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- Sensors that allow the system to perceive its environment - Actuators that allow the system to act on or change its environment - An ability to reason and make rational decisions or plans of action based on its perceptions The document discusses sensors and actuators in the context of intelligent agents - for humans, examples of sensors are eyes and ears, while actuators are hands, legs, and mouth. For robots, examples of sensors would be cameras and other input devices, while actuators would allow the robot to move or manipulate objects.

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1 Introduction to Artificial Intelligence Third Year Computer Engineering Bhalke A.P.
Content  Introduction to Artificial Intelligence,  Foundations of Artificial Intelligence,  History of Artificial Intelligence,  State of the Art,  Risks and Benefits of AI,  Intelligent Agents,  Agents and Environments,  Good Behavior: Concept of Rationality,  Nature of Environments, Structure of Agents. 2
Why Study AI?  AI makes computers more useful  Intelligent computer would have huge impact on civilization  AI cited as “field I would most like to be in” by scientists in all fields  Computer is a good metaphor for talking and thinking about intelligence
Why Study AI?  Turning theory into working programs forces us to work out the details  AI yields good results for Computer Science  AI yields good results for other fields  Computers make good experimental subjects  Personal motivation: mystery
What is artificial intelligence?  Popular conception driven by science ficition  Robots good at everything except emotions, empathy, appreciation of art, culture, …  … until later in the movie.  Perhaps more representative of human autism than of (current?) real robotics/AI  “It is my belief that the existence of autism has contributed to [the theme of the intelligent but soulless automaton] in no small way.” [Uta Frith, “Autism”]  Current AI is also bad at lots of simpler stuff!  There is a lot of AI work on thinking about what others are thinking
Real AI  A serious science.  General-purpose AI like the robots of science fiction is incredibly hard  Human brain appears to have lots of special and general functions, integrated in some amazing way that we really do not understand at all (yet)  Special-purpose AI is more doable (nontrivial)  E.g., chess/poker playing programs, logistics planning, automated translation, voice recognition, web search, data mining, medical diagnosis, keeping a car on the road, … … … …
Definitions of AI Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally focus on action avoids philosophical issues such as “is the system conscious” etc. • We will follow “act rationally” approach – Distinction may not be that important • acting rationally/like a human presumably requires (some sort of) thinking rationally/like a human, • humans much more rational anyway in complex domains if our system can be more rational than humans in some cases, why not?
What is the definition of AI? Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally
What is the definition of AI? Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally Bellman, 1978 “[The automation of] activities that we associate with human thinking, activities such as decision making, problem solving, learning”
What is the definition of AI? Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally Charniak & McDermott, 1985 “The study of mental faculties through the use of computational models”
What is the definition of AI? Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally Dean et al., 1995 “The design and study of computer programs that behave intelligently. These programs are constructed to perform as would a human or an animal whose behavior we consider intelligent”
What is the definition of AI? Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally Haugeland, 1985 “The exciting new effort to make computers think machines with minds, in the full and literal sense”
What is the definition of AI? Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally Kurzweil, 1990 “The art of creating machines that perform functions that require intelligence when performed by people”
What is the definition of AI? Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally Luger & Stubblefield, 1993 “The branch of computer science that is concerned with the automation of intelligent behavior”
What is the definition of AI? Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally Nilsson, 1998 “Many human mental activities such as writing computer programs, doing mathematics, engaging in common sense reasoning, understanding language, and even driving an automobile, are said to demand intelligence. We might say that [these systems] exhibit artificial intelligence”
What is the definition of AI? Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally Rich & Knight, 1991 “The study of how to make computers do things at which, at the moment, people are better”
What is the definition of AI? Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally Schalkoff, 1990 “A field of study that seeks to explain and emulate intelligent behavior in terms of computational processes”
What is the definition of AI? Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally Winston, 1992 “The study of the computations that make it possible to perceive, reason, and act”
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
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 Turing Test (Can Machine think? A. M. Turing, 1950)  Requires  Natural language  Knowledge representation  Automated reasoning  Machine learning  (vision, robotics) for full test
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
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
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)
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
Abridged history of AI  1943  1950  1956  1952—69  1950s McCulloch & Pitts: Boolean circuit model of brain Turing's "Computing Machinery and Intelligence" Dartmouth meeting: "Artificial Intelligence" adopted Look, Ma, no hands! Early AI programs, including Samuel's checkers program, Newell & Simon's Logic Theorist, Gelernter's Geometry Engine Robinson's complete algorithm for logical reasoning AI discovers computational complexity Neural network research almost disappears Early development of knowledge-based systems AI becomes an industry Neural networks return to popularity AI becomes a science The emergence of intelligent agents  1965  1966—73  1969—79  1980--  1986--  1987--  1995--
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
Components of an AI System An agent perceives its environment through sensors and acts on the environment through actuators. Human: sensors are eyes, ears, actuators (effectors) are hands, legs, mouth. Robot: sensors are cameras, sonar, lasers, ladar, bump, effectors are grippers, manipulators, motors The agent’s behavior is described by it function that maps percept to action.
