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Slide 1 - Intorduction to Artificial Intelligence Rina Dechter CS 171 Fall 2006
Slide 2 - 271- Fall 2006 Robotic links Robocup Video Soccer Robocupf Darpa Challenge Darpa’s-challenge-video http://www.darpa.mil/grandchallenge05/TechPapers/Stanford.pdf
Slide 3 - 271- Fall 2006 CS171 Course home page: http://www.ics.uci.edu/~dechter/ics-171/fall-06/ schedule, lecture notes, tutorials, assignment, grading, office hours, etc. Textbook: S. Russell and P. Norvig Artificial Intelligence: A Modern Approach Prentice Hall, 2003, Second Edition Grading: Homeworks and projects (30-40%) Midterm and final (60-70%)
Slide 4 - 271- Fall 2006 Course overview Introduction and Agents (chapters 1,2) Search (chapters 3,4) Games (chapter 5) Constraints processing (chapter 6) Representation and Reasoning with Logic (chapters 7,8,9) Learning (chapters 18,20) Planning (chapter 11) Uncertainty (chapters 13,14) Natural Language Processing (chapter 22,23)
Slide 5 - 271- Fall 2006 Course Outline Resources on the Internet AI on the Web: A very comprehensive list of Web resources about AI from the Russell and Norvig textbook. Essays and Papers What is AI, John McCarthy Computing Machinery and Intelligence, A.M. Turing Rethinking Artificial Intelligence, Patrick H.Winston
Slide 6 - 271- Fall 2006 Today’s class What is Artificial Intelligence? A brief History Intelligent agents State of the art
Slide 7 - 271- Fall 2006 What is Artificial Intelligence (John McCarthy , Basic Questions) What is artificial intelligence? It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable. Yes, but what is intelligence? Intelligence is the computational part of the ability to achieve goals in the world. Varying kinds and degrees of intelligence occur in people, many animals and some machines. Isn't there a solid definition of intelligence that doesn't depend on relating it to human intelligence? Not yet. The problem is that we cannot yet characterize in general what kinds of computational procedures we want to call intelligent. We understand some of the mechanisms of intelligence and not others. More in: http://www-formal.stanford.edu/jmc/whatisai/node1.html
Slide 8 - 271- Fall 2006 What is AI? Views of AI fall into four categories: Thinking humanly Thinking rationally Acting humanly Acting rationally The textbook advocates "acting rationally“ List of AI-topics
Slide 9 - 271- Fall 2006 What is Artificial Intelligence? Human-like (“How to simulate humans intellect and behavior on by a machine.) Mathematical problems (puzzles, games, theorems) Common-sense reasoning (if there is parking-space, probably illegal to park) Expert knowledge: lawyers, medicine, diagnosis Social behavior Rational-like: achieve goals, have performance measure
Slide 10 - 271- Fall 2006 What is Artificial Intelligence Thought processes “The exciting new effort to make computers think .. Machines with minds, in the full and literal sense” (Haugeland, 1985) Behavior “The study of how to make computers do things at which, at the moment, people are better.” (Rich, and Knight, 1991) The automation of activities that we associate with human thinking, activities such as decision-making, problem solving, learning… (Bellman)
Slide 11 - 271- Fall 2006 The Turing Test (Can Machine think? A. M. Turing, 1950) Requires Natural language Knowledge representation Automated reasoning Machine learning (vision, robotics) for full test
Slide 12 - 271- Fall 2006 What is AI? Turing test (1950) Requires: Natural language Knowledge representation automated reasoning machine learning (vision, robotics.) for full test Thinking humanly: Introspection, the general problem solver (Newell and Simon 1961) Cognitive sciences Thinking rationally: Logic Problems: how to represent and reason in a domain Acting rationally: Agents: Perceive and act
Slide 13 - 271- Fall 2006 AI examples Common sense reasoning Tweety Yale Shooting problem Update vs revise knowledge The OR gate example: A or B - C Observe C=0, vs Do C=0 Chaining theories of actions Looks-like(P)  is(P) Make-looks-like(P)  Looks-like(P) ---------------------------------------- Makes-looks-like(P) ---is(P) ??? Garage-door example: garage door not included. Planning benchmarks 8-puzzle, 8-queen, block world, grid-space world Abduction: cambridge parking example
Slide 14 - 271- Fall 2006 History of AI McCulloch and Pitts (1943) Neural networks that learn Minsky (1951) Built a neural net computer Darmouth conference (1956): McCarthy, Minsky, Newell, Simon met, Logic theorist (LT)- proves a theorem in Principia Mathematica-Russel. The name “Artficial Intelligence” was coined. 1952-1969 GPS- Newell and Simon Geometry theorem prover - Gelernter (1959) Samuel Checkers that learns (1952) McCarthy - Lisp (1958), Advice Taker, Robinson’s resolution Microworlds: Integration, block-worlds. 1962- the perceptron convergence (Rosenblatt)
Slide 15 - 271- Fall 2006 The Birthplace of “Artificial Intelligence”, 1956 Darmouth workshop, 1956: historical meeting of the precieved founders of AI met: John McCarthy, Marvin Minsky, Alan Newell, and Herbert Simon. A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence. J. McCarthy, M. L. Minsky, N. Rochester, and C.E. Shannon. August 31, 1955. "We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it." And this marks the debut of the term "artificial intelligence.“ 50 anniversery of Darmouth workshop
