Carla P. Gomes CS CS 4700: Foundations of Artificial Intelligence Carla P. Gomes Module: Introduction (Reading R&N: Chapter 1)

Carla P. Gomes CS Overview of this Lecture Course Administration What is Artificial Intelligence? Course Themes, Goals, and Syllabus

Carla P. Gomes CS Course Administration

Carla P. Gomes CS Lectures: Monday, Wednesday, Friday 1:115 – 12:05 Location: Phillips Hall, room 101 Lecturer: Prof. Gomes Office: 5133 Upson Hall Phone: Administrative Assistant: Kelly Duby Kelly Duby 4105 Upson Hall, Web Site: CS 4700: Foundations of Artificial Intelligence

Carla P. Gomes CS Head Teaching Assistants Yunsong Guo Anton Morozov Teaching Assistants Clayton Chang Sean Sullivan Web Site: CS 4700: Foundations of Artificial Intelligence

Carla P. Gomes CS Office Hours Prof. Gomes: Office: 5133 Upson Hall I prefer to meet during my scheduled office hours, however, if you need to meet with me at a different time please schedule an appointment by . TAs - TBA Fridays: 1:15p.m – 2:15 p.m. (starting next week)

Carla P. Gomes CS Grades Midterm (15%) Homework (45%) Participation (5%) Final (35%)

Carla P. Gomes CS Homework Homework is very important. It is the best way for you to learn the material. You are encouraged to discuss the problems with your classmates, but all work handed in should be original, written by you in your own words. Assignments turned in late will drop 5 points for each period of 24 hours for which the assignment is late. In addition, no assignments will be accepted after the solutions have been made available. No late homework will be accepted

Carla P. Gomes CS Mailing List Contact us by using this mailing list. The list is set to mail all the TA's and Prof. Gomes -- you will get the best response time by using this facility, and all the TA's will know the question you asked and the answers you receive.

Carla P. Gomes CS CS4701 Project (Optional) CS4700 is a co-requisite for CS473. There will be an organizational meeting in Hollister Hall room 110 on Tuesday, September 2nd at 3:35pm. The main assignment for CS4701 is a course project. Students will work in groups (probably pairs). A project proposal is required. A separate project handout with project suggestions, details, and due dates regarding the project proposal, and final project write-up will be made available from the course home page. Grading CS %: Project proposal 80%: Final code, write-up, and presentation CS 4701: Practicum in Artificial Intelligence (Optional)

Carla P. Gomes CS Textbook Artificial Intelligence: A Modern Approach (AIMA) (Second Edition) by Stuart Russell and Peter Norvig Artificial Intelligence : A New Synthesis By Nils Nilsson Required

Carla P. Gomes CS Lecture notes and reading material Optional reading material

Carla P. Gomes CS Welcome to this class! We will work together throughout this semester. Questions and suggestions are welcome anytime. –E.g., if you find anything incorrect or unclear, send an or talk to me. Any questions?

Carla P. Gomes CS Overview of this Lecture Course Administration What is Artificial Intelligence? Course Themes, Goals, and Syllabus

Carla P. Gomes CS AI: Goals Ambitious goals: –understand “intelligent” behavior –build “intelligent” agents

Carla P. Gomes CS What is Intelligence? Intelligence: –“the capacity to learn and solve problems” (Webster dictionary) –the ability to act rationally

Carla P. Gomes CS What is AI? 2.Thinking humanly3.Thinking Rationally 1.Acting Humanly 4.Acting Rationally Thought/ Reasoning Behavior/ Actions Human-like Intelligence “Ideal” Intelligent/ Rationally Views of AI fall into four different perspectives: Thinking versus Acting Human versus Rational

Carla P. Gomes CS Different AI Perspectives Human Thinking Human Acting Rational Thinking Rational Acting 1. Systems that act like humans 2. Systems that think like humans3. Systems that think rationally 4. Systems that act rationally

Carla P. Gomes CS Acting Humanly 2. Thinking humanly 3. Thinking Rationally 1. Acting Humanly  Turing Test 4. Acting Rationally Thought/ Reasoning Behavior/ Actions Human-like Intelligence “Ideal” Intelligent/ Rationally

Alan Turing In 1936, Alan Turing, a British mathematician, showed that there exists a relatively simple universal computing device that can perform any computational process. Computers use such a universal model. Turing Machine (abstraction) Turing also showed the limits of computation – some problems cannot be computed even with the most powerful computer and even with unlimited amount of time – e.g., Halting problem.