Rationality  A rational agent does the right thing (what is this?)  A fixed performance measure evaluates the sequence of observed action effects on the environment
PEAS  Use PEAS to describe task  Performance measure  Environment  Actuators  Sensors
PEAS  Use PEAS to describe task environment  Performance measure  Environment  Actuators  Sensors  Example: Taxi driver  Performance measure: safe, fast, comfortable (maximize profits)  Environment: roads, other traffic, pedestrians, customers  Actuators: steering, accelerator, brake, signal, horn  Sensors: cameras, sonar, speedometer, GPS, odometer, accelerometer, engine sensors
Environment Properties  Fully observable vs. partially observable  Deterministic vs. stochastic / strategic  Episodic vs. sequential  Static vs. dynamic  Discrete vs. continuous  Single agent vs. multiagent
Environment Examples Environment Obse rvabl e Determ inistic Episodic Stati c Discret e Agent s Chess with a clock Chess without a clock Fully observable vs. partially observable Deterministic vs. stochastic / strategic Episodic vs. sequential Static vs. dynamic Discrete vs. continuous Single agent vs. multiagent
Environment Examples Environment Obse rvabl e Determ inistic Episodic Stati c Discret e Agent s Chess with a clock Fully Strategi c Sequenti al Semi Discret e Multi Chess without a clock Fully Strategi c Sequenti al Stati c Discret e Multi Fully observable vs. partially observable Deterministic vs. stochastic / strategic Episodic vs. sequential Static vs. dynamic Discrete vs. continuous Single agent vs. multiagent
Environment Examples Fully observable vs. partially observable Deterministic vs. stochastic / strategic Episodic vs. sequential Static vs. dynamic Discrete vs. continuous Single agent vs. multiagent Environment Obse rvabl e Determ inistic Episodic Stati c Discret e Agent s Chess with a clock Fully Strategi c Sequenti al Semi Discret e Multi Chess without a clock Fully Strategi c Sequenti al Stati c Discret e Multi Poker
Environment Examples Fully observable vs. partially observable Deterministic vs. stochastic / strategic Episodic vs. sequential Static vs. dynamic Discrete vs. continuous Single agent vs. multiagent Environment Obse rvabl e Determ inistic Episodic Stati c Discret e Agent s Chess with a clock Fully Strategi c Sequenti al Semi Discret e Multi Chess without a clock Fully Strategi c Sequenti al Stati c Discret e Multi Poker Partia l Strategi c Sequenti al Stati c Discret e Multi
Environment Examples Fully observable vs. partially observable Deterministic vs. stochastic / strategic Episodic vs. sequential Static vs. dynamic Discrete vs. continuous Single agent vs. multiagent Environment Obse rvabl e Determ inistic Episodic Stati c Discret e Agent s Chess with a clock Fully Strategi c Sequenti al Semi Discret e Multi Chess without a clock Fully Strategi c Sequenti al Stati c Discret e Multi Poker Partia l Strategi c Sequenti al Stati c Discret e Multi Backgammon
Environment Examples Fully observable vs. partially observable Deterministic vs. stochastic / strategic Episodic vs. sequential Static vs. dynamic Discrete vs. continuous Single agent vs. multiagent Environment Obse rvabl e Determ inistic Episodic Stati c Discret e Agent s Chess with a clock Fully Strategi c Sequenti al Semi Discret e Multi Chess without a clock Fully Strategi c Sequenti al Stati c Discret e Multi Poker Partia l Strategi c Sequenti al Stati c Discret e Multi Backgammon Fully Stochas tic Sequenti al Stati c Discret e Multi
Environment Examples Fully observable vs. partially observable Deterministic vs. stochastic / strategic Episodic vs. sequential Static vs. dynamic Discrete vs. continuous Single agent vs. multiagent Environment Obse rvabl e Determ inistic Episodic Stati c Discret e Agent s Chess with a clock Fully Strategi c Sequenti al Semi Discret e Multi Chess without a clock Fully Strategi c Sequenti al Stati c Discret e Multi Poker Partia l Strategi c Sequenti al Stati c Discret e Multi Backgammon Fully Stochas tic Sequenti al Stati c Discret e Multi Taxi driving Partia l Stochas tic Sequenti al Dyn amic Continu ous Multi
