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Artificial Intelligence An introduction

Artificial Intelligence An introduction. Alain Mille LIRIS CNRS UMR 5205 Université Lyon1. Summary. Part I – AI short history Part II – AI basics > formal systems Part III – Knowledge Based Systems Part IV – Knowledge Engineering Part V - Ontologies Part VI – Case-Based Reasoning

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Artificial Intelligence An introduction

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  1. Artificial IntelligenceAn introduction Alain Mille LIRIS CNRS UMR 5205 Université Lyon1

  2. Summary • Part I – AI short history • Part II – AI basics > formal systems • Part III – Knowledge Based Systems • Part IV – Knowledge Engineering • Part V - Ontologies • Part VI – Case-Based Reasoning • Part VII – AI challenges and AI for robotics BEST

  3. Part I AI short story

  4. Artificial intelligence …born only few years after computers… • https://www.aaai.org/AITopics/html/history.html • Official birth date : 1956, Darmouth College (New Hampshire, USA) • John McCarthy (logics supporter) • Marvin Minsky (dynamic schemes supporter) • Computer  « thinking machines » • Computer  Brain BEST

  5. Pioneers • [1936] Turing : Universal Turing Machine • [1945] Von Neumann : computer architecture • [1948] Wiener : cybernetics • [1948] Shannon : information theory • [1949] Mc Culloch and Pitts : neural networks (physiological approach) BEST

  6. First AI programs • Newell, Simon and Shaw write a program in logics for theorem proof [1956!] • They generalize the process through what they call a GENERAL PROBLEM SOLVER (GPS). A GPS solves a problem by exploring possible ways to go from an initial state to a state satisfying the goal to reach. A set of operators allows to move from one state to one another. A path going from the starting state to a state satisfying the goal is a solution (the optimal solution is the shortest path). BEST

  7. First challenges… • Computers playing chess -> first win in 1997 Deep Blue wins Kasparov • IQ Test (Evans 1963) : finding “logical” mapping between series of pictures. • Constraint Solving Approach (Waltz 1975) • “Natural language” processing (Eliza, Weizenbaum 1965) (SHRDLU, Winograd 1971) BEST

  8. Expert Systems • [seventies, eighties, until now…] a dream…or a nightmare? • DENDRAL (Chemical application) • MYCIN (Medical application -> THE model) • Hersay II (Speech understanding) • Prospector (Geology) • Expert Systems Generators • GURU • CLIPS BEST

  9. Part II AI Basics Formal Systems

  10. Formal systems for inference processes • How to build systems able to infer true things from other true things…(of the world!) • Symbolic approaches • Formal descriptions • Syntactic reformulations • Semantic declarations BEST

  11. Formal system For building a formal system, we need : • An alphabet, i.e. a set of symbols (not necessary characters) • A process to build expressions (not necessary concatenation) => Expression Building Process (EBP) • A set of axioms , i.e. expressions written according to 1 and 2. These expressions belongs (arbitrarily) to the “system” (are “true”) • Derivation rules which, starting from existing axioms, are able to produce theorems (expressions belonging now to the system) and which can be applied (to produced theorems) in order to produce new ones. BEST

  12. Example of a formal system ! • PEO System • alphabet = set of 3 symbols "p" , "e" , and “o" • EBP = concatenation • axiom = opoeoo • Derivation rules : • R1 : if an expression AeB is a theorem (where "A" and “B” stand for any suite of "o", "p", or "e"), then expression oAeBo is also a theorem. • R2 : if an expression AeB is a theorem , then expression AoeoB is also a theorem. • Questions • Q1 = oopooeoooo is a theorem? • Q2 = opooeoooo ? • Q3 = opopoeooo ? . BEST

  13. R1 R2 oopoeooo opooeooo R1 R2 ooopoeoooo oopooeoooo Theorem demonstration opoeoo • This system is semi-decidable because we have a provable process to decide that an expression is a theorem, but we do not have a provable process to decide that an expression is not a theorem. As you are humans (having learned mathematical addition) it should be helpful to read « p » as « plus », o as « one » and « e » as « equals » (opoeo one plus one equals one one) BEST