Carla P. Gomes CS Acting humanly: Turing Test Turing (1950) "Computing machinery and intelligence": "Can machines think?“ Instead, "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 all major arguments against AI in following 50 years Suggested major components of AI AI system passes if interrogator cannot tell which one is the machine Alan Turing (interaction via written questions)

Carla P. Gomes CS Turing Test Three rooms contain a person, a computer, and an interrogator. The interrogator can communicate with the other two by teleprinter. The interrogator tries to determine which is the person and which is the machine. The machine tries to fool the interrogator into believing that it is the person. If the machine succeeds, then we conclude that the machine can think.

Carla P. Gomes INFO372 Acting humanly: Turing Test Natural Language Processing – to communicate with the machine; Knowledge Representation – to store and manipulate information; Automated reasoning – to use the stored information to answer questions and draw new conclusions; Machine Learning – to adapt to new circumstances and to detect and extrapolate patterns. but does a machine need to act humanly to be considered intelligent? Turing test  identified key research areas in AI:

Carla P. Gomes INFO372 Some Famous Imitation Games 1960sELIZA Joseph Weizenbaum –Rogerian psychotherapist 1990s ALICE Loebner prize –win $100,000 if you pass the test

Carla P. Gomes INFO372 ELIZA: impersonating a Rogerian psychotherapist Eliza: a friend you could never have before Eliza: Hello. I am ELIZA. How can I help you? You: Well I feel sad Eliza: Do you often feel sad? You: not very often, but it's becoming more common Eliza: Please go on. 1960sELIZA Joseph Weizenbaum

Carla P. Gomes INFO372 Chat Bot – ALICE (AI Foundation) Human: hi my name is Carla ALICE: How are you doing, Carla. Human: what is your name? ALICE: My name is ALICE. Human: what is your full name? ALICE: ALICE = Artificial Linguistic Internet Computer Entity. Human: when were you born? ALICE: I was connected to the net on November 23, Human: Have you passed the Loebner Prize? ALICE: The Loebner Prize is an annual Turing Test sponsored by New York philanthropist Hugh Loebner. Dr. Wallace and ALICE won it in The ALICE program was ranked "most human computer" by the panel of judges.it Human: who is Dr. Wallace? ALICE: He is a famous computer scientist, the author of ALICE, and a graduate of Carnegie Mellon. Human: Where does Dr. Wallace work? ALICE: he works from his home in San Francisco.

Carla P. Gomes CS The Loebner contest A modern version of the Turing Test, held annually, with a $100,000 cash prize. Hugh Loebner was once director of UMBC’s Academic Computing Services (née UCS) Restricted topic (removed in 1995) and limited time. Participants include a set of humans and a set of computers and a set of judges. Scoring –Rank from least human to most human. –Highest median rank wins $2000. –If better than a human, win $100,000. (Nobody yet…)

Carla P. Gomes CS Thinking Humanly 2. Thinking humanly  Cognitive Modeling Thinking Rationally Acting Humanly  Turing Test Acting Rationally Thought/ Reasoning Behavior/ Actions Human-like Intelligence “Ideal” Intelligent/ Rationally

Carla P. Gomes INFO372 Thinking humanly: modeling cognitive processes Requires scientific theories of internal activities of the brain; 1) Cognitive Science (top-down) : computer models + experimental techniques from psychology  Predicting and testing behavior of human subjects 2) Cognitive Neuroscience (bottom-up) –  Direct identification from neurological data Both approaches are now distinct from AI 1960s "cognitive revolution": information-processing psychology