Environment Examples Fully observable vs. partially observable Deterministic vs. stochastic / strategic Episodic vs. sequential Static vs. dynamic Discrete vs. continuous Single agent vs. multiagent Environment Obse rvabl e Determ inistic Episodic Stati c Discret e Agent s Chess with a clock Fully Strategi c Sequenti al Semi Discret e Multi Chess without a clock Fully Strategi c Sequenti al Stati c Discret e Multi Poker Partia l Strategi c Sequenti al Stati c Discret e Multi Backgammon Fully Stochas tic Sequenti al Stati c Discret e Multi Taxi driving Partia l Stochas tic Sequenti al Dyn amic Continu ous Multi Medical diagnosis
Environment Examples Fully observable vs. partially observable Deterministic vs. stochastic / strategic Episodic vs. sequential Static vs. dynamic Discrete vs. continuous Single agent vs. multiagent Environment Obse rvabl e Determ inistic Episodic Stati c Discret e Agent s Chess with a clock Fully Strategi c Sequenti al Semi Discret e Multi Chess without a clock Fully Strategi c Sequenti al Stati c Discret e Multi Poker Partia l Strategi c Sequenti al Stati c Discret e Multi Backgammon Fully Stochas tic Sequenti al Stati c Discret e Multi Taxi driving Partia l Stochas tic Sequenti al Dyn amic Continu ous Multi Medical diagnosis Partia l Stochas tic Episodic Stati c Continu ous Single
Environment Examples ally observable ic / strategic Fully observable vs. parti Deterministic vs. stochast Episodic vs. sequential Static vs. dynamic Discrete vs. continuous Single agent vs. multiagent Environment Obse rvabl e Determ inistic Episodic Stati c Discret e Agent s Chess with a clock Fully Strategi c Sequenti al Semi Discret e Multi Chess without a clock Fully Strategi c Sequenti al Stati c Discret e Multi Poker Partia l Strategi c Sequenti al Stati c Discret e Multi Backgammon Fully Stochas tic Sequenti al Stati c Discret e Multi Taxi driving Partia l Stochas tic Sequenti al Dyn amic Continu ous Multi Medical diagnosis Partia l Stochas tic Episodic Stati c Continu ous Single Image analysis
Environment Examples Environment Obse rvabl e Determ inistic Episodic Stati c Discret e Agent s Chess with a clock Fully Strategi c Sequenti al Semi Discret e Multi Chess without a clock Fully Strategi c Sequenti al Stati c Discret e Multi Poker Partia l Strategi c Sequenti al Stati c Discret e Multi Backgammon Fully Stochas tic Sequenti al Stati c Discret e Multi Taxi driving Partia l Stochas tic Sequenti al Dyn amic Continu ous Multi Medical diagnosis Partia l Stochas tic Episodic Stati c Continu ous Single Image analysis Fully Determi nistic Episodic Semi Discret e Single Fully observable vs. partially observable Deterministic vs. stochastic / strategic Episodic vs. sequential Static vs. dynamic Discrete vs. continuous Single agent vs.
Environment Examples Fully observable vs. partially observable Deterministic vs. stochastic / strategic Episodic vs. sequential Static vs. dynamic Discrete vs. continuous Single agent vs. Environment Obse rvabl e Determ inistic Episodic Stati c Discret e Agent s Chess with a clock Fully Strategi c Sequenti al Semi Discret e Multi Chess without a clock Fully Strategi c Sequenti al Stati c Discret e Multi Poker Partia l Strategi c Sequenti al Stati c Discret e Multi Backgammon Fully Stochas tic Sequenti al Stati c Discret e Multi Taxi driving Partia l Stochas tic Sequenti al Dyn amic Continu ous Multi Medical diagnosis Partia l Stochas tic Episodic Stati c Continu ous Single Image analysis Fully Determi nistic Episodic Semi Discret e Single Robot part picking
Environment Examples Fully observable vs. partially observable Deterministic vs. stochastic / strategic Episodic vs. sequential Static vs. dynamic Discrete vs. continuous Single agent vs. Environment Obse rvabl e Determ inistic Episodic Stati c Discret e Agent s Chess with a clock Fully Strategi c Sequenti al Semi Discret e Multi Chess without a clock Fully Strategi c Sequenti al Stati c Discret e Multi Poker Partia l Strategi c Sequenti al Stati c Discret e Multi Backgammon Fully Stochas tic Sequenti al Stati c Discret e Multi Taxi driving Partia l Stochas tic Sequenti al Dyn amic Continu ous Multi Medical diagnosis Partia l Stochas tic Episodic Stati c Continu ous Single Image analysis Fully Determi nistic Episodic Semi Discret e Single Robot part picking Fully Determi nistic Episodic Semi Discret e Single
