Second Generation Expert Systems
(motto: “knowledge is power”)

Presentation: I’am…

Presentation: You are…



Main ideas

Plan of the week…

I would like to discuss about

Plan of the 1. Day

Problems, computer, resolution, algorithm, AI

“What is knowledge?”

What is the difference between information and knowledge?

Example of knowledge

Required characteristics of knowledge

“Is there any knowledge in computer programs? Where?”

Data Base Systems

I. generation (Classical)
Expert Systems

“What is an Expert System?”

“What is the difference between Expert System and Knowledge Based System?”

Knowledge base + inference engine

“How to construct an Expert System?”

Knowledge acquisition

Knowledge representation


“Inference engines”
“How does it work?”

The forward chaining mechanism

The backward chaining mechanism

Logic used in the Expert Systems

Expert System Shells

What is an expert system shell?

What is provided by a shell?

Shell examples / applications



Example of M1 rules

Example of a CLIPS rule

The MYCIN family tree

Applications of the expert systems

Other information sources

Example applications

Meta knowledge

Problems, inconveniences,

“What is explicit, what is implicit?”

“But where is the
knowledge about
how to
resolve the problem?”

Conclusions of the 1. Day

End of the 1. Day


Plan of the 2. day

General context

Knowledge acquisition is a modellisation

General approach (MACAO)

The main point is the construction of the conceptual model

“What is a conceptual model?”

“Which are the components of a conceptual model?”

Modelling language

“Why is the modelisation necessary?”

Specific context

“What kind of problem solver to construct?”

“Why reasoning the same way the expert does?”

Need for a constructive approach

The existing constructive approaches lack the followings:

Modelling approaches

Two main approaches:


The 4 layers of KADS

Generic Task


The project Mapcar


“Knowledge level prototyping”

Principle of the structural correspondence

Prototyping at the knowledge level

The MAPCAR approach

Construction of a conceptual model

“Need for a language“

“What language we need?“

Languages with predefined modelling primitives

Other languages?

“Why a reflexive language?”

Practical works

Discussions about

“I know that
I can make mistakes!”

Conclusions of the 2. Day

End of the 2. day


Plan of the 3. Day

The Mapcar language

Objective of the Mapcar language

Architecture of the Mapcar language

Examples of inference rules in Mapcar

The ZoLa language

General objectives



Analysis tools

Architecture of the ZoLa

Conceptual model layers
ZoLa sub-languages

Architecture of the ZoLa (properties)

Architecture of the ZoLa (functionality)

Reflexivity in ZoLa

Examples of the ZoLa constructions

L3 types

L3 instances

L2 profil and L2 operation
(type manipulation)

L2 profil (type control)

L2 operation (type control)

L1 strategy

L4 conceptual handles

L4 technical information

The modellisation of the DSTM

The DSTM modelling

Notions of the model (conceptual model of the domain)

High level actions of DSTM (conceptual model of the reasoning)

Algorithmic control for DSTM

Architecture with an algorithmic control

“Is this control is good enough? Can we do better?“

Dynamic control for DSTM

Architecture with an dynamic control

Architecture with an dynamic control (2)

Connexion with conceptual handles

Dictionnary to translate between the layers

Connexion layer (control algorithmic)



Different forms of reflexivity, some “definitions”



Initial situation

What happens if we start the reasoning?

SADE supervise the knowledge based system…

How to supervise (1)

How to supervise (2)

How to supervise (3)

“Everyone needs to be supervised“

Reflexivity in ZoLa

Conclusions of the 3. Day


Conclusions of the week

This is the