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Introduction to Artificial Intelligence

Introduction to Artificial Intelligence. Mitch Marcus CIS391 Fall, 2008. Welcome to CIS 391. Course home page: http://www.cis.upenn.edu/~cse391 Textbook: S. Russell and P. Norvig Artificial Intelligence: A Modern Approach Prentice Hall, 2003, Second Edition

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Introduction to Artificial Intelligence

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  1. Introduction to Artificial Intelligence Mitch Marcus CIS391 Fall, 2008

  2. Welcome to CIS 391 • Course home page: http://www.cis.upenn.edu/~cse391 • Textbook: S. Russell and P. Norvig Artificial Intelligence: A Modern Approach Prentice Hall, 2003, Second Edition • Lecturer: Mitch Marcus, mitch@cis.upenn.edu, Levine 503 • TA: Ryan Gabbard, gabbard@seas.upenn.edu, Levine 554 • TA: Qiuye (Sophie) Zhao, qiuye@seas.upenn.edu, Levine 571 • Prerequisites: CSE 121, (CSE 260) • For Grading details, see Handout…

  3. “Computers who will proud of us”

  4. A REAL Accomplishment: DARPA Grand Challenge

  5. A REAL Accomplishment: DARPA Urban Challenge

  6. Some Real Accomplishments of AI • DARPA Grand Challenge – 123 miles through the desert • DARPA Urban Challenge – Autonomous Driving in Traffic! • Deep Space 1 – Remote Agent Experiment • Deep Blue defeated the reigning world chess champion Garry Kasparov in 1997 • Proved a mathematical conjecture (Robbins conjecture) unsolved for decades • Logistics Planning for 1991 Gulf War • Computer Algebra Systems • Credit Evaluation • Fraud Detection • Spam Filtering

  7. What is AI? Views of AI fall into four categories: Thinking humanly Thinking rationally Acting humanly Acting rationally We will focus on "acting rationally"

  8. Acting humanly: Turing Test • Turing (1950) "Computing machinery and intelligence":“Can machines think?”  “Can machines behave intelligently?” • Operational test for intelligent behavior: the Imitation Game • Predicted that by 2000, a machine might have a 30% chance of fooling a lay person for 5 minutes • Anticipated most major arguments against AI • Suggested major components of AI: knowledge, reasoning, language understanding, learning

  9. Thinking humanly:cognitive modeling • 1960s "cognitive revolution": information-processing psychology • Requires scientific theories of internal activities of the brain • How to validate? Requires 1) Predicting and testing behavior of human subjects (top-down) or 2) Direct identification from neurological data (bottom-up) • Both approaches (roughly, Cognitive Science and Cognitive Neuroscience) are now distinct from AI

  10. Thinking rationally: "laws of thought" • Aristotle: what are correct arguments/thought processes? • Several Greek schools developed various forms of logic: notation and rules of derivation for thoughts; may or may not have proceeded to the idea of mechanization • Direct line through mathematics and philosophy to modern AI • Problems: • Not all intelligent behavior is mediated by logical deliberation • What is the purpose of thinking? What thoughts should I have? • Ignores the hard problem of perception

  11. Acting rationally: rational agents • Rational behavior: doing the right thing • The right thing: that which is expected to maximize goal achievement, given the available information • Doesn't necessarily involve thinking – e.g., blinking reflex – but thinking should be in the service of rational action

  12. Rational agents • An agent is an entity that perceives and acts • This course is about effective programming techniques for designing rational agents • Abstractly, an agent is a function from percept histories to actions: [f: P*A] • For any given class of environments and tasks, we seek the agent (or class of agents) with the best performance • Caveat: computational limitations make perfect rationality unachievable  design best program for given machine resources

  13. Search Reasoning Machine Learning Representation of the World Rational Agent skills can include: Vision Processing Planning Robotics Natural Language Understanding Symbols (Logic, Numbers)

  14. Course Highlights 0. Introduction & Python Programming   • Search Strategies, Constraint Satisfaction, Adversarial Games • Homework: Build a solver for any Sudoku problem • Final Project: Build a bot that competes against other game bots to play a simple board game (Kingdoms) • Knowledge Representation and Logic • Homework: Build an agent that plays Hunt the Wumpus using propositional logic theorem proving • Machine Learning: Perceptrons, Naïve Bayes, etc. • Homework: Build a spam filter using Naïve Bayes machine learning

  15. More Detail – First Half Module 0: Introduction  • Intelligent Agents • Python Programming (Week long introduction) Module 1: Search Strategies • Uninformed & Informed Search • Constraint Satisfaction (Sudoku Solver) • Adversarial Search (Game Playing) Module 2: Knowledge Representation and Logic • Logical Agents (Automated Wumpus adventurer) • Topics in Knowledge Representation • (Midterm more or less here)

  16. More Detail – Second Half Module 3: Machine Learning and Natural Language Processing • Introduction to Probability and Bayesian Networks • Naive Bayes (Spam Filtering) • Hidden Markov Models & Part of Speech Tagging • Decision Trees • Perceptrons and Support Vector Machines • (Context Free Parsing for English)

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