Artificial Intelligence An introduction Alain Mille LIRIS CNRS

Artificial Intelligence An introduction Alain Mille LIRIS CNRS UMR 5205 Université Lyon 1 BEST

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 25/09/2021 BEST 2

Part I AI short story BEST

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 Mc. Carthy (logics supporter) – Marvin Minsky (dynamic schemes supporter) • Computer « thinking machines » – Computer Brain 25/09/2021 BEST 4

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) 25/09/2021 BEST 5

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). 25/09/2021 BEST 6

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) 25/09/2021 BEST 7

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 25/09/2021 BEST 8

Part II AI Basics Formal Systems BEST

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 25/09/2021 BEST 10

Formal system For building a formal system, we need : 1. An alphabet, i. e. a set of symbols (not necessary characters) 2. A process to build expressions (not necessary concatenation) => Expression Building Process (EBP) 3. A set of axioms , i. e. expressions written according to 1 and 2. These expressions belongs (arbitrarily) to the “system” (are “true”) 4. 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. 25/09/2021 BEST 11

Example of a formal system ! • PEO System – – alphabet = set of 3 symbols "p" , "e" , and “o" EBP = concatenation axiom = opoeoo Derivation rules : • R 1 : if an expression Ae. B is a theorem (where "A" and “B” stand for any suite of "o", "p", or "e"), then expression o. Ae. Bo is also a theorem. • R 2 : if an expression Ae. B is a theorem , then expression Aoeo. B is also a theorem. • Questions – Q 1 = oopooeoooo is a theorem? – Q 2 = opooeoooo ? – Q 3 = opopoeooo ? . 25/09/2021 BEST 12

Theorem demonstration R 1 opoeoo R 2 oopoeooo R 1 ooopoeoooo opooeooo R 2 oopooeoooo • 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) 25/09/2021 BEST 13

Part III Knowledge Based Systems BEST

=> Knowledge Based System Domain knowledge (Rules, constraints, cases, …) [Axioms] Facts Fi [Axioms and Theorems] Inference Engine Kinds of possible requests : - Is F 12 inferable from F 6 and F 14? - What is inferable from F 2 or F 7? - How F 13 could be inferred (which Fi could lead to F 13)? 25/09/2021 BEST 15

A (simple) KBS • Alphabet (symbols) – Distance_<_2 km distance_<_300 km walking travelling_by_train travelling_by_plane having_a_phone going_to_the_agency calling_the_agency buying_a_ticket trip_duration_>_2_days being_a_civil_servant ( ) not /*(negation) ^ /*(and, conjunction) -> /*(implies) 25/09/2021 BEST 16

Expression Building Process • • • expression : = symbol expression : = ( expression ) expression : = not expression : = expression 1 ^ expression 2 expression : = expression 1 -> expression 2 25/09/2021 BEST 17

Axioms • Rules – R 1 : distance_<_2 km -> walking – R 2 : ((not distance_<_2 km) ^ distance_<_300 km) -> travelling_by_train – R 3 : (not distance_<_300 km) -> travelling_by_plane – R 4 : (buying_a_ticket ^ having_a_phone) -> calling_the_agency – R 5 : (buying_a_ticket ^ (not having_a_phone)) -> going_to_the_agency – R 6 : travelling_by_plane -> buying_a_ticket – R 7 : (trip_duration. >. 2_days ^ being_a_civil_servant) -> (not travelling_by_plane) • Facts – F 1 : (not distance_<_300 km) – F 2 : having_a_phone 25/09/2021 BEST 18

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 Facts tagg Fj it works endif » endloop /* FI – endif • Endloop /*Fj – Endloop /Ri endwhile 25/09/2021 BEST 19

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” 25/09/2021 BEST 20

From propositions to predicates From 0 to first order logics Introduction of VARIABLES with Existential Quantifier Universal Quantifier 25/09/2021 BEST 21

Programming languages for AI? • • LISP (American: Mac Carthy) PROLOG (France ! Colmerauer) Small. Talk (Object Language) Frame Languages – YAFOOL (Yet Another Frame based Object Oriented Language) – KL-ONE (Knowledge Language) • Description logics 25/09/2021 BEST 22

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 25/09/2021 BEST 23

Part IV Knowledge Engineering BEST

Knowledge Engineering: Why? Knowledge Base « representing » the world Symbolic level The « world » to model ? 25/09/2021 BEST 25

Alan Newell idea [1982]: modeling the world at a “KNOWLEDGE LEVEL” Intermediate knowledge representation « understandable » by both humans and computers? (Knowledge Level) ? The « world » to model ? 25/09/2021 BEST Knowledge Base « representing » the world (Symbolic Level) 26

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 25/09/2021 BEST 27

Model Driven Knowledge Acquisition Experts / data Unstructured Expertise Conceptual Model description Knowledge Level Symbol Level 25/09/2021 Conceptual Model Instantiation KBS design BEST Conceptual Model Schema Completed Conceptual Model KBS 28

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. 25/09/2021 BEST 29

Knowledge Analysis and Design System (KADS) Problem solving behaviours Conceptual Models Interpretation framework = vocabulary, generic components AI Techniques, Methods and representations Knowledge Based System 25/09/2021 Transformation Design Model BEST 30

KADS : Knowledge Engineering 25/09/2021 BEST 31

Part V Ontologies BEST

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 25/09/2021 BEST 33

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! 25/09/2021 BEST 34

Ontology Example Anything Person Organization Worker Faculty Student Assistant Administrative. Staff Professor Lecturer 25/09/2021 ISA relation BEST 35

Different classes of ontologies [from http: //www. loa-cnr. it ] 25/09/2021 BEST 36

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/Cours. Fabien. Gandon. htm • An other tutorial about ontologies (in english) – (http: //www. loa-cnr. it/odcm. html ) 25/09/2021 BEST 37

Part VI Analogical Reasoning => Case Based Reasoning BEST

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] 25/09/2021 BEST 39

Case-Based Reasoning Cycle 25/09/2021 BEST 40

CBR: the reasoning kernel (1) 25/09/2021 BEST 41

CBR: the reasoning kernel (2) 25/09/2021 BEST 42

CBR: simple example (1) 25/09/2021 BEST 43

CBR example (2) 25/09/2021 BEST 44

CBR useful pointers • • • Orenge Tool (http: //www. empolis. com/) Kaidara (http: //www. kaidara. com/) Case. Bank Jcolibri Environment CBR community website (no more maintained ) • David Aha web site 25/09/2021 BEST 45

Part VII AI new challenges AI and Robotics BEST

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, Multiagent paradigm, Dynamic neural networks … 25/09/2021 BEST 47

AI and Robotics http: //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). " 25/09/2021 BEST 48

AI Robotics… 25/09/2021 BEST 49

AI and Robotics 25/09/2021 BEST 50

AI and Robotics 25/09/2021 BEST 51

AI and Robotics Sony AIBO … http: //www. eu. aibo. com/5_1_casestudies. asp sonydog 1. mov Reacting to face interaction : kismet. mov Biorobobics -> a cricket… 25/09/2021 BEST 52

New ways of moving… Thinking Machines Corporation 25/09/2021 BEST 53

The end… • Thank you for your attention • Any question? 25/09/2021 BEST 54