INFO 372: Explorations in Artificial Intelligence Prof. Carla P. Gomes gomes@cs.cornell.edu Introduction

Overview of this Lecture Course Administration What is Artificial Intelligence? Course Themes, Goals, and Syllabus

Course Administration

Artificial Intelligence INFO372 – Explorations in Artificial Intelligence Lectures: Tuesday and Thursday - 10:10 - 11:25 Location: Olin Hall, room 216 Lecturer: Prof. Gomes Office: 5133 Upson Hall Phone: 255 9189 Email: gomes@cs.cornell.edu Administrative Assistant: Beth Howard (bhoward@cs.cornell.edu)     5136 Upson Hall, 255-4188 TA: Robert Xiao rkx2@cornell.edu Web Site: www.cs.cornell.edu/gomes/COURSES/INFO372

Office Hours TA: Robert Xiao (TBA) 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 email. Wednesdays: 2:30p.m – 3:30 p.m.

Grades Midterm (25%) Homework (30%) Participation (5%) Final (40%) Note: The lowest homework grade will be dropped before the final grade is computed.

Homework Homework is very important. It is the best way for you to learn the material. Your lowest homework grade will be dropped before the final grade is computed. 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. No late homework will be accepted

Textbook Linear Programming by Vasek Chvatal Artificial Intelligence: A Modern Approach (AIMA) (Second Edition) by Stuart Russell and Peter Norvig Artificial Intelligence : A New Synthesis By Nils Nilsson Principles of Constraint Programming By Krzysztof Apt Linear Programming by Vasek Chvatal

Overview of this Lecture Course Administration What is Artificial Intelligence? Course Themes, Goals, and Syllabus

What is Artificial Intelligence (AI)? What is Intelligence? Historical Perspective of AI State-of-the-art and Challenges

What is AI? Ambitious goals: understand “intelligent” behavior build “intelligent” agents

What is Intelligence? Intelligence: “the capacity to learn and solve problems” (Webster dictionary) the ability to act rationally Artificial Intelligence: build and understand intelligent entities synergy between: philosophy, psychology, and cognitive science computer science and engineering mathematics and physics

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., statistical modeling, continuous mathematics, Markov models, statistical physics, and complex systems. and others, e.g., cognitive science, neuroscience, economics, psychology, linguistics, …

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

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. Alan Turing 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.

Acting humanly: Turing Test Alan Turing Turing (1950) "Computing machinery and intelligence": "Can machines think?"  "Can machines behave intelligently?" Operational test for intelligent behavior: the Imitation Game Predicted that by 2000, a machine might have a 30% chance of fooling a lay person for 5 minutes Anticipated all major arguments against AI in following 50 years Suggested major components of AI: knowledge, reasoning, natural language understanding, learning AI system passes if interrogator cannot tell which one is the machine

Some Famous Imitation Games 1960s ELIZA Joseph Weizenbaum Rogerian psychotherapist 1990s ALICE Loebner prize win $100,000 if you pass the test

ELIZA: impersonating a Rogerian psychotherapist 1960s ELIZA Joseph Weizenbaum http://www-ai.ijs.si/eliza/eliza.html 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.

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, 1995. 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 2000. The ALICE program was ranked "most human computer" by the panel of judges. 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.

Acting humanly: Turing Test Turing test  identified key research areas in AI: 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?

Other perspectives on Intelligence Thinking humanly: cognitive modeling Requires scientific theories of internal activities of the brain; How to validate? 1) Cognitive Science (top-down)  Predicting and testing behavior of human subjects – computer models + experimental techniques from psychology 2) Cognitive Neuroscience (bottom-up)  Direct identification from neurological data Thinking rationally: "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 Acting rationally: rational agent Rational behavior: doing 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; Always doing the right thing  sometimes not feasible in complex environments  Computational demands can be too high!

What is AI? Thinking humanly Thinking Rationally Acting Humanly Human-like Intelligence “Ideal” Intelligent/ Rationally Thinking humanly Thinking Rationally Acting Humanly Thought/ Reasoning A system is intelligent if it does the right thing given what it knows. Behavior/ Actions

Different Approaches 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 INFO372 (most recent progress).

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! INFO 372

Reasoning and Planning in AI State-of-the-art Reasoning and Planning in AI A few examples…

Robbin’s Algebras are all boolean 1996 - EQP: Robbin’s Algebras are all boolean A mathematical conjecture (Robbins conjecture) unsolved for decades 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. [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, 1996 http://www-unix.mcs.anl.gov/~mccune/papers/robbins/

Deep Blue beats the World Chess Champion 1997: Deep Blue beats the World Chess Champion vs. I could feel human-level intelligence across the room -Gary Kasparov, World Chess Champion (human…)

Deep Blue vs. Kasparov One of the most famous modern computers, 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! One of the most famous modern computers, Deep Blue, which defeated Gary Kasparov at chess.

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

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, 1999. 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.   A hundred million miles from Earth, NASA’s Remote Agent program became the first on-board autonomous planning program to control the scheduling of operations of the spacecraft. Remote Agent generated plans from high-level goals specified from the ground, and it monitored the operation of the spacecraft as the plans were executed – detecting, diagnosing, and recovering from problems ass they occurred http://ic.arc.nasa.gov/projects/remote-agent/index.html

2000: 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

better than most humans Proverb 1999: Solving Crossword Puzzles as Probabilistic Constraint Satisfaction Proverb solves crossword puzzles better than most humans Large database of past puzzles, a variety of information sources, including dictionaries and thesaurus, and online databases such as list of movies and actors, etc Michael Littman et a. 99

Robocup @ Cornell 199 http://www.mae.cornell.edu/raff/MultiAgentSystems/MultiAgentSystems.htm

2003 Robocup Italy

2005 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.

Course Themes, Goals, and Syllabus

Goals of INFO 372 Formalisms: Logical representations; Introduce the students to a range of computational modeling approaches and solution strategies using examples from AI and Information Science. Formalisms: Logical representations; Constraint-based languages, Mathematical programming; Multi-agent formalisms (including adversarial games); Solution strategies: Logical inference; General complete backtrack search; Local search; Dynamic Programming;

Goals of INFO 372 Special models: Satisfiability (SAT); Maximum SAT; Horn Constraint Satisfaction; Binary Constraint Satisfaction; Mixed Integer Programming, Linear Programming and Network Flow Models; Themes: Expressiveness and efficiency tradeoffs of the various representation formalisms Students learn about the tradeoffs in modeling choices.; Concrete examples to move from one representation modeling formalism to another formalism;

Summary Discussed Artificial Intelligence and characteristics of intelligent systems. Gave series of example systems, involving e.g. game playing, automated reasoning, and planning. Computers are getting smarter !!! Suggested Reading: Chapter 1 Russell & Norvig

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