Carla P. Gomes CS Thinking Rationally Thinking humanly  Cognitive Modeling 3. Thinking Rationally  ”Laws of Thought” Acting Humanly  Turing Test Acting Rationally Thought/ Reasoning Behavior/ Actions Human-like Intelligence “Ideal” Intelligent/ Rationally

Carla P. Gomes CS Thinking rationally: formalizing the "laws of thought“ Logic  Making the right inferences! Several Greek schools developed various forms of logic: notation and rules of derivation for thoughts; Aristotle: what are correct arguments/thought processes? (characterization of “right thinking”); Socrates is a man All men are mortal Therefore Socrates is mortal More contemporary logicians (e.g. Boole, Frege, Tarski)  Direct line through mathematics and philosophy to modern AI Limitations:: Not all intelligent behavior is mediated by logical deliberation What is the purpose of thinking? What thoughts should I have?

Carla P. Gomes CS Course Perspective 4. Acting Rationally Thinking humanly  Cognitive Modeling 3. Thinking Rationally  ”Laws of Thought” Acting Humanly  Turing Test 4. Acting Rationally Thought/ Reasoning Behavior/ Actions Human-like Intelligence “Ideal” Intelligent/ Rationally

Carla P. Gomes CS Acting rationally: rational agent 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

Carla P. Gomes CS 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

Carla P. Gomes CS Building Intelligent Machines I Building exact models of human cognition view from psychology and cognitive science II Developing methods to match or exceed human performance in certain domains, possibly by very different means  e.g., Deep Blue; Focus of CS4700 (most recent progress).

Carla P. Gomes CS Methodology of AI Theoretical aspects –Mathematical formalizations, properties, algorithms Engineering aspects –The act of building (useful) machines Empirical science –Experiments

Carla P. Gomes CS What's involved in Intelligence? A) Ability to interact with the real world to perceive, understand, and act speech recognition and understanding image understanding (computer vision) B) Reasoning and Planning modelling the external world problem solving, planning, and decision making ability to deal with unexpected problems, uncertainties C) Learning and Adaptation We are continuously learning and adapting. We want systems that adapt to us! CS4700

Carla P. Gomes CS Historic Perspective

Carla P. Gomes CS AI Leverages from different disciplines Philosophy e.g., foundational issues in logic, methods of reasoning, mind as physical system, foundations of learning, language, rationality Computer science and engineering e.g., complexity theory, algorithms, logic and inference, programming languages, and system building (hardware and software). Mathematics and physics e.g., probability theory, statistical modeling, continuous mathematics, Markov models, statistical physics, and complex systems. and others, e.g., cognitive science, neuroscience, economics, psychology, linguistics,

Carla P. Gomes CS AI More direct Influence Obtaining an understanding of the human mind is one of the final frontiers of modern science. George Boole, Gottlob Frege, and Alfred Tarski formalizing the laws of human thought Alan Turing, John von Neumann, and Claude Shannon thinking as computation Direct Founders: John McCarthy, Marvin Minsky, Herbert Simon, and Allen Newell the start of the field of AI (1959)

Carla P. Gomes CS History of AI: Milestones The gestation of AI : McCulloch and Pitts –McCulloch and Pitts’s model of artificial neurons –Minsky’s 40-neuron network 1950 : Turing’s “Computing machinery and intelligence” 1950s Early AI programs, including Samuel’s checkers program, Newell and Simon’s Logic theorist 1956 Dartmouth meeting : Birth of “Artificial Intelligence” –A 2-month Dartmouth workshop of 10 attendees – the name of AI –Newell and Simon’s Logic Theorist –Do you think AI is a god name?