Slide 16 - 271- Fall 2006 History, continued 1966-1974 a dose of reality Problems with computation 1969-1979 Knowledge-based systems Weak vs. strong methods Expert systems: Dendral:Inferring molecular structures Mycin: diagnosing blood infections Prospector: recomending exploratory drilling (Duda). Roger Shank: no syntax only semantics 1980-1988: AI becomes an industry R1: Mcdermott, 1982, order configurations of computer systems 1981: Fifth generation 1986-present: return to neural networks Recent event: AI becomes a science: HMMs, planning, belief network
Slide 17 - 271- Fall 2006 Abridged history of AI 1943 McCulloch & Pitts: Boolean circuit model of brain 1950 Turing's "Computing Machinery and Intelligence" 1956 Dartmouth meeting: "Artificial Intelligence" adopted 1952—69 Look, Ma, no hands! 1950s Early AI programs, including Samuel's checkers program, Newell & Simon's Logic Theorist, Gelernter's Geometry Engine 1965 Robinson's complete algorithm for logical reasoning 1966—73 AI discovers computational complexity Neural network research almost disappears 1969—79 Early development of knowledge-based systems 1980-- AI becomes an industry 1986-- Neural networks return to popularity 1987-- AI becomes a science 1995-- The emergence of intelligent agents
Slide 18 - 271- Fall 2006 State of the art Deep Blue defeated the reigning world chess champion Garry Kasparov in 1997 Proved a mathematical conjecture (Robbins conjecture) unsolved for decades No hands across America (driving autonomously 98% of the time from Pittsburgh to San Diego) During the 1991 Gulf War, US forces deployed an AI logistics planning and scheduling program that involved up to 50,000 vehicles, cargo, and people NASA's on-board autonomous planning program controlled the scheduling of operations for a spacecraft Proverb solves crossword puzzles better than most humans DARPA grand challenge 2003-2005, Robocup
Slide 19 - 271- Fall 2006 Robotic links Robocup Video Soccer Robocupf Darpa Challenge Darpa’s-challenge-video http://www.darpa.mil/grandchallenge05/TechPapers/Stanford.pdf
Slide 20 - 271- Fall 2006 Agents (chapter 2) Agents and environments Rationality PEAS (Performance measure, Environment, Actuators, Sensors) Environment types Agent types
Slide 21 - 271- Fall 2006 Agents An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators Human agent: eyes, ears, and other organs for sensors; hands, legs, mouth, and other body parts for actuators Robotic agent: cameras and infrared range finders for sensors; various motors for actuators
Slide 22 - 271- Fall 2006 Agents and environments The agent function maps from percept histories to actions: [f: P*  A] The agent program runs on the physical architecture to produce f agent = architecture + program
Slide 23 - 271- Fall 2006 Vacuum-cleaner world Percepts: location and contents, e.g., [A,Dirty] Actions: Left, Right, Suck, NoOp
Slide 24 - 271- Fall 2006 Rational agents An agent should strive to "do the right thing", based on what it can perceive and the actions it can perform. The right action is the one that will cause the agent to be most successful Performance measure: An objective criterion for success of an agent's behavior E.g., performance measure of a vacuum-cleaner agent could be amount of dirt cleaned up, amount of time taken, amount of electricity consumed, amount of noise generated, etc.
Slide 25 - 271- Fall 2006 Rational agents Rational Agent: For each possible percept sequence, a rational agent should select an action that is expected to maximize its performance measure, given the evidence provided by the percept sequence and whatever built-in knowledge the agent has.
Slide 26 - 271- Fall 2006 What’s involved in Intelligence? Intelligent agents Ability to interact with the real world to perceive, understand, and act e.g., speech recognition and understanding and synthesis e.g., image understanding e.g., ability to take actions, have an effect Knowledge Representation, Reasoning and Planning modeling the external world, given input solving new problems, planning and making decisions ability to deal with unexpected problems, uncertainties Learning and Adaptation we are continuously learning and adapting our internal models are always being “updated” e.g. a baby learning to categorize and recognize animals
Slide 27 - 271- Fall 2006 Implementing agents Table look-ups Autonomy All actions are completely specified no need in sensing, no autonomy example: Monkey and the banana Structure of an agent agent = architecture + program Agent types medical diagnosis Satellite image analysis system part-picking robot Interactive English tutor cooking agent taxi driver
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Slide 40 - 271- Fall 2006 Agent types Example: Taxi driver Simple reflex If car-in-front-is-breaking then initiate-breaking Agents that keep track of the world If car-in-front-is-breaking and on fwy then initiate-breaking needs internal state goal-based If car-in-front-is-breaking and needs to get to hospital then go to adjacent lane and plan search and planning utility-based If car-in-front-is-breaking and on fwy and needs to get to hospital alive then search of a way to get to the hospital that will make your passengers happy. Needs utility function that map a state to a real function (am I happy?)
Slide 41 - 271- Fall 2006 Summary What is Artificial Intelligence? modeling humans thinking, acting, should think, should act. History of AI Intelligent agents We want to build agents that act rationally Real-World Applications of AI AI is alive and well in various “every day” applications many products, systems, have AI components Assigned Reading Chapters 1 and 2 in the text R&N