  14. Part III Knowledge Based Systems

  15. => Knowledge Based System Facts Fi [Axioms and Theorems] Domain knowledge (Rules, constraints, cases, …) [Axioms] Inference Engine • Kinds of possible requests : • - Is F12 inferable from F6 and F14? • What is inferable from F2 or F7? • How F13 could be inferred (which Fi could lead to F13)? BEST

  16. A (simple) KBS • Alphabet (symbols) • Distance_<_2kmdistance_<_300kmwalkingtravelling_by_traintravelling_by_planehaving_a_phonegoing_to_the_agencycalling_the_agencybuying_a_tickettrip_duration_>_2_daysbeing_a_civil_servant()not /*(negation)^ /*(and, conjunction)-> /*(implies) BEST

  17. Expression Building Process • expression := symbol • expression := ( expression ) • expression := not expression • expression := expression1 ^ expression2 • expression := expression1 -> expression2 BEST

  18. Axioms • Rules • R1 : distance_<_2km -> walking • R2 : ((not distance_<_2km) ^ distance_<_300km) -> travelling_by_train • R3 : (not distance_<_300km) -> travelling_by_plane • R4 : (buying_a_ticket ^ having_a_phone) -> calling_the_agency • R5 : (buying_a_ticket ^ (not having_a_phone)) -> going_to_the_agency • R6 : travelling_by_plane -> buying_a_ticket • R7 : (trip_duration.>.2_days ^ being_a_civil_servant) ->(not travelling_by_plane) • Facts • F1 : (not distance_<_300km) • F2 : having_a_phone BEST

  19. Inference Engine • It works • While it works • It does’nt work • Loop on Ri • Loop on not tagged Fj • if Ri fits the pattern "Fj -> Fk" • add Fk to Facts • tagg Fj • It works • else • loop on Fl if Ri fits the pattern "Fj ^ Fl ->..." add Fm = (Fj ^ Fl) to the Factstagg Fj it works endif • endloop /* FI • endif • Endloop /*Fj • Endloop /Ri • endwhile BEST

  20. How things are called… • R axioms are called RULES • Left part (of ->) : premises (conjunction of) • Right part (of ->) : Consequents (conjunction of) • F axioms are called FACTS • A kind of Rule which doesn't need premises to be true. Such Rules and Facts are called “Propositions” and the paradigm is called “Proposition logics” or “Order 0 logics” BEST

  21. From propositions to predicatesFrom 0 to first order logics Introduction of VARIABLES with Existential Quantifier Universal Quantifier BEST

  22. Programming languages for AI? • LISP (American: Mac Carthy) • PROLOG (France ! Colmerauer) • SmallTalk (Object Language) • Frame Languages • YAFOOL (Yet Another Frame based Object Oriented Language) • KL-ONE (Knowledge Language) • Description logics BEST

  23. Knowledge Based Systems? • Rules based KBS • Rules and facts + inference engine • LOGICAL approach • Expert Systems for • Diagnosis • Planning • Decision Helping => Challenge: how the set of rules and facts can be acquired and maintained -> Knowledge Engineering BEST

  24. Part IV Knowledge Engineering

  25. ? Knowledge Engineering: Why? Knowledge Base « representing » the world Symbolic level The « world » to model BEST

  26. Alan Newell idea [1982]: modeling the world at a “KNOWLEDGE LEVEL” Intermediate knowledge representation « understandable » by both humans and computers? (Knowledge Level) ? Knowledge Base « representing » the world (Symbolic Level) The « world » to model ? BEST

  27. Knowledge Level? • Domain abstraction for conceptualizing it (concepts and relationships + interactions) • A logical semantic will be described in order to allow computer calculations on the Domain • => Domain Theory • Intermediate language • Able to represent efficiently concepts, relations and interactions for human interpretation… • … an able to specify a corresponding logical semantic for computers calculations BEST