Carla P. Gomes INFO372 Perceptrons Early neural nets More about Neural Nets later in the course…

Carla P. Gomes INFO372 History of AI Look, Ma, no hands ! ( ) Early enthusiasm, great expectations 1957 Herb Simon: It is not my aim to surprise or shock you – but the simplest way I can summarize is to say that there are now in the world machines that think, that learn and that create. Moreover their ability to do these things is going to increase rapidly until – in the visible future – the range of problems that they can handle will be coextensive with the range to which human mind has been applied : John McCarthy’s LISP 1965 : J.A. Robinson invents the resolution principle, basis for automated theorem Intelligent reasoning in Microworlds (such as Block’s world)

Carla P. Gomes INFO372 The Block’s world T ABC D Initial State A C D Goal State

History of AI A dose of reality ( ) 1965 : Weizenbaum’s ELIZA Difficulties in automated translation ( try ) Syntax is not enough “the spirit is willing but the flesh is weak” “the vodka is good but the meat is rotten” Limitations of Perceptrons discovered  can only represent linearly separable functions Neural network research almost disappears  NP-Completeness (Cook 72) Intractability of the problems attempted by AI, Worst- case result….

Carla P. Gomes INFO372 History of AI Knowledge based systems ( ) Intelligence requires knowledge - Knowledge based systems as opposed to weak methods (general-purpose search methods)  Expert Systems, E.g.: –Mycin : diagnose blood infections –R1 : configuring computer systems

Carla P. Gomes INFO372 History of AI AI becomes industry ( ) Expert systems Lisp-machines Return of Neural Nets  End of 80’s – limitations of expert systems became clear, even though they have been quite successful in certain domains.

Carla P. Gomes INFO372 History of AI: AI is Alive and Kicking Current work on “intelligent agents”: Emphasis on integration of reasoning (search and inference as well as probabilistic reasoning), knowledge representation, and learning techniques AI as a science: Combining theoretical and empirical analysis  Mathematical sophistication of AI techniques “… A better understanding of the problems and their complexity properties, combined with increased mathematical sophistication, has led to workable research agendas and robust methods” R&N. Key challenge: building flexible and scalable AI systems in the Open World. AAAI08

Carla P. Gomes INFO372 A few recent examples… AI Achievements

Carla P. Gomes INFO EQP: Robbin’s Algebras are all boolean [An Argonne lab program] has come up with a major mathematical proof that would have been called creative if a human had thought of it. New York Times, December, A mathematical conjecture (Robbins conjecture) unsolved for 60 years! The Robbins problem was to determine whether one particular set of rules is powerful enough to capture all of the laws of Boolean algebra. One way to state the Robbins problem in mathematical terms is: Can the equation not(not(P))=P be derived from the following three equations? [1] P or Q = Q or P, [2] (P or Q) or R = P or (Q or R), [3] not(not(P or Q) or not(P or not(Q))) = P. First creative mathematical proof by computer: unlike brute-force based proofs such as the 4-color theorem.

Carla P. Gomes INFO372 Microsoft Office’97 + Answer Wizard Diagnosis reasoning using Bayesian Models Restricted NLP

Carla P. Gomes INFO : Deep Blue beats the World Chess Champion I could feel human-level intelligence across the room -Gary Kasparov, World Chess Champion (human…) vs.

Carla P. Gomes INFO372 Deep Blue vs. Kasparov One of the most famous modern computers, Deep Blue, which defeated Gary Kasparov at chess. Game 1: 5/3/97: Kasparov wins Game 2: 5/4/97: Deep Blue wins Game 3: 5/6/97: Draw Game 4: 5/7/97: Draw Game 5: 5/10/97: Draw Game 6: 5/11/97: Deep Blue wins “I felt a new kind of Intelligence” ( across the board from him) Kasparov 1997 The value of IBM’s stock Increased by $18 Billion! IBM Stock price skyrocketed on the day Deep Blue beat Kasparov

Carla P. Gomes INFO372 For two days in May, 1999, an AI Program called Remote Agent autonomously ran Deep Space 1 (some 60,000,000 miles from earth) 1999: Remote Agent takes Deep Space 1 on a galactic ride