Environment Examples Full par Det stoc Epi Stat Dis con Sin Environment Obse rvabl e Determ inistic Episodic Stati c Discret e Agent s Chess with a clock Fully Strategi c Sequenti al Semi Discret e Multi Chess without a clock Fully Strategi c Sequenti al Stati c Discret e Multi Poker Partia l Strategi c Sequenti al Stati c Discret e Multi Backgammon y observable vs. Fully Stochas tic Sequenti al Stati c Discret e Multi tially observable Taxi driving Partia l Stochas tic Sequenti al Dyn amic Continu ous Multi erministic vs. hastic / strategic Medical diagnosis Partia l Stochas tic Episodic Stati c Continu ous Single sodic vs. sequential Image analysis ic vs. dynamic Fully Determi nistic Episodic Semi Discret e Single crete vs. Robot part picking tinuous Fully Determi nistic Episodic Semi Discret e Single Interactive English gle agent vs. tutor
Environment Examples Fully observable vs. partially observable Deterministic vs. stochastic / strategic Episodic vs. sequential Static vs. dynamic Discrete vs. continuous Single agent vs. Environment Obse rvabl e Determ inistic Episodic Stati c Discret e Agent s Chess with a clock Fully Strategi c Sequenti al Semi Discret e Multi Chess without a clock Fully Strategi c Sequenti al Stati c Discret e Multi Poker Partia l Strategi c Sequenti al Stati c Discret e Multi Backgammon Fully Stochas tic Sequenti al Stati c Discret e Multi Taxi driving Partia l Stochas tic Sequenti al Dyn amic Continu ous Multi Medical diagnosis Partia l Stochas tic Episodic Stati c Continu ous Single Image analysis Fully Determi nistic Episodic Semi Discret e Single Robot part picking Fully Determi nistic Episodic Semi Discret e Single Interactive English tutor Partia l Stochas tic Sequenti al Dyn amic Discret e Multi
Agent Types  Types of agents (increasing in generality and ability to handle complex environments)  Simple reflex agents  Reflex agents with state  Goal-based agents  Utility-based agents  Learning agent
Simple Reflex Agent  Use simple “if then” rules  Can be short sighted SimpleReflexAgent(percept) state rule = InterpretInput(percept) = RuleMatch(state, rules) action = RuleAction(rule) Return action
Example: Vacuum Agent  Performance?  1 point for each square cleaned in time T?  #clean squares per time step - #moves per time step?  Environment: vacuum, dirt, multiple areas defined by square regions  Actions: left, right, suck, idle  Sensors: location and contents  [A, dirty]  Rational is not omniscient  Environment may be partially observable  Rational is not clairvoyant  Environment may be stochastic  Thus Rational is not always successful
Reflex Vacuum Agent  If status=Dirty then return Suck else if location=A then return Right else if location=B then right Left
Reflex Agent With State  Store previously- observed information  Can reason about unobserved aspects of current state ReflexAgentWithState(percept) state rule = UpdateDate(state,action,percept) = RuleMatch(state, rules) action = RuleAction(rule) Return action
Reflex Vacuum Agent  If status=Dirty then Suck else if have not visited other square in >3 time units, go there
Goal-Based Agents  Goal reflects desires of agents  May project actions to see if consistent with goals  Takes time, world may change during reasoning
Utility-Based Agents  Evaluation function to measure utility f(state) -> value  Useful for evaluating competing goals
Learning Agents
Xavier mail delivery robot  Performance: Completed tasks  Environment: See for yourself  Actuators: Wheeled robot actuation  Sensors: Vision, sonar, dead reckoning  Reasoning: Markov model induction, A* search, Bayes classification
Other Example AI Systems  Knowledge Representation  Search  Problem solving  Planning  Machine learning  Natural language processing  Uncertainty reasoning  Computer Vision  Robotics
Some AI videos  Note: there is a lot of AI that is not quite this “sexy” but still very valuable!  E.g. logistics planning – DARPA claims that savings from a single AI planning application during 1991 Persian Gulf crisis more than paid back for all of DARPA’s investment in AI, ever. [Russell and Norvig]  http://www.youtube.com/watch?v=1JJsBFiXGl0&feature=related  http://www.youtube.com/watch?v=ICgL1OWsn58&feature=relate d  http://www.cs.utexas.edu/~kdresner/aim/video/fcfs-insanity.mov  http://www.youtube.com/watch?v=HacG_FWWPOw&feature=rela ted  http://videolectures.net/aaai07_littman_ai/  http://www.ai.sri.com/~nysmith/videos/SRI_AR-PA_AAAI08.avi  http://www.youtube.com/watch?v=ScXX2bndGJc
Thank You!!! 60

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Unit 1 ppt.pptx

  • 1. 1 Introduction to Artificial Intelligence Third Year Computer Engineering Bhalke A.P.
