Artificial Intelligence Introduction Fall 2008 professor Luigi Ceccaroni

Artificial Intelligence Introduction Fall 2008 professor: Luigi Ceccaroni

Instructors • Luigi Ceccaroni – Omega building - Office 111 – luigi@lsi. upc. edu • Núria Castell Ariño – FIB building - Second floor – castell@lsi. upc. edu

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 assignments that expect students to be able to read and write basic Java. This is the only formal prerequisite.

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. 2 nd 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 Mc. Dermott, 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 3 D 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 16 circuits’ interaction

Areas of work in AI • Theory • Philosophy • Applications

Turing test

The Chinese room (John Searle)

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”)

Autonomous robots

Personalization, recommendation

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