  28. Model Driven Knowledge Acquisition Unstructured Expertise Experts / data Conceptual Model Schema Conceptual Model description Knowledge Level Completed Conceptual Model Conceptual Model Instantiation Symbol Level KBS design KBS BEST

  29. Conceptual Model • Expressing Domain Knowledge  manipulated concepts + relationships / considering some tasks • Expressing how a task has to be realized on the base of Domain Knowledge. BEST

  30. Knowledge Analysis and Design System (KADS) Conceptual Models Problem solving behaviours Interpretation framework = vocabulary, generic components Transformation AI Techniques, Methods and representations Design Model Knowledge Based System BEST

  31. KADS : Knowledge Engineering BEST

  32. Part V Ontologies

  33. Domain theory as an ontology • Knowledge Based Systems remain difficult to build and maintain, but • For knowledge management, • For knowledge sharing, • and, in the general scope of the Semantic Web • Ontologies took a big place in AI research and applications BEST

  34. ONTOLOGY? • A specific ARTIFACT designed for expressing the intended meaning of a shared vocabulary • A shared vocabulary + a specification of its intended meaning • « An ontology is a specification of a conceptualization » [Gruber 95] • => an ontology accounts for the commitment of a language to a certain conceptualization! BEST

  35. Ontology Example Anything Person Organization Worker Student Faculty Assistant AdministrativeStaff Professor Lecturer Lecturer ISA relation BEST

  36. Different classes of ontologies [from http://www.loa-cnr.it ] BEST

  37. More about ontologies… • A site with links for anything you need for going further and mastering ontologies technologies • http://www.cs.utexas.edu/users/mfkb/related.html • THE french web site about Knowledge Engineering • http://www.irit.fr/GRACQ/index-bib.html • A nice tutorial about ontologies (in french) • http://www.irit.fr/GRACQ/COURS/CoursFabienGandon.htm • An other tutorial about ontologies (in english) • (http://www.loa-cnr.it/odcm.html ) BEST

  38. Part VI Analogical Reasoning => Case Based Reasoning

  39. Beyond « logical » systems, the analogical approach: Case Based-Reasoning • First ideas • Marvin Minsky (a frame based model for memory) [1975] • Roger Schank (scripts for understanding natural language) [1982] • Janet Kolodner (Case-Based Reasoning as a central research object)[1993] BEST

  40. Case-Based Reasoning Cycle BEST

  41. CBR: the reasoning kernel (1) BEST

  42. CBR: the reasoning kernel (2) BEST

  43. CBR: simple example (1) BEST

  44. CBR example (2) BEST

  45. CBR useful pointers • Orenge Tool (http://www.empolis.com/) • Kaidara (http://www.kaidara.com/) • CaseBank • Jcolibri Environment • CBR community website (no more maintained ) • David Aha web site BEST

  46. Part VII AI new challenges AI and Robotics

  47. AI Challenges • Dynamic and situated knowledge and reasoning (Robotics, help desk, semantic web, …) • Human learning / Machine Learning • Heterogeneous agents interactions • Cognition as knowledge emergence • > Biologically inspired systems • > Continuous learning man-machine systems • > Situated Cognition, Distributed Cognition, Multi-agent paradigm, Dynamic neural networks … BEST

  48. AI and Roboticshttp://www.faculty.ucr.edu/~currie/roboadam.htm • Definition of a Robot • According to The Robot Institute of America (1979) : "A reprogrammable, multifunctional manipulator designed to move materials, parts, tools, or specialized devices through various programmed motions for the performance of a variety of tasks." • According to the Webster dictionary: "An automatic device that performs functions normally ascribed to humans or a machine in the form of a human (Webster, 1993)." BEST

  49. AI Robotics… BEST

  50. AI and Robotics BEST

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