  • 2. Content  Introduction to Artificial Intelligence,  Foundations of Artificial Intelligence,  History of Artificial Intelligence,  State of the Art,  Risks and Benefits of AI,  Intelligent Agents,  Agents and Environments,  Good Behavior: Concept of Rationality,  Nature of Environments, Structure of Agents. 2
  • 3. Why Study AI?  AI makes computers more useful  Intelligent computer would have huge impact on civilization  AI cited as “field I would most like to be in” by scientists in all fields  Computer is a good metaphor for talking and thinking about intelligence
  • 4. Why Study AI?  Turning theory into working programs forces us to work out the details  AI yields good results for Computer Science  AI yields good results for other fields  Computers make good experimental subjects  Personal motivation: mystery
  • 5. What is artificial intelligence?  Popular conception driven by science ficition  Robots good at everything except emotions, empathy, appreciation of art, culture, …  … until later in the movie.  Perhaps more representative of human autism than of (current?) real robotics/AI  “It is my belief that the existence of autism has contributed to [the theme of the intelligent but soulless automaton] in no small way.” [Uta Frith, “Autism”]  Current AI is also bad at lots of simpler stuff!  There is a lot of AI work on thinking about what others are thinking
  • 6. Real AI  A serious science.  General-purpose AI like the robots of science fiction is incredibly hard  Human brain appears to have lots of special and general functions, integrated in some amazing way that we really do not understand at all (yet)  Special-purpose AI is more doable (nontrivial)  E.g., chess/poker playing programs, logistics planning, automated translation, voice recognition, web search, data mining, medical diagnosis, keeping a car on the road, … … … …
  • 7. Definitions of AI Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally focus on action avoids philosophical issues such as “is the system conscious” etc. • We will follow “act rationally” approach – Distinction may not be that important • acting rationally/like a human presumably requires (some sort of) thinking rationally/like a human, • humans much more rational anyway in complex domains if our system can be more rational than humans in some cases, why not?
  • 8. What is the definition of AI? Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally
  • 9. What is the definition of AI? Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally Bellman, 1978 “[The automation of] activities that we associate with human thinking, activities such as decision making, problem solving, learning”
  • 10. What is the definition of AI? Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally Charniak & McDermott, 1985 “The study of mental faculties through the use of computational models”
  • 11. What is the definition of AI? Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally Dean et al., 1995 “The design and study of computer programs that behave intelligently. These programs are constructed to perform as would a human or an animal whose behavior we consider intelligent”
  • 12. What is the definition of AI? Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally Haugeland, 1985 “The exciting new effort to make computers think machines with minds, in the full and literal sense”
  • 13. What is the definition of AI? Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally Kurzweil, 1990 “The art of creating machines that perform functions that require intelligence when performed by people”
  • 14. What is the definition of AI? Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally Luger & Stubblefield, 1993 “The branch of computer science that is concerned with the automation of intelligent behavior”
  • 15. What is the definition of AI? Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally Nilsson, 1998 “Many human mental activities such as writing computer programs, doing mathematics, engaging in common sense reasoning, understanding language, and even driving an automobile, are said to demand intelligence. We might say that [these systems] exhibit artificial intelligence”
  • 16. What is the definition of AI? Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally Rich & Knight, 1991 “The study of how to make computers do things at which, at the moment, people are better”
  • 17. What is the definition of AI? Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally Schalkoff, 1990 “A field of study that seeks to explain and emulate intelligent behavior in terms of computational processes”
  • 18. What is the definition of AI? Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally Winston, 1992 “The study of the computations that make it possible to perceive, reason, and act”
  • 19. 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
  • 20. 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)
  • 21. The Turing Test (Can Machine think? A. M. Turing, 1950)  Requires  Natural language  Knowledge representation  Automated reasoning  Machine learning  (vision, robotics) for full test
  • 22. 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
  • 23. 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
  • 24. 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)
  • 25. 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
  • 26. Abridged history of AI  1943  1950  1956  1952—69  1950s McCulloch & Pitts: Boolean circuit model of brain Turing's "Computing Machinery and Intelligence" Dartmouth meeting: "Artificial Intelligence" adopted Look, Ma, no hands! Early AI programs, including Samuel's checkers program, Newell & Simon's Logic Theorist, Gelernter's Geometry Engine Robinson's complete algorithm for logical reasoning AI discovers computational complexity Neural network research almost disappears Early development of knowledge-based systems AI becomes an industry Neural networks return to popularity AI becomes a science The emergence of intelligent agents  1965  1966—73  1969—79  1980--  1986--  1987--  1995--
  • 27. 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
  • 28. Components of an AI System An agent perceives its environment through sensors and acts on the environment through actuators. Human: sensors are eyes, ears, actuators (effectors) are hands, legs, mouth. Robot: sensors are cameras, sonar, lasers, ladar, bump, effectors are grippers, manipulators, motors The agent’s behavior is described by it function that maps percept to action.
