CS440 Introduction to Artificial Intelligence Fall 2003

Today’s topics Course administration. What is AI? AI and … –Cognitive science, philosophy, psychology, economics, computer science, control theory, … History of AI. Applications of AI. Reading: –This week: AIMA, Ch. 1 –Next week: AIMA, Ch. 2 & 3

Course administration Instructor Vladimir Pavlovic Office: 312 CoRE Web: Phone: Office hours: Mon, 3:00-4:00 TA Zhi Wei Office: 416 Hill Phone: Office hours: Thu, 2:00-4:00 PM Web site Mailing list

Course administration (cont’d) Lectures:Mon & Wed, 4:30 – 5:50 Discussion:Wed, 6:35 – 7:30 Classroom:Arc-105 Textbook: Russell & Norvig, "Artificial Intelligence: A Modern Approach", 2nd Edition, Prentice Hall, Also referred to as AIMA Prerequisites: CS314 (Principles of Programming Languages). You also need a solid knowledge of calculus. Some knowledge of probability and linear algebra will be beneficial.

Course administration (cont’d) Grading Homework30% Midterm30% Final40% Homework assignments –Weekly, will include programming problems (mini projects). –Programming in Java / Matlab (Lush? Lisp?) –Assignments are due in class, on due date. –No late homeworks accepted! Tests –Midterm, around Oct. 20 –Final –Closed book, closed notes

What is AI? What is intelligence? –“The capacity to learn and solve problems” [Webster dictionary] –“The computational part of the ability to achieve goals in the world. Varying kinds and degrees of intelligence occur in people, many animals and some machines.” [McCarthy] & Alice Bot ( –Ability to think and act rationally. What are “ingredients” of intelligence?

“Ingredients” of intelligence Ability to interact with real world –Perceive, understand, act. –Language understanding and formation. –Visual perception. Reasoning and planning –Modeling external world –Problem solving, planning, decision making –Ability to deal with unexpected problems, dealing with uncertainty

“Ingredients” of intelligence (cont’d) Learning and adaptation –Continuous update of our model of the world and adaptation to it

What is AI? A field that focuses on developing techniques to enable computer systems to perform activities that are considered intelligent (in humans and other animals). [Dyer] The science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable. [McCarthy] The study of how to make computer do things which, at the moment, people do better. [Rich&Knight] The design and study of computer programs that behave intelligently. [Dean, Allen, & Aloimonos] The study of [rational] agents that exist in an environment and perceive and act. [Russell&Norvig]

Goals of AI Scientific and engineering –Understanding of computational mechanisms needed for intelligent behavior –Intelligent connection of perception and action –Replicate human intelligence –Solve knowledge-intensive tasks –Enhance human-human, human-computer and computer-computer interaction/communication

Some applications of AI Game Playing Deep Blue Chess program beat world champion Gary Kasparov Speech Recognition PEGASUS spoken language interface to American Airlines' EAASY SABRE reseration system, which allows users to obtain flight information and make reservations over the telephone. The 1990s has seen significant advances in speech recognition so that limited systems are now successful. Computer Vision Face recognition programs in use by banks, government, etc. The ALVINN system from CMU autonomously drove a van from Washington, D.C. to San Diego (all but 52 of 2,849 miles), averaging 63 mph day and night, and in all weather conditions. Handwriting recognition, electronics and manufacturing inspection, photointerpretation, baggage inspection, reverse engineering to automatically construct a 3D geometric model. Expert Systems Application-specific systems that rely on obtaining the knowledge of human experts in an area and programming that knowledge into a system. Diagnostic Systems Microsoft Office Assistant in Office 97 provides customized help by decision-theoretic reasoning about an individual user. MYCIN system for diagnosing bacterial infections of the blood and suggesting treatments. Intellipath pathology diagnosis system (AMA approved). Pathfinder medical diagnosis system, which suggests tests and makes diagnoses. Whirlpool customer assistance center.

