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Intelligent Systems 1 Lecture 1 Lecture 1 1 Outline ♦ What are Intelligent Systems? ♦ A brief history ♦ The state of the art ♦ Agents and environments ♦ Rationality ♦ PEAS (Performance measure, Environment, Actuators, Sensors) ♦ Environment types ♦ Agent types Lecture 1 2 Administrivia Find the ourse home page via my webpage http://www.iiitd.edu.in/∼ashwin for lecture slides, practicals, announcements, etc. Books: Russell and Norvig Artificial Intelligence: A Modern Approach (2nd or 3rd ed) Bratko Prolog Programming for Artificial Intelligence (3rd or 4th ed) Slides: As much as possible, I will be following Stuart Russell’s slides Office Hour: Thurs 2-3. Lecture 1 3 Lectures, Projects and Practicals Lectures: Basic Concepts ♦ ♦ ♦ ♦ ♦ Rational agents Problem solving and search Representation and reasoning Decision making Modelling and Learning Videos: Then and Now ♦ The Lighthill Debate ♦ Watson and Beyond Presentation Themes: Contemporary AI Lecture 1 4 ♦ ♦ ♦ ♦ Game Playing Robotics Ubiquitous AI Smart Tools Practicals ♦ ♦ ♦ ♦ ♦ ♦ Introduction to Prolog Propositional Logic First-order logic Agents Search Learning Lecture 1 5 Assessment ♦ ♦ ♦ ♦ ♦ Mid-semester: 30% Final exam: 30% Lab work: 20% Paper-reading: 10% Presentations: 10% ♦ Lab work will be assessed by a separate set of questions in the final exam paper. ♦ A classic paper will be given to you. Questions on the paper will be given to you in Week 3. Your answers will be due back in Week 8. ♦ Questions on the paper may appear in the mid-semester exam. Questions on the video content may appear in the final exam. ♦ Presentations will be in groups. Assessment will be a combination of peer-review (5%) and my assessment (5%) Lecture 1 6 ♦ In Week 6, I will give you a number of topics in each Presentation Theme. You will form groups (no more than 3) and prepare a presentation on a topic of your choice (Weeks 11 and 12). Your presentation will be marked by other groups and by me. Lecture 1 7 Introduction AIMA: Chapters 1,2 for today’s material Bratko: Begin work on Chapters 1-5 Lecture 1 8 What are Intelligent Systems? Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally Views of Intelligent Systems or AI fall into four categories: Thinking humanly Thinking rationally Acting humanly Acting rationally Examining these, we will plump for acting rationally (sort of) Lecture 1 9 Acting humanly: The Turing test Turing (1950) “Computing machinery and intelligence”: ♦ “Can machines think?” −→ “Can machines behave intelligently?” ♦ Operational test for intelligent behavior: the Imitation Game HUMAN HUMAN INTERROGATOR ? AI SYSTEM ♦ 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 50 years ♦ Suggested major components: knowledge, reasoning, language understanding, learning ♦ Your paper-reading for this term will be Turing’s paper Lecture 1 10 Thinking humanly: Cognitive Science 1960s “cognitive revolution”: information-processing psychology replaced prevailing orthodoxy of behaviorism Requires scientific theories of internal activities of the brain – What level of abstraction? “Knowledge” or “circuits”? – How to validate? Requires 1) Predicting and testing behavior of human subjects or 2) Direct identification from neurological data Both approaches (roughly, Cognitive Science and Cognitive Neuroscience) are now distinct from AI Both share with AI the following characteristic: the available theories do not explain (or engender) anything resembling human-level general intelligence Lecture 1 11 Thinking rationally: Laws of Thought Normative (or prescriptive) rather than descriptive 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: 1) Not all intelligent behavior is mediated by logical deliberation 2) What is the purpose of thinking? What thoughts should I have out of all the thoughts (logical or otherwise) that I could have? Lecture 1 12 Acting rationally 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 Aristotle (Nicomachean Ethics): Every art and every inquiry, and similarly every action and pursuit, is thought to aim at some good Lecture 1 13 Rational agents An agent is an entity that perceives and acts This course is about 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 Lecture 1 14 AI prehistory Lecture 1 15 Philosophy logic, methods of reasoning mind as physical system foundations of learning, language, rationality Mathematics formal representation and proof algorithms, computation, (un)decidability, (in)tractability probability Psychology adaptation phenomena of perception and motor control experimental techniques (psychophysics, etc.) Economics formal theory of rational decisions Linguistics knowledge representation grammar Neuroscience plastic physical