  • 29. Rationality  A rational agent does the right thing (what is this?)  A fixed performance measure evaluates the sequence of observed action effects on the environment
  • 30. PEAS  Use PEAS to describe task  Performance measure  Environment  Actuators  Sensors
  • 31. PEAS  Use PEAS to describe task environment  Performance measure  Environment  Actuators  Sensors  Example: Taxi driver  Performance measure: safe, fast, comfortable (maximize profits)  Environment: roads, other traffic, pedestrians, customers  Actuators: steering, accelerator, brake, signal, horn  Sensors: cameras, sonar, speedometer, GPS, odometer, accelerometer, engine sensors
  • 32. Environment Properties  Fully observable vs. partially observable  Deterministic vs. stochastic / strategic  Episodic vs. sequential  Static vs. dynamic  Discrete vs. continuous  Single agent vs. multiagent
  • 33. Environment Examples Environment Obse rvabl e Determ inistic Episodic Stati c Discret e Agent s Chess with a clock Chess without a clock Fully observable vs. partially observable Deterministic vs. stochastic / strategic Episodic vs. sequential Static vs. dynamic Discrete vs. continuous Single agent vs. multiagent
  • 34. Environment Examples Environment Obse rvabl e Determ inistic Episodic Stati c Discret e Agent s Chess with a clock Fully Strategi c Sequenti al Semi Discret e Multi Chess without a clock Fully Strategi c Sequenti al Stati c Discret e Multi Fully observable vs. partially observable Deterministic vs. stochastic / strategic Episodic vs. sequential Static vs. dynamic Discrete vs. continuous Single agent vs. multiagent
  • 35. Environment Examples Fully observable vs. partially observable Deterministic vs. stochastic / strategic Episodic vs. sequential Static vs. dynamic Discrete vs. continuous Single agent vs. multiagent Environment Obse rvabl e Determ inistic Episodic Stati c Discret e Agent s Chess with a clock Fully Strategi c Sequenti al Semi Discret e Multi Chess without a clock Fully Strategi c Sequenti al Stati c Discret e Multi Poker
  • 36. Environment Examples Fully observable vs. partially observable Deterministic vs. stochastic / strategic Episodic vs. sequential Static vs. dynamic Discrete vs. continuous Single agent vs. multiagent Environment Obse rvabl e Determ inistic Episodic Stati c Discret e Agent s Chess with a clock Fully Strategi c Sequenti al Semi Discret e Multi Chess without a clock Fully Strategi c Sequenti al Stati c Discret e Multi Poker Partia l Strategi c Sequenti al Stati c Discret e Multi
  • 37. Environment Examples Fully observable vs. partially observable Deterministic vs. stochastic / strategic Episodic vs. sequential Static vs. dynamic Discrete vs. continuous Single agent vs. multiagent Environment Obse rvabl e Determ inistic Episodic Stati c Discret e Agent s Chess with a clock Fully Strategi c Sequenti al Semi Discret e Multi Chess without a clock Fully Strategi c Sequenti al Stati c Discret e Multi Poker Partia l Strategi c Sequenti al Stati c Discret e Multi Backgammon
  • 38. Environment Examples Fully observable vs. partially observable Deterministic vs. stochastic / strategic Episodic vs. sequential Static vs. dynamic Discrete vs. continuous Single agent vs. multiagent Environment Obse rvabl e Determ inistic Episodic Stati c Discret e Agent s Chess with a clock Fully Strategi c Sequenti al Semi Discret e Multi Chess without a clock Fully Strategi c Sequenti al Stati c Discret e Multi Poker Partia l Strategi c Sequenti al Stati c Discret e Multi Backgammon Fully Stochas tic Sequenti al Stati c Discret e Multi
  • 39. Environment Examples Fully observable vs. partially observable Deterministic vs. stochastic / strategic Episodic vs. sequential Static vs. dynamic Discrete vs. continuous Single agent vs. multiagent Environment Obse rvabl e Determ inistic Episodic Stati c Discret e Agent s Chess with a clock Fully Strategi c Sequenti al Semi Discret e Multi Chess without a clock Fully Strategi c Sequenti al Stati c Discret e Multi Poker Partia l Strategi c Sequenti al Stati c Discret e Multi Backgammon Fully Stochas tic Sequenti al Stati c Discret e Multi Taxi driving Partia l Stochas tic Sequenti al Dyn amic Continu ous Multi
