Roli, Andrea

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Almae Matris Studiorum Campus

Artificial Intelligence: IntroductionSlides from Russell & Norvig book, revised by Andrea RoliOutline♦ What is AI?♦ A brief history♦ Philosophical foundations♦ The state of the artActing humanly: The Turing testTuring (1950) “Computing machinery and intelligence”:♦ “Can machines think?” −→ “Can machines behaveintelligently?”♦ Operational test for intelligent behavior: the Imitation GameAI SYSTEMHUMAN?        HUMANINTERROGATOR♦ Predicted that by 2000, a machine might have a 30%chance of fooling a lay person for 5 minutes♦ Anticipated all major arguments against AI in following 50years♦ Suggested major components of AI: knowledge, reasoning,language understanding, learningProblem: Turing test is not reproducible, constructive, oramenable to mathematical analysisThinking humanly: Cognitive Science• 1960s “cognitive revolution”: information-processingpsychology replaced prevailing orthodoxy of behaviorism• Requires scientific theories of internal activities of thebrain:• What level of abstraction? “Knowledge” or “circuits”?• How to validate? Requires (1) Predicting and testingbehavior of human subjects (top-down) or (2) Directidentification from neurological data (bottom-up)• Both approaches (roughly, Cognitive Science andCognitive Neuroscience) are now distinct from AI• Both share with AI the following characteristic: theavailable theories do not explain (or engender)anything resembling human-level general intelligenceAI prehistoryPhilosophy logic, methods of reasoningmind as physical systemfoundations of learning, language, rationalityMathematics formal representation and proofalgorithms,computation,(un)decidability,(in)tractabilityprobabilityPsychology adaptationphenomena of perception and motor controlexperimental techniques (psychophysics, etc.)Economics formal theory of rational decisionsLinguistics knowledge representationgrammarNeuroscience plastic physical substrate for mental activityControl theory homeostatic systems, stabilitysimple optimal agent designsHistory of AI1943 McCulloch & Pitts: Boolean circuit model of brain1950 Turing’s “Computing Machinery and Intelligence”1950s Early AI programs, including Samuel’s checkers program,Newell & Simon’s Logic Theorist, Gelernter’s Geometry Engine1956 Dartmouth meeting: “Artificial Intelligence” adopted1965 Robinson’s complete algorithm for logical reasoning1966–74 AI discovers computational complexityNeural network research almost disappears1969–79 Early development of knowledge-based systems1980–88 Expert systems industry booms1988–93 Expert systems industry busts: “AI Winter”1985–95 Neural networks return to popularity1988– Resurgence of probability; general increase in technical depth“Nouvelle AI”: ALife, GAs, soft computing1995– Agents, agents, everywhere . . .2003– Human-level AI back on the agendaPhilosophical foundationsTwo main hypotheses:• Weak AI: Machine can act as if they were intelligent• Strong AI: Machines can be intelligentObjections to weak AI• The argument from disability (“A machine can never do X”)• The mathematical objection (computability theory)• The argument from ‘informality’/complexity of behaviorObjections to strong AI• Consciousness• Mind-body problem• Free willMental experiments:• The brain in a vat (The matrix?)• The brain prosthesis• The Chinese room⊲ Still ongoing debate. . .State of the art• Autonomous planning and scheduling• Game playing• Autonomous control• Diagnosis• Logistics planning• Robotics• Language understanding and problem solving

01 - Introduction to AI (handouts)

http://campus.unibo.it/2836/1/01-AI_intro.4x.pdf

01 - Introduction to AI (handouts)

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