Artificial Intelligence An introduction

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Artificial Intelligence An introduction

Alain Mille

LIRIS CNRS UMR 5205

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Friday 25 February 2022 BEST 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

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Part I

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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

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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)

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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).

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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)

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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

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Part II

AI Basics

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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

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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.

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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 ? .

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Theorem demonstration

• 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.

opoeoo

oopoeooo opooeooo

R1 R2

ooopoeoooo oopooeoooo

R1 R2

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)

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Part III

Knowledge Based Systems

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=> Knowledge Based System

Domain knowledge

(Rules, constraints, cases, …) [Axioms]

Facts Fi

[Axioms and Theorems]

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)?

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A (simple) KBS

• Alphabet (symbols)

– Distance_<_2km distance_<_300km 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)

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Expression Building Process

• expression := symbol

• expression := ( expression )

• expression := not expression

• expression := expression1 ^ expression2

• expression := expression1 -> expression2

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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

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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

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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”

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From propositions to predicates From 0 to first order logics

Introduction of VARIABLES with Existential Quantifier Universal Quantifier





) n destinatio (

walking 2

) estination distance(d

n destinatio







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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

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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

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Part IV

Knowledge Engineering

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?

Knowledge Engineering: Why?

The « world » to model Knowledge Base

« representing » the world Symbolic level

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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? Knowledge Base

« representing » the world (Symbolic Level)

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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

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Model Driven Knowledge Acquisition

Experts / data

Conceptual Model description

Conceptual Model Instantiation

KBS design

Unstructured Expertise

KBS Conceptual Model Schema

Completed

Conceptual Model Knowledge

Level

Symbol Level

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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.

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Knowledge Analysis and Design System (KADS)

Problem solving

behaviours Conceptual Models

Transformation

Design Model Knowledge Based

System Interpretation framework

= vocabulary, generic components

AI Techniques, Methods and representations

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KADS : Knowledge Engineering

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Part V

Ontologies

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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

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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!

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Ontology Example

Anything

Person Organization

Worker

Student Faculty Assistant AdministrativeStaff

Professor

Lecturer

Lecturer ISA relation

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Different classes of ontologies

[from http://www.loa-cnr.it ]

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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 )

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Part VI

Analogical Reasoning

=>

Case Based Reasoning

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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]

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Case-Based Reasoning Cycle

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CBR: the reasoning kernel (1)

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CBR: the reasoning kernel (2)

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CBR: simple example (1)

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CBR example (2)

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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

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Part VII

AI new challenges

AI and Robotics

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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 …

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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)."

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AI Robotics…

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AI and Robotics

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AI and Robotics

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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 -> a cricket…

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New ways of moving…

Thinking Machines Corporation

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