  • 40. Environment Examples Fully observable vs. partially observable Deterministic vs. stochastic / strategic Episodic vs. sequential Static vs. dynamic Discrete vs. continuous Single agent vs. multiagent Environment Obse rvabl e Determ inistic Episodic Stati c Discret e Agent s Chess with a clock Fully Strategi c Sequenti al Semi Discret e Multi Chess without a clock Fully Strategi c Sequenti al Stati c Discret e Multi Poker Partia l Strategi c Sequenti al Stati c Discret e Multi Backgammon Fully Stochas tic Sequenti al Stati c Discret e Multi Taxi driving Partia l Stochas tic Sequenti al Dyn amic Continu ous Multi Medical diagnosis
  • 41. Environment Examples Fully observable vs. partially observable Deterministic vs. stochastic / strategic Episodic vs. sequential Static vs. dynamic Discrete vs. continuous Single agent vs. multiagent Environment Obse rvabl e Determ inistic Episodic Stati c Discret e Agent s Chess with a clock Fully Strategi c Sequenti al Semi Discret e Multi Chess without a clock Fully Strategi c Sequenti al Stati c Discret e Multi Poker Partia l Strategi c Sequenti al Stati c Discret e Multi Backgammon Fully Stochas tic Sequenti al Stati c Discret e Multi Taxi driving Partia l Stochas tic Sequenti al Dyn amic Continu ous Multi Medical diagnosis Partia l Stochas tic Episodic Stati c Continu ous Single
  • 42. Environment Examples ally observable ic / strategic Fully observable vs. parti Deterministic vs. stochast Episodic vs. sequential Static vs. dynamic Discrete vs. continuous Single agent vs. multiagent Environment Obse rvabl e Determ inistic Episodic Stati c Discret e Agent s Chess with a clock Fully Strategi c Sequenti al Semi Discret e Multi Chess without a clock Fully Strategi c Sequenti al Stati c Discret e Multi Poker Partia l Strategi c Sequenti al Stati c Discret e Multi Backgammon Fully Stochas tic Sequenti al Stati c Discret e Multi Taxi driving Partia l Stochas tic Sequenti al Dyn amic Continu ous Multi Medical diagnosis Partia l Stochas tic Episodic Stati c Continu ous Single Image analysis
  • 43. Environment Examples Environment Obse rvabl e Determ inistic Episodic Stati c Discret e Agent s Chess with a clock Fully Strategi c Sequenti al Semi Discret e Multi Chess without a clock Fully Strategi c Sequenti al Stati c Discret e Multi Poker Partia l Strategi c Sequenti al Stati c Discret e Multi Backgammon Fully Stochas tic Sequenti al Stati c Discret e Multi Taxi driving Partia l Stochas tic Sequenti al Dyn amic Continu ous Multi Medical diagnosis Partia l Stochas tic Episodic Stati c Continu ous Single Image analysis Fully Determi nistic Episodic Semi Discret e Single Fully observable vs. partially observable Deterministic vs. stochastic / strategic Episodic vs. sequential Static vs. dynamic Discrete vs. continuous Single agent vs.
  • 44. Environment Examples Fully observable vs. partially observable Deterministic vs. stochastic / strategic Episodic vs. sequential Static vs. dynamic Discrete vs. continuous Single agent vs. Environment Obse rvabl e Determ inistic Episodic Stati c Discret e Agent s Chess with a clock Fully Strategi c Sequenti al Semi Discret e Multi Chess without a clock Fully Strategi c Sequenti al Stati c Discret e Multi Poker Partia l Strategi c Sequenti al Stati c Discret e Multi Backgammon Fully Stochas tic Sequenti al Stati c Discret e Multi Taxi driving Partia l Stochas tic Sequenti al Dyn amic Continu ous Multi Medical diagnosis Partia l Stochas tic Episodic Stati c Continu ous Single Image analysis Fully Determi nistic Episodic Semi Discret e Single Robot part picking
  • 45. Environment Examples Fully observable vs. partially observable Deterministic vs. stochastic / strategic Episodic vs. sequential Static vs. dynamic Discrete vs. continuous Single agent vs. Environment Obse rvabl e Determ inistic Episodic Stati c Discret e Agent s Chess with a clock Fully Strategi c Sequenti al Semi Discret e Multi Chess without a clock Fully Strategi c Sequenti al Stati c Discret e Multi Poker Partia l Strategi c Sequenti al Stati c Discret e Multi Backgammon Fully Stochas tic Sequenti al Stati c Discret e Multi Taxi driving Partia l Stochas tic Sequenti al Dyn amic Continu ous Multi Medical diagnosis Partia l Stochas tic Episodic Stati c Continu ous Single Image analysis Fully Determi nistic Episodic Semi Discret e Single Robot part picking Fully Determi nistic Episodic Semi Discret e Single
