CS 2710 ISSP 2610 Foundations of Artificial Intelligence

CS 2710, ISSP 2610 Foundations of Artificial Intelligence introduction 1

Welcome! • Syllabus 2

What is Artificial Intelligence? • 4 views on what makes an AI system (next slide; R&N 1. 1) • Depends on what matters more in the evaluation • Reasoning vs. Behavior [diagram] • Human performance vs. Rationality – Human model – Rational model 3

4 Views of AI Think like humans Think rationally Act like humans Act rationally 4

Acting Like Humans? • Turing (1950) • “Can machines think? ” “Can machines behave intelligently? ” • Operational test for intelligent behavior: the Imitation Game 5

Player C tries to determine which of A and B are human/computer 6

Turing Test • Suggested the major components of AI: – NLP (communicate in language) – Knowledge representation (to store what it knows) – Automated reasoning (use knowledge to answer questions and draw conclusions) – Machine learning (adapt to new circumstances; prediction) – Vision – Robotics (manipulate objects and move around) 7

Turing Test • Not how we evaluate AI methods; Why? [board + next slide] 8

Sample Eliza Dialogue Joseph Weizenbaum ~1965 • • • • • young woman: Men are all alike. eliza: In what way? young woman: They're always bugging us about something specific or other. eliza: Can you think of a specific example? young woman: Well, my boyfriend made me come here. eliza: Your boyfriend made you come here? young woman: He says I'm depressed much of the time. eliza: I am sorry to hear you are depressed. young woman: It's true. I'm unhappy. eliza: Do you think coming here will help you not to be unhappy? young woman: I need some help, that much seems certain. eliza: What would it mean to you if you got some help? young woman: Perhaps I could learn to get along with my mother. eliza: Tell me more about your family. young woman: My mother takes care of me. eliza: Who else in your family takes care of you? young woman: My father … 9

Thinking Like Humans? • For this approach, need a way to determine how humans think – Introspection (rejected by researchers as too subjective) – Psychological experiments: predicting (based on cognitive theory) and then testing, behavior of human subjects; matched predictions give evidence for theory – Brain imaging – observing the brain in action • Cognitive Science: – Express a theory as a computer program; input/output behavior matching human behavior is evidence for theory – Computer models from AI and experimental techniques from Psychology; also – Neurophysiological evidence incorporated into computational models, e. g. vision 10

Thinking Like Humans • AI and Cognitive Science are now largely distinct research areas [board] 11

Thinking Rationally? 12

The “Laws of thought” approach • Logicist tradition: • Problems: • Logic is important in AI; but a pure logicist approach (early AI history) to intelligence is not effective – – – Logic: notation and rules of derivation for thoughts Aristotle: what are correct arguments/thought processes? Direct line through mathematics, philosophy, to modern AI – – – Not all intelligent behavior is mediated by logical deliberation It is difficult to express informal knowledge in logic It is not sufficient: • Need a search process to go down fruitful reasoning paths • logical systems tend to do the wrong thing in the presence of uncertainty • That leaves us with …. 13

Acting Rationally: Our Basic Framework • Getting computers to do the right thing based on their circumstances and what they know. – Irrational != insane; irrationality is sub-optimal action – Rational != successful; the most rational action may not succeed due to some circumstance beyond our control or due to incomplete knowledge – Make the best choice, given the options • Rational agents [board] 14

1940 1950: Early days 1943: Mc. Culloch & Pitts: Boolean circuit model of brain 1950: Turing's “Computing Machinery and Intelligence” 1950— 70: Excitement: Look, Ma, no hands! 1950 s: Early AI programs, including Samuel's checkers program, Newell & Simon's Logic Theorist, Gelernter's Geometry Engine 1956: Dartmouth meeting: “Artificial Intelligence” adopted 1965: Robinson's complete algorithm for logical reasoning 1970— 88: Knowledge based approaches 1969— 79: Early development of knowledge based systems 1980— 88: Expert systems industry booms 1988— 93: Expert systems industry busts: “AI Winter” 1988—: Statistical approaches Resurgence of probability, focus on uncertainty General increase in technical depth 15 Agents and learning systems… “AI Spring”?

AI applications AI techniques are used in many common applications; just a sample – – – – – Intelligent user interfaces Search Engines Spell/grammar checkers Context sensitive help systems Medical diagnosis systems Regulating/Controlling hardware devices and processes (e. g, in automobiles) Voice/image recognition (more generally, pattern recognition) Scheduling systems (airlines, hotels, manufacturing) Error detection/correction in electronic communication Program verification / compiler and programming language design Web search engines / Web spiders Web personalization and Recommender systems (collaborative/content filtering) Personal agents Customer relationship management Credit card verification in e-commerce / fraud detection Data mining and knowledge discovery in databases Computer games 16

What to expect Abstractive thinking/imagination sometimes needed Extreme range of problem domains (as we just saw on the sample of applications). We need to look for frameworks that apply to a hugely diverse range of problem domains. Abstract distinctions abound. Real problem domains are often so complex we need to work with simpler ones, and imagine what would be needed in a realistic domain Not a definitive answer about which method is best; depends on the problem! AI problems are those that we really don’t know how to solve. Otherwise, we would use a direct solution (and it would not be considered AI anymore) Real AI systems are often mixtures of various algorithms/techniques, experimentally determined 17

Course Topics Four major areas: • Problem solving and search. – Formulating a search problem, uninformed and informed search; constraint satisfaction, optimization, and adversarial search. • Logic and knowledge representation – First-order logic; reasoning; knowledge representation schemes • Planning – Situation calculus, STRIPS, Partial-order planning, Graph. Plan and SAT planners • Uncertainty and Learning – Modeling uncertainty, Bayesian belief networks, decision theory, classification, density estimation 18

Wrap Up • Chapter 1: – You will not be tested on Sections 1. 2 and 1. 3 (history; foundations). But it’s interesting! – Be able to explain the different possible approaches to AI and why AI has settled on the rational action approach • Chapter 2: – Will be covered on homework 1; Any explicit exam question will be related to its coverage on homework 1 19