substrate for mental activity Control theory homeostatic systems, stability simple optimal agent designs Lecture 1 16 Potted history of AI Lecture 1 17 1943 1950 1952–69 1950s 1956 1965 1966–74 1969–79 1980–88 1988–93 1985–95 1988– 1995– 2003– 2008– McCulloch & Pitts: Boolean circuit model of brain Turing’s “Computing Machinery and Intelligence” Look, Ma, no hands! Early AI programs, including Samuel’s checkers program, Newell & Simon’s Logic Theorist, Gelernter’s Geometry Engine Dartmouth meeting: “Artificial Intelligence” adopted Robinson’s complete algorithm for logical reasoning AI discovers computational complexity Neural network research almost disappears Early development of knowledge-based systems Expert systems industry booms Expert systems industry busts: “AI Winter” Neural networks return to popularity Resurgence of probability; general increase in technical depth “Nouvelle AI”: ALife, GAs, soft computing Agents, agents, everywhere . . . Human-level AI back on the agenda Statistical, data-driven computational systems Lecture 1 18 State of the art Which of the following can be done at present? ♦ Play a decent game of table tennis Lecture 1 19 State of the art Which of the following can be done at present? ♦ Play a decent game of table tennis ♦ Drive safely along a curving mountain road Lecture 1 20 State of the art Which of the following can be done at present? ♦ Play a decent game of table tennis ♦ Drive safely along a curving mountain road ♦ Drive safely along a Delhi road Lecture 1 21 State of the art Which of the following can be done at present? ♦ ♦ ♦ ♦ Play a decent game of table tennis Drive safely along a curving mountain road Drive safely along a Delhi road Buy a week’s worth of groceries on the web Lecture 1 22 State of the art Which of the following can be done at present? ♦ ♦ ♦ ♦ ♦ Play a decent game of table tennis Drive safely along a curving mountain road Drive safely along a Delhi road Buy a week’s worth of groceries on the web Play a decent game of chess Lecture 1 23 State of the art Which of the following can be done at present? ♦ ♦ ♦ ♦ ♦ ♦ Play a decent game of table tennis Drive safely along a curving mountain road Drive safely along a Delhi road Buy a week’s worth of groceries on the web Play a decent game of chess Discover and prove a new mathematical theorem Lecture 1 24 State of the art Which of the following can be done at present? ♦ ♦ ♦ ♦ ♦ ♦ ♦ Play a decent game of table tennis Drive safely along a curving mountain road Drive safely along a Delhi road Buy a week’s worth of groceries on the web Play a decent game of chess Discover and prove a new mathematical theorem Design and execute a research program in molecular biology Lecture 1 25 State of the art Which of the following can be done at present? ♦ ♦ ♦ ♦ ♦ ♦ ♦ ♦ Play a decent game of table tennis Drive safely along a curving mountain road Drive safely along a Delhi road Buy a week’s worth of groceries on the web Play a decent game of chess Discover and prove a new mathematical theorem Design and execute a research program in molecular biology Write an intentionally funny story Lecture 1 26 State of the art Which of the following can be done at present? ♦ ♦ ♦ ♦ ♦ ♦ ♦ ♦ ♦ Play a decent game of table tennis Drive safely along a curving mountain road Drive safely along a Delhi road Buy a week’s worth of groceries on the web Play a decent game of chess Discover and prove a new mathematical theorem Design and execute a research program in molecular biology Write an intentionally funny story Give competent legal advice in a specialized area of law Lecture 1 27 State of the art Which of the following can be done at present? ♦ ♦ ♦ ♦ ♦ ♦ ♦ ♦ ♦ ♦ Play a decent game of table tennis Drive safely along a curving mountain road Drive safely along a Delhi road Buy a week’s worth of groceries on the web Play a decent game of chess Discover and prove a new mathematical theorem Design and execute a research program in molecular biology Write an intentionally funny story Give competent legal advice in a specialized area of law Translate spoken English into spoken Swedish in real time Lecture 1 28 State of the art Which of the following can be done at present? ♦ ♦ ♦ ♦ ♦ ♦ ♦ ♦ ♦ ♦ ♦ ♦ Play a decent game of table tennis Drive safely along a curving mountain road Drive safely along a Delhi road Buy a week’s worth of groceries on the web Buy a week’s worth of groceries at Berkeley Bowl Play a decent game of chess Discover and prove a new mathematical theorem Design and execute a research program in molecular biology Write an intentionally funny story Give competent legal advice in a specialized area of law Translate spoken English into spoken Swedish in real time Converse successfully with another