Artificial IntelligenceIntroduction Fall 2008 professor: Luigi Ceccaroni
Instructors • Luigi Ceccaroni • Omega building - Office 111 • email@example.com • Núria Castell Ariño • FIB building - Second floor • firstname.lastname@example.org
Course description • This course introduces: • Representations • Techniques • Architectures • This course also explores applications of: • Rule chaining • Heuristic search • Constraint propagation • Constrained search • Decision trees • Knowledge representation • Knowledge-based systems • Natural-language processing • It accounts for 7.2 credits of work load, distributed as: • 3.6 credits for theory • 2.4 for recitations • 1.2 for laboratory
Web pages • http://www.lsi.upc.es/~bejar/ia/ia.html • http://www.lsi.upc.edu/~luigi/MTI/AI-2008-fall/ai.html • http://raco.fib.upc.es/
Background • Students need the following knowledge (at the undergraduate level) to appropriately follow the course: • English language • Propositional and predicate logic; capacity to formulate a problem in logical terms • Logical inference; strategies of resolution; capacity to solve problems by resolution • Graph and tree structures; algorithms for search in trees and graphs • Computational complexity; calculation of algorithm execution's cost • There are assignments that expect students to be able to read and write basic Java. This is the only formal pre-requisite.
Aim of the course • The general objectives of the course can be summarized as: • To identify the kind of problems that can be solved using AI techniques; to know the relation between AI and other areas of computer science. • To have knowledge of generic problem-solving methods in AI. • To understand the role of knowledge in present IA; to know the basic techniques of knowledge representation and their use. • To be able to apply basic AI techniques as support for the solution of practical problems. • To be able to navigate the basic bibliography of AI.
Topics • [ 1.] Search • [1.1] Problem representation • [1.2] Search in state space • [1.3] Uninformed search • [1.4] Informed search (A*,IDA*, local search) • [1.5] Games • [1.6] Constraint satisfaction
Topics • [2.] Knowledge representation and inference • [2.1] Methodologies for knowledge representation • [2.2] Rule-based systems • [2.3] Structured representations: frames and ontologies
Topics • [3.] Knowledge-based systems • [3.1] Definition and architecture • [3.2] Expert systems • [3.3] Knowledge engineering • [3.4] Approximate reasoning
Topics • [ 4.] Natural language • [4.1] Textual, lexical and morphological analyses • [4.2] Levels of natural language processing • [4.3] Logical formalisms: definite clause grammars • [4.4] Applications and current areas of interest
Topics • [ 5.] Machine learning • [5.1] Decision trees
Bibliography • There are no required readings, apart from the course lecture notes. Additional reading can be found in the following text: • Russell, Stuart J. and Peter Norvig • Artificial intelligence: a modern approach. 2nd edition • Upper Saddle River, NJ: Prentice Hall, 2002 • ISBN: 0137903952.
What is AI? • There is no single definition, but several approaches, that Russell-Norvig summarize in four main ones. • These approaches follow different points of view. • Their influences are diverse (Philosophy, Mathematics, Psychology, Biology...). • Their fields of application are ample and interrelated.
Approaches to AI • Systems that act like humans • The study of how to obtain that computers perform tasks at which, at the moment, people are better (Rich and Knight, 1991) • Systems that think like humans • The effort to make computers think... machines with minds in the full and literal sense (Haugeland, 1985) • Systems that think rationally • The study of the mental faculties through the study of computational models (Charniak and McDermott, 1985) • Systems that act rationally • The effort to explain and emulate the intelligent behavior in terms of computational processes (Shalkoff, 1990)
Other approaches to AI • Good Old-Fashioned Artificial Intelligence (GOFAI) (Douglas Lenat: Cyc) • Cramming a computer full of knowledge about the real world and methods to manipulate it • Intelligence intimately tied up with having and being able to use knowledge • “It’s worth to go to school” • Robotic insects example • Legs controlled by a central computer that has a detailed 3D map of the terrain and knows all the relevant laws of physics and strategies 15
Other approaches to AI • Learning through experience (Rodney Brooks: Cog, Kismet) • Machines that experience the world in much the way human beings do • “You can learn stuff on your own” • Robotic insects example • Each leg containing a small circuit that tells it about basic movements • All local computation physically coupled through the body, with gait emerging spontaneously from circuits’ interaction 16
Areas of work in AI • Theory • Philosophy • Applications
Eliza (1966) • Conversation with ELIZA E> Hello. I am ELIZA. How can I help you? P> I am feeling sad. E> I am sorry to hear you are sad. P> I had some nightmares tonight. E> You say you had some nightmares tonight? P> Yes, my dreams are worrying me. E> What does that dream suggest to you? P> Thrill, scare. E> I am not sure I understand you fully. P> My dreams annoy me. E> Do you dream often? P> Yes, daily.
Lost in translation English → Russian (or Spanish)→ English “The spirit is willing, but the flesh is weak” → → Дух охотно готов, но плоть слаба → → “The vodka is good, but the meat is rotten” (Actually: “Spirit is willingly ready, but flesh is weak” or “The alcohol is arranged, but the meat is weak”)
US District Court judge John E. Jones III • Proponents of intelligent design argued that their supposedly scientific alternative to evolutionary theory should be presented in biology classes. • “An objective student can reasonably infer that the school’s favored view is a religious one, and that the school is accordingly sponsoring a form of religion.”
One book • What if I want to read just one book about artificial intelligence? Darwin's Dangerous Idea by Daniel Dennett In favor of materialistic Darwinism Victims: Noam Chomsky, Roger Penrose, John Searle and, specially, Stephen Jay Gould