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Artificial Intelligence An introduction

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Title: Artificial Intelligence An introduction


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

2
Summary
  • Part I AI short history
  • Part II AI basics gt 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

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

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)

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).

7
First challenges
  • Computers playing chess -gt 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)

8
Expert Systems
  • seventies, eighties, until now a dreamor a
    nightmare?
  • DENDRAL (Chemical application)
  • MYCIN (Medical application -gt THE model)
  • Hersay II (Speech understanding)
  • Prospector (Geology)
  • Expert Systems Generators
  • GURU
  • CLIPS

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

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) gt 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.

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 ? .

13
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)
14
Part III
  • Knowledge Based Systems

15
gt 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)?

16
A (simple) KBS
  • Alphabet (symbols)
  • Distance_lt_2kmdistance_lt_300kmwalkingtravelling
    _by_traintravelling_by_planehaving_a_phonegoing
    _to_the_agencycalling_the_agencybuying_a_ticket
    trip_duration_gt_2_daysbeing_a_civil_servant()n
    ot /(negation) /(and,
    conjunction)-gt /(implies)

17
Expression Building Process
  • expression symbol
  • expression ( expression )
  • expression not expression
  • expression expression1 expression2
  • expression expression1 -gt expression2

18
Axioms
  • Rules
  • R1 distance_lt_2km -gt walking
  • R2 ((not distance_lt_2km) distance_lt_300km) -gt
    travelling_by_train
  • R3 (not distance_lt_300km) -gt travelling_by_plane
  • R4 (buying_a_ticket having_a_phone) -gt
    calling_the_agency
  • R5 (buying_a_ticket (not having_a_phone)) -gt
    going_to_the_agency
  • R6 travelling_by_plane -gt buying_a_ticket
  • R7 (trip_duration.gt.2_days being_a_civil_serva
    nt) -gt(not travelling_by_plane)
  • Facts
  • F1 (not distance_lt_300km)
  • F2 having_a_phone

19
Inference Engine
  • It works
  • While it works
  • It doesnt work
  • Loop on Ri
  • Loop on not tagged Fj
  • if Ri fits the pattern "Fj -gt Fk"
  • add Fk to Facts
  • tagg Fj
  • It works
  • else
  • loop on Fl
  • if Ri fits the pattern "Fj Fl -gt..."
    add Fm (Fj Fl) to the Facts
    tagg Fj
  • it works
  • endif
  • endloop / FI
  • endif
  • Endloop /Fj
  • Endloop /Ri
  • endwhile

20
How things are called
  • R axioms are called RULES
  • Left part (of -gt) premises (conjunction of)
  • Right part (of -gt) 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

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

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

24
Part IV
  • Knowledge Engineering

25
Knowledge Engineering Why?
Knowledge Base  representing  the
world Symbolic level
The  world  to model
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
?
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
  • gt 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

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
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.

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
31
KADS Knowledge Engineering
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

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
  • gt an ontology accounts for the commitment of a
    language to a certain conceptualization!

35
Ontology Example
Anything
Person
Organization
Worker
Student
Faculty
Assistant
AdministrativeStaff
Professor
Lecturer
Lecturer
ISA relation
36
Different classes of ontologies
from http//www.loa-cnr.it
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.h
    tm
  • An other tutorial about ontologies (in english)
  • (http//www.loa-cnr.it/odcm.html )

38
Part VI
  • Analogical Reasoning
  • gt
  • 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

40
Case-Based Reasoning Cycle
41
CBR the reasoning kernel (1)
42
CBR the reasoning kernel (2)
43
CBR simple example (1)
44
CBR example (2)
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

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
  • gt Biologically inspired systems
  • gt Continuous learning man-machine systems
  • gt Situated Cognition, Distributed Cognition,
    Multi-agent paradigm, Dynamic neural networks

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)."

49
AI Robotics
50
AI and Robotics
51
AI and Robotics
52
AI and Robotics
Sony AIBO http//www.eu.aibo.com/5_1_casestudies
.asp sonydog1.mov Reacting to face to face
interaction kismet.mov Biorobobics -gt a
cricket
53
New ways of moving
Thinking Machines Corporation
54
The end
  • Thank you for your attention
  • Any question?
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