing Test Interrogator asks questions of two “people” who are out of sight and hearing. One is a person; the other is a machine. • 30 minutes to ask whatever he or she wants. • Task: to determine, only through the questions and answers typed into a computer terminal, which is which. • If can’t reliably distinguish the human from the computer, then the computer is deemed intelligent. Artificial intelligence is the enterprise of constructing an artifact that can pass the Turing test. http://en.wikipedia.org/wiki/Turing_test
Deep Blue • Feng-hsiung Hsu, Thomas Anantharaman and Murray Campbell at Carnegie Mellon University - CMU • Chiptest 50,000 moves per second • Chiptest Bug 500,000 moves per second • ChipTest-M won the North American Computer Chess Championship in 1987 • Deep Thought 0.01 was created in May 1988 720,000 moves per second • Deep Thought 0.02 won the World Computer Chess Championship with a perfect 5-0 score in 1989. • Deep Blue 200 million positions per second • Deep Fritz or Deep Junior ran on a personal computer containing two Intel Core 2 Duo CPUs, capable of evaluating only 8 million positions per second http://en.wikipedia.org/wiki/ChipTest http://en.wikipedia.org/wiki/Deep_Blue_(chess_computer)
#2 The Current Frontier Deep Blue vs. Kasparov (May, ’97) –first match won against world-champion –“intelligent & creative” play –200 million board positions per second Kasparov: “I could feel — I could smell — a new kind of intelligence across the table.” ... still understood 99.9% of Deep Blue’s moves. Intriguing issue: How does human cognition deal with the combinatorics of chess?
Eliza • http://www-ai.ijs.si/eliza/eliza.html • Chat Bot • ELIZA is a computer program by Joseph Weizenbaum, designed in 1966, which parodied a Rogerian therapist, largely by rephrasing many of the patient's statements as questions and posing them to the patient. Thus, for example, the response to "My head hurts" might be "Why do you say your head hurts?" The response to "My mother hates me" might be "Who else in your family hates you?" ELIZA was named after Eliza Doolittle, a working-class character in George Bernard Shaw's play Pygmalion, who is taught to speak with an upper classaccent.
Eliza • http://en.wikipedia.org/wiki/Image:GNU_Emacs_ELIZA_example.png
Rules and Learning Learning rules means adaption .. get reward/punishment (eg. In behavioral psychologygive reward – dog trainning experiment) http://animals.howstuffworks.com/pets/dog-training.htm Ivan Palov
#3 Which method do computer think ? : Structure The brain — a neuron is the basic processing unit, >100 Billion neurons — many more synapses connect the neurons — cycle time: EEG: Theta 4-8 Hz Alpha 8-12Hz Gamma 26-70Hz Computers — 55Million or more transistors per CPU — supercomputer: hundreds of CPUs — cycle times: 3GHz
Which method do computer think ? : Algorithm & Experience The brain — Experience Induction/ Inductive Reasoning (Learning from experience) Computers — Algorithm Fix method — Algorithm Induction (Learning from data)
Game Play / Search Tree start Example of Search Tree — Possible Move x x x O x O O O x x O x x x o o x goal o x
Heuristic Search Heuristic — heuristic is a function h(x) defined on node of search tree Search will expand the lowest value for g(n)+h(n) g(n) = (exact) cost of the path from the initial state to the current node h(n) = never over estimates the cost of reaching the goal Examples: Solved n-Puzzle, Rubik
#4 Problem in AI Data Fitting / Data Prediction Problem — future, who knows? eg. stock market overfit underfit value goodfit time
Problem in AI Classification Problem —
Problem in AI Optimization Problem — Traveling Salesman Problem [TSP] — Findout a new design to reduce cost, energy, efficiency ..
#5 Game AI — Path Solver — Expert System — Finite Stage Machine FSM
Path Finding Path Solver — Automatic movement in map http://theory.stanford.edu/~amitp/GameProgramming/AStarComparison.html
Finite Stage Machine [FSM] — Given Rules ตื่น พลังเพิ่ม? มองเห็นผู้เล่น? หลับ หนี มองไม่เห็นผู้เล่น? เข้าใกล้? มองไม่เห็นผู้เล่น? พลังลดลง? ต่อสู้ รอ? พลังหมด? พลังหมด? เกิดใหม่ ตาย มีโควต้า? Drop Items..
#6 More Game AI examples RPGs — Scripting (eg. Phython, Ruby ) — Rules based — Agent, Bots
Ethics in Game AI ? Should AI cheat ? — Cheat Declaration: Know opponent's data Know systems data — no cheat = hard code — cheat = easier code base on global data Game purpose is for fun. even AI cheating and people who play game still feel fun. do it.