Some applications of AI (cont’d) Financial Decision Making Credit card companies, mortgage companies, banks, and the U.S. government employ AI systems to detect fraud and expedite financial transactions. For example, AMEX credit check. Systems often use learning algorithms to construct profiles of customer usage patterns, and then use these profiles to detect unusual patterns and take appropriate action. Classification Systems Put information into one of a fixed set of categories using several sources of information. E.g., financial decision making systems. NASA developed a system for classifying very faint areas in astronomical images into either stars or galaxies with very high accuracy by learning from human experts' classifications. Mathematical Theorem Proving Use inference methods to prove new theorems. Natural Language Understanding Google's translation of web pages. Translation of Catepillar Truck manuals into 20 languages. (Note: One early system translated the English sentence "The spirit is willing but the flesh is weak" into the Russian equivalent of "The vodka is good but the meat is rotten.") Scheduling and Planning Automatic scheduling for manufacturing. DARPA's DART system used in Desert Storm and Desert Shield operations to plan logistics of people and supplies. American Airlines rerouting contingency planner. European space agency planning and scheduling of spacecraft assembly, integration and verification. Robotics and Path planning NASA’s Rover mission. Biology and medicine Modeling of cellular functions, analysis of DNA and proteins. and…

Roomba! Roomba’s (artificial) intelligence fits in 256 bytes of program space!

Turing test ( A. Turing, “Computing machinery and intelligence”, 1950) Interrogator asks questions of two “people” who are out of sight and hearing. One is a human, the other one a machine. 30mins to ask whatever she/he wants. To determine only through questions and answers which is which. If it cannot distinguish between human and computer, the machine has passed the test! Predicted that in 2000 a machine would have 30% chance of fooling a lay person for 5min. Suggested major components of AI (knowledge, reasoning, language understanding, learning) Anticipated arguments against AI in 50 years to follow

Problems with Turing test Newel and Simon –As much a test of the judge as of the machine. –Promotes artificial con-artists, not intelligence ( Loebner prize,

Fundamental Issues for most AI problems Representation Facts about the world have to be represented in some way, e.g., mathematical logic is one language that is used in AI. Deals with the questions of what to represent and how to represent it. How to structure knowledge? What is explicit, and what must be inferred? How to encode "rules" for inferencing so as to find information that is only implicitly known? How to deal with incomplete, inconsistent, and probabilistic knowledge? Epistemology issues (what kinds of knowledge are required to solve problems). Search Many tasks can be viewed as searching a very large problem space for a solution. For example, Checkers has about 1040 states, and Chess has about states in a typical games. Use of heuristics (meaning "serving to aid discovery") and constraints. Inference From some facts others can be inferred. Related to search. For example, knowing "All elephants have trunks" and "Clyde is an elephant," can we answer the question "Does Clyde hae a trunk?" What about "Peanuts has a trunk, is it an elephant?" Or "Peanuts lives in a tree and has a trunk, is it an elephant?" Deduction, abduction, non-monotonic reasoning, reasoning under uncertainty. Learning Inductive inference, neural networks, genetic algorithms, artificial life, evolutionary approaches. Planning Starting with general facts about the world, facts about the effects of basic actions, facts about a particular situation, and a statement of a goal, generate a strategy for achieving that goals in terms of a sequence of primitive steps or actions.

Design methodology and goals Focus not just on behavior and I/O, look at reasoning process. Computational model should reflect "how" results were obtained. GPS (General Problem Solver): Goal not just to produce humanlike behavior (like ELIZA), but to produce a sequence of steps of the reasoning process that was similar to the steps followed by a person in solving the same task. Think like humans "cognitive science" Ex. GPS Think rationally => formalize inference process "laws of thought" Act like humans Ex. ELIZA Turing Test Act rationally "satisficing" methods HumanRational Think Act Formalize the reasoning process, producing a system that contains logical inference mechanisms that are provably correct, and guarantee finding an optimal solution. This brings up the question: How do we represent information that will allow us to do inferences like the following one? "Socrates is a man. All men are mortal. Therefore Socrates is mortal." -- Aristotle Behaviorist approach. Not interested in how you get results, just the similarity to what human results are. ELIZA: A program that simulated a psychotherapist interacting with a patient and successfully passed the Turing Test. For a given set of inputs, tries to generate an appropriate output that is not necessarily correct but gets the job done. Rational and sufficient ("satisficing" methods, not "optimal").

Brief history of AI 1943McCulloch & Pitts: Boolean circuit model of brain 1950Turing's ``Computing Machinery and Intelligence'' Look, Ma, no hands! 1950sEarly AI programs, including Samuel's checkers program, Newell & Simon's Logic Theorist, Gelernter's Geometry Engine 1956Dartmouth meeting: ``Artificial Intelligence'' adopted 1965Robinson's complete algorithm for logical reasoning AI discovers computational complexity and 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 1988Resurgence of probability; general increase in technical depth and ``Nouvelle AI'': ALife, GAs, soft computing 1995-Agents agents everywhere…

This course Search, Knowledge representation, Planning, Uncertainty, Learning, and Examples and applications in speech and language modeling, visual perception, medical informatics, and robotics.