person for an hour Lecture 1 29 State of the art Which of the following can be done at present? ♦ ♦ ♦ ♦ ♦ ♦ ♦ ♦ ♦ ♦ ♦ ♦ Play a decent game of table tennis Drive safely along a curving mountain road Drive safely along a Delhi road Buy a week’s worth of groceries on the web Play a decent game of chess Discover and prove a new mathematical theorem Design and execute a research program in molecular biology Write an intentionally funny story Give competent legal advice in a specialized area of law Translate spoken English into spoken Swedish in real time Converse successfully with another person for an hour Perform a complex surgical operation Lecture 1 30 State of the art Which of the following can be done at present? ♦ ♦ ♦ ♦ ♦ ♦ ♦ ♦ ♦ ♦ ♦ ♦ ♦ Play a decent game of table tennis Drive safely along a curving mountain road Drive safely along a straigh road Buy a week’s worth of groceries on the web Play a decent game of chess Discover and prove a new mathematical theorem Design and execute a research program in molecular biology Write an intentionally funny story Give competent legal advice in a specialized area of law Translate spoken English into spoken Swedish in real time Converse successfully with another person for an hour Perform a complex surgical operation Unload any dishwasher and put everything away Lecture 1 31 State of the art Which of the following can be done at present? ♦ ♦ ♦ ♦ ♦ ♦ ♦ ♦ ♦ ♦ ♦ ♦ ♦ Play a decent game of table tennis Drive safely along a curving mountain road Drive safely along a straigh road Buy a week’s worth of groceries on the web Play a decent game of chess Discover and prove a new mathematical theorem Design and execute a research program in molecular biology Write an intentionally funny story Give competent legal advice in a specialized area of law Translate spoken English into spoken Swedish in real time Converse successfully with another person for an hour Perform a complex surgical operation Unload any dishwasher and put everything away Lecture 1 32 Unintentionally funny stories One day Joe Bear was hungry. He asked his friend Irving Bird where some honey was. Irving told him there was a beehive in the oak tree. Joe threatened to hit Irving if he didn’t tell him where some honey was. The End. Henry Squirrel was thirsty. He walked over to the river bank where his good friend Bill Bird was sitting. Henry slipped and fell in the river. Gravity drowned. The End. Once upon a time there was a dishonest fox and a vain crow. One day the crow was sitting in his tree, holding a piece of cheese in his mouth. He noticed that he was holding the piece of cheese. He became hungry, and swallowed the cheese. The fox walked over to the crow. The End. Lecture 1 33 Unintentionally funny stories Joe Bear was hungry. He asked Irving Bird where some honey was. Irving refused to tell him, so Joe offered to bring him a worm if he’d tell him where some honey was. Irving agreed. But Joe didn’t know where any worms were, so he asked Irving, who refused to say. So Joe offered to bring him a worm if he’d tell him where a worm was. Irving agreed. But Joe didn’t know where any worms were, so he asked Irving, who refused to say. So Joe offered to bring him a worm if he’d tell him where a worm was . . . Lecture 1 34 Agents Agent Sensors Percepts Environment ? Actuators Actions Lecture 1 35 Agents and environments sensors percepts ? environment actions agent actuators Agents include humans, robots, softbots, thermostats, etc. The agent function maps from percept histories to actions: f : P∗ → A The agent program runs on the physical architecture to produce f Lecture 1 36 Vacuum-cleaner world A B Percepts: location and contents, e.g., [A, Dirty] Actions: Lef t, Right, Suck, N oOp Lecture 1 37 A vacuum-cleaner agent Percept sequence [A, Clean] [A, Dirty] [B, Clean] [B, Dirty] [A, Clean], [A, Clean] [A, Clean], [A, Dirty] .. Action Right Suck Lef t Suck Right Suck .. function Reflex-Vacuum-Agent( [location,status]) returns an action if status = Dirty then return Suck else if location = A then return Right else if location = B then return Left Lecture 1 38 What is the right function? Can it be implemented in a small agent program? Lecture 1 39 Rationality Fixed performance measure evaluates the environment sequence – one point per square cleaned up in time T ? – one point per clean square per time step, minus one per move? – penalize for > k dirty squares? A rational agent chooses whichever action maximizes the expected value of the performance measure given the percept sequence to date Rational 6= omniscient – percepts may not supply all relevant information Rational 6= clairvoyant – action outcomes may not be as expected Hence, rational 6= successful Rational ⇒ exploration, learning, autonomy Lecture 1 40 Designing an Intelligent System To design a rational agent, we must specify the task environment Consider, e.g., the task of designing an automated taxi: Performance measure?? Environment?? Actuators?? Sensors?? Lecture 1 41 PEAS To design a rational agent, we must specify the task environment Consider, e.g., the task of designing an automated taxi: Performance measure?? safety, destination, profits, legality, comfort, ... Environment?? Streets, traffic, pedestrians, potholes, weather, . . . Actuators?? steering, accelerator, brake, horn, speaker/display, . . . Sensors?? video, accelerometers, gauges, engine sensors, keyboard, GPS, . . . Lecture 1 42 Internet shopping agent Performance measure?? Environment?? Actuators?? Sensors?? Lecture 1 43 Internet shopping agent Performance measure?? price, quality, appropriateness, efficiency Environment?? current and future WWW sites, vendors, shippers Actuators?? display to user, follow URL, fill in form Sensors?? HTML pages (text, graphics, scripts) Lecture 1 44 Environment types Solitaire Backgammon Internet shopping Taxi Observable?? Deterministic?? Episodic?? Static?? Discrete?? Single-agent?? Lecture 1 45 Environment types Observable?? Deterministic?? Episodic?? Static?? Discrete?? Single-agent?? Solitaire Backgammon Internet shopping Taxi Yes Yes No No Lecture 1 46 Environment types Observable?? Deterministic?? Episodic?? Static?? Discrete?? Single-agent?? Solitaire Backgammon Internet shopping Taxi Yes Yes No No Yes No Partly No Lecture 1 47 Environment types Observable?? Deterministic?? Episodic?? Static?? Discrete?? Single-agent?? Solitaire Backgammon Internet shopping Yes Yes No Yes No Partly No No No Taxi No No No Lecture 1 48 Environment types Observable?? Deterministic?? Episodic?? Static?? Discrete?? Single-agent?? Solitaire Backgammon Internet shopping Yes Yes No Yes No Partly No No No Yes Semi Semi Taxi No No No No Lecture 1 49 Environment types Observable?? Deterministic?? Episodic?? Static?? Discrete?? Single-agent?? Solitaire Backgammon Internet shopping Yes Yes No Yes No Partly No No No Yes Semi Semi Yes Yes Yes Taxi No No No No No Lecture 1 50 Environment types Observable?? Deterministic?? Episodic?? Static?? Discrete?? Single-agent?? Solitaire Backgammon Internet shopping Yes Yes No Yes No Partly No No No Yes Semi Semi Yes Yes Yes Yes No Yes (except auctions) Taxi No No No No No No The environment type largely determines the agent design The real world is (of course) partially observable, stochastic, sequential, dynamic, continuous, multi-agent Lecture 1 51 Agent types Four basic types in order of increasing generality: – simple reflex agents – reflex agents with state – goal-based agents – utility-based agents All these can be turned into learning agents Lecture 1 52 Simple reflex agents Agent Sensors Condition−action rules What action I should do now Environment What the world is like now Actuators Lecture 1 53 Example function Reflex-Vacuum-Agent( [location,status]) returns an action if status = Dirty then return Suck else if location = A then return Right else if location = B then return Left Lecture 1 54 Reflex agents with state Sensors State How the world evolves What my actions do Condition−action rules Agent What action I should do now Environment What the world is like now Actuators Lecture 1 55 Example function Reflex-Vacuum-Agent( [location,status]) returns an action static: last A, last B, numbers, initially ∞ if status = Dirty then . . . Lecture 1 56 Goal-based agents Sensors State What the world is like now What my actions do What it will be like if I do action A Goals What action I should do now Agent Environment How the world evolves Actuators Lecture 1 57 Utility-based agents Sensors State What the world is like now What my actions do What it will be like if I do action A Utility How happy I will be in such a state What action I should do now Agent Environment How the world evolves Actuators Lecture 1 58 Learning agents Performance standard Sensors Critic changes Learning element knowledge Performance element learning goals Environment feedback Problem generator Agent Actuators Lecture 1 59 Summary Agents interact with environments through actuators and sensors The agent function describes what the agent does in all circumstances The performance measure evaluates the environment sequence A perfectly rational agent maximizes expected performance Agent programs implement (some) agent functions PEAS descriptions define task environments Environments are categorized along several dimensions: observable? deterministic? episodic? static? discrete? singleagent? Several basic agent architectures exist: reflex, reflex with state, goal-based, utility-based Lecture 1 60