Carla P. Gomes INFO372 Remote Agent: 1999 Winner of NASA's Software of the Year Award It's one small step in the history of space flight. But it was one giant leap for computer-kind, with a state of the art artificial intelligence system being given primary command of a spacecraft. Known as Remote Agent, the software operated NASA's Deep Space 1 spacecraft and its futuristic ion engine during two experiments that started on Monday, May 17, For two days Remote Agent ran on the on-board computer of Deep Space 1, more than 60,000,000 miles (96,500,000 kilometers) from Earth. The tests were a step toward robotic explorers of the 21st century that are less costly, more capable and more independent from ground control.

Carla P. Gomes INFO372 Proverb 1999: Solving Crossword Puzzles as Probabilistic Constraint Satisfaction Proverb solves crossword puzzles better than most humans Michael Littman et a. 99

Carla P. Gomes INFO : SCIFINANCE synthesizes programs for financial modeling Develop pricing models for complex derivative structures Involves the solution of a set of PDEs (partial differential equations) Integration of object-oriented design, symbolic algebra, and plan-based scheduling

Carla P. Gomes INFO372 Cornell Raff D’andrea

Carla P. Gomes INFO372 From Robocup to Warehouse Automation Raff D’Andrea First user of system

Carla P. Gomes INFO372 Machine learning successes Source: R. Greiner

Carla P. Gomes INFO372 Machine learning successes Source: R. Greiner

Carla P. Gomes INFO372 Machine learning successes Source: R. Greiner

Carla P. Gomes INFO Autonomous Control: DARPA GRAND CHALLENGE October 9, 2005 Stanley and the Stanford RacingTeam were awarded 2 million dollars for being the first team to complete the 132 mile DARPA Grand Challenge course (Mojave Desert). Stanley finished in just under 6 hours 54 minutes and averaged over 19 miles per hours on the course.

Carla P. Gomes INFO372

Carla P. Gomes INFO372 A* algorithm

Carla P. Gomes INFO Darpa Urban Challenge Winner: CMU Tartan Racing's Boss

Carla P. Gomes INFO372 The DARPA Urban Challenge is being held at the former George Air Force Base. The old base buildings are abandoned now and the Marines use the area to train for urban missions.

Carla P. Gomes INFO372 Where can you learn more about AI?

Main annual AI conference: AAAI Association for Advancement of AI

Carla P. Gomes INFO372 Association for Advancement of Artificial Intelligence (AAAI) AI Topics

Carla P. Gomes INFO372 Goals for this course

Carla P. Gomes INFO372 Setting expectations for this course Are we going to build real systems and robots? NO!!! Goal: Introduce the theoretical and computational techniques that serve as a foundation for the study of artificial intelligence (AI).

Carla P. Gomes INFO372 Syllabus Structure of intelligent agents and environments. Problem solving by search: principles of search, uninformed (“blind”) search, informed (“heuristic”) search, and local search. Constraint satisfaction problems: definition, search and inference, and study of structure. Adversarial search: games, optimal strategies, imperfect, real-time decisions. Logical agents: propositional and first order logic, knowledge bases and inference. Uncertainty and probabilistic reasoning: probability concepts, Bayesian networks, probabilistic reasoning over time, and decision making Learning: inductive learning, concept formation, decision tree learning, statistical approaches, neural networks, reinforcement learning

Carla P. Gomes INFO372 Notes The syllabus is quite ambitious: some of the topics may only be covered briefly, depending on time. Detailed reading information (chapters and sections of R&N) will be provided in the lectures notes and homework assignments. This is not a machine learning course: we will only cover some introductory material learning topics  if you are looking for a machine learning course, here is a specialized machine learning course offered this fall: CS Probabilistic Graphical Models.

Carla P. Gomes INFO372 Summary Artificial Intelligence and characteristics of intelligent systems. Brief history of AI Examples of AI achievements Computers are getting smarter !!! Reading: Chapter 1 Russell & Norvig

Carla P. Gomes INFO372 The End !