Artificial Intelligence


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Artificial Intelligence

  1. 1. Artificial IntelligenceIntroductionFall 2008professor: Luigi Ceccaroni
  2. 2. Instructors• Luigi Ceccaroni– Omega building - Office 111–• Núria Castell Ariño– FIB building - Second floor–
  3. 3. 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
  4. 4. Web pages•••
  5. 5. Background• Students need the following knowledge (at theundergraduate level) to appropriately follow the course:– English language– Propositional and predicate logic; capacity to formulate aproblem in logical terms– Logical inference; strategies of resolution; capacity to solveproblems by resolution– Graph and tree structures; algorithms for search in trees andgraphs– Computational complexity; calculation of algorithm executionscost• There are assignments that expect students to be ableto read and write basic Java. This is the only formal pre-requisite.
  6. 6. Aim of the course• The general objectives of the course can besummarized as:– To identify the kind of problems that can be solvedusing AI techniques; to know the relation between AIand other areas of computer science.– To have knowledge of generic problem-solvingmethods in AI.– To understand the role of knowledge in present IA; toknow the basic techniques of knowledgerepresentation and their use.– To be able to apply basic AI techniques as supportfor the solution of practical problems.– To be able to navigate the basic bibliography of AI.
  7. 7. 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
  8. 8. Topics• [2.] Knowledge representation andinference– [2.1] Methodologies for knowledgerepresentation– [2.2] Rule-based systems– [2.3] Structured representations: frames andontologies
  9. 9. Topics• [3.] Knowledge-based systems– [3.1] Definition and architecture– [3.2] Expert systems– [3.3] Knowledge engineering– [3.4] Approximate reasoning
  10. 10. Topics• [ 4.] Natural language– [4.1] Textual, lexical and morphologicalanalyses– [4.2] Levels of natural language processing– [4.3] Logical formalisms: definite clausegrammars– [4.4] Applications and current areas ofinterest
  11. 11. Topics• [ 5.] Machine learning– [5.1] Decision trees
  12. 12. Bibliography• There are no required readings, apartfrom the course lecture notes. Additionalreading can be found in the following text:– Russell, Stuart J. and Peter Norvig– Artificial intelligence: a modern approach. 2ndedition– Upper Saddle River, NJ: Prentice Hall, 2002– ISBN: 0137903952.
  13. 13. What is AI?• There is no single definition, but severalapproaches, that Russell-Norvigsummarize in four main ones.• These approaches follow different pointsof view.• Their influences are diverse (Philosophy,Mathematics, Psychology, Biology...).• Their fields of application are ample andinterrelated.
  14. 14. Approaches to AI• Systems that act like humans– The study of how to obtain that computers perform tasks atwhich, at the moment, people are better (Rich and Knight, 1991)• Systems that think like humans– The effort to make computers think... machines with minds inthe full and literal sense (Haugeland, 1985)• Systems that think rationally– The study of the mental faculties through the study ofcomputational models (Charniak and McDermott, 1985)• Systems that act rationally– The effort to explain and emulate the intelligent behavior interms of computational processes (Shalkoff, 1990)
  15. 15. 15Other approaches to AI• Good Old-Fashioned Artificial Intelligence(GOFAI) (Douglas Lenat: Cyc)– Cramming a computer full of knowledge about thereal world and methods to manipulate it– Intelligence intimately tied up with having and beingable to use knowledge– “It’s worth to go to school”– Robotic insects example• Legs controlled by a central computer that has a detailed 3Dmap of the terrain and knows all the relevant laws of physicsand strategies15
  16. 16. 16Other approaches to AI• Learning through experience (RodneyBrooks: Cog, Kismet)– Machines that experience the world in muchthe way human beings do– “You can learn stuff on your own”– Robotic insects example• Each leg containing a small circuit that tells itabout basic movements• All local computation physically coupled throughthe body, with gait emerging spontaneously fromcircuits’ interaction 16
  17. 17. Areas of work in AI• Theory• Philosophy• Applications
  18. 18. Turing test
  19. 19. The Chinese room (John Searle)
  20. 20. Eliza (1966)• Conversation with ELIZAE> 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.
  21. 21. Lost in translationEnglish → 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”)
  22. 22. Autonomous robots
  23. 23. Personalization,recommendation
  24. 24. US District Court judgeJohn E. Jones III• Proponents of intelligent design arguedthat their supposedly scientific alternativeto evolutionary theory should bepresented in biology classes.• “An objective student can reasonably inferthat the school’s favored view is areligious one, and that the school isaccordingly sponsoring a form of religion.”
  25. 25. One book• What if I want to read just one book aboutartificial intelligence?Darwins Dangerous Idea by Daniel DennettIn favor of materialistic DarwinismVictims: Noam Chomsky, Roger Penrose, JohnSearle and, specially, Stephen Jay Gould