  • 46. Environment Examples Full par Det stoc Epi Stat Dis con Sin Environment Obse rvabl e Determ inistic Episodic Stati c Discret e Agent s Chess with a clock Fully Strategi c Sequenti al Semi Discret e Multi Chess without a clock Fully Strategi c Sequenti al Stati c Discret e Multi Poker Partia l Strategi c Sequenti al Stati c Discret e Multi Backgammon y observable vs. Fully Stochas tic Sequenti al Stati c Discret e Multi tially observable Taxi driving Partia l Stochas tic Sequenti al Dyn amic Continu ous Multi erministic vs. hastic / strategic Medical diagnosis Partia l Stochas tic Episodic Stati c Continu ous Single sodic vs. sequential Image analysis ic vs. dynamic Fully Determi nistic Episodic Semi Discret e Single crete vs. Robot part picking tinuous Fully Determi nistic Episodic Semi Discret e Single Interactive English gle agent vs. tutor
  • 47. Environment Examples Fully observable vs. partially observable Deterministic vs. stochastic / strategic Episodic vs. sequential Static vs. dynamic Discrete vs. continuous Single agent vs. Environment Obse rvabl e Determ inistic Episodic Stati c Discret e Agent s Chess with a clock Fully Strategi c Sequenti al Semi Discret e Multi Chess without a clock Fully Strategi c Sequenti al Stati c Discret e Multi Poker Partia l Strategi c Sequenti al Stati c Discret e Multi Backgammon Fully Stochas tic Sequenti al Stati c Discret e Multi Taxi driving Partia l Stochas tic Sequenti al Dyn amic Continu ous Multi Medical diagnosis Partia l Stochas tic Episodic Stati c Continu ous Single Image analysis Fully Determi nistic Episodic Semi Discret e Single Robot part picking Fully Determi nistic Episodic Semi Discret e Single Interactive English tutor Partia l Stochas tic Sequenti al Dyn amic Discret e Multi
  • 48. Agent Types  Types of agents (increasing in generality and ability to handle complex environments)  Simple reflex agents  Reflex agents with state  Goal-based agents  Utility-based agents  Learning agent
  • 49. Simple Reflex Agent  Use simple “if then” rules  Can be short sighted SimpleReflexAgent(percept) state rule = InterpretInput(percept) = RuleMatch(state, rules) action = RuleAction(rule) Return action
  • 50. Example: Vacuum Agent  Performance?  1 point for each square cleaned in time T?  #clean squares per time step - #moves per time step?  Environment: vacuum, dirt, multiple areas defined by square regions  Actions: left, right, suck, idle  Sensors: location and contents  [A, dirty]  Rational is not omniscient  Environment may be partially observable  Rational is not clairvoyant  Environment may be stochastic  Thus Rational is not always successful
  • 51. Reflex Vacuum Agent  If status=Dirty then return Suck else if location=A then return Right else if location=B then right Left
  • 52. Reflex Agent With State  Store previously- observed information  Can reason about unobserved aspects of current state ReflexAgentWithState(percept) state rule = UpdateDate(state,action,percept) = RuleMatch(state, rules) action = RuleAction(rule) Return action
  • 53. Reflex Vacuum Agent  If status=Dirty then Suck else if have not visited other square in >3 time units, go there
  • 54. Goal-Based Agents  Goal reflects desires of agents  May project actions to see if consistent with goals  Takes time, world may change during reasoning
  • 55. Utility-Based Agents  Evaluation function to measure utility f(state) -> value  Useful for evaluating competing goals
  • 57. Xavier mail delivery robot  Performance: Completed tasks  Environment: See for yourself  Actuators: Wheeled robot actuation  Sensors: Vision, sonar, dead reckoning  Reasoning: Markov model induction, A* search, Bayes classification
  • 58. Other Example AI Systems  Knowledge Representation  Search  Problem solving  Planning  Machine learning  Natural language processing  Uncertainty reasoning  Computer Vision  Robotics
  • 59. Some AI videos  Note: there is a lot of AI that is not quite this “sexy” but still very valuable!  E.g. logistics planning – DARPA claims that savings from a single AI planning application during 1991 Persian Gulf crisis more than paid back for all of DARPA’s investment in AI, ever. [Russell and Norvig]  http://www.youtube.com/watch?v=1JJsBFiXGl0&feature=related  http://www.youtube.com/watch?v=ICgL1OWsn58&feature=relate d  http://www.cs.utexas.edu/~kdresner/aim/video/fcfs-insanity.mov  http://www.youtube.com/watch?v=HacG_FWWPOw&feature=rela ted  http://videolectures.net/aaai07_littman_ai/  http://www.ai.sri.com/~nysmith/videos/SRI_AR-PA_AAAI08.avi  http://www.youtube.com/watch?v=ScXX2bndGJc