This document discusses knowledge modelling techniques for traffic systems. It presents CommonKADS as a knowledge modelling methodology that can model the organizational structure, tasks, communications and knowledge of a system. It then discusses how CommonKADS has been applied to model knowledge in generic traffic systems and how MAS-CommonKADS extends it for multi-agent systems. Finally, it proposes using CommonKADS to model the identification and formation phases of dynamic virtual organizations for traffic control.

1. Knowledge Modelling in Various
applications in Traffic Systems
Presented by : Yomna Hassan
Submitted to : Dr. Hesham Hassan

2.  Techniques of Knowledge Modelling
 Difference in knowledge modelling techniques
 Generic Tasks in Traffic Systems
 CommonKADS in Traffic Systems
 MAS CommonKADS
 DVO CommonKADS model
 Conclusions
Contents

3.  Generic Tasks
 CommonKADS
Techniques of Knowledge Modelling

4.  Generic Tasks focus mainly on knowledge part of the
system
 CommonKADS includes the organizational structure
of the whole system, tasks and communications, and
knowledge model.
 CommonKADS is more adaptive to existence of
intelligence within the system structure
Difference in knowledge modelling
techniques

5. CommonKADS in Traffic
Systems

6. CommonKADS
organization model
task model
agent model
knowledge-
intensive
task
communication
model
knowledge
model
design
model
requirements
specification
for interaction functions
requirements
specification
for reasoning functions
task selected in feasibility study
and further detailed in
Task and Agent Models

7. Knowledge Categories

8. Generic Traffic System

9.  In this modelling context, which reveals an activity based on experiences (i.e. cases) reusing
 locate the incident reusing activity into the global control activity.
 identify relevant descriptors of the incident case model.
 identify discriminant index to organize the case base.
 define a similarity metric for matching.
 register knowledge necessary to adapt solution part of the selected case, in order to
solve the current problem.
 Elicitation sessions of the expertise, in the control room, are based on different methods:
document analysis, interviews, repertory grids, traffic manager's activities analysis and
results of the activity (problem reports).
 Each method presents an own goal and allows, generally, to obtain a particular type of
knowledge. Therefore, it is necessary to use these methods concurrently, one cancelling out
the drawbacks of others taken apart, benefiting the qualities of each one
Knowledge Elicitation

10.  From interviews with traffic management experts, we
have found that a large part of their knowledge is
episodic. That is, the expert solves a new problem by
relating the current network situation to his previous
experiences.
 These experiences are sometimes specific incidents,
with real dates and places, and sometimes general
classes of similar occasions.
Knowledge Elicitation (Cont’d)

11. Traffic system Domain architecture

12. Task decomposition structure

13. primitive inferences of the
knowledge model

14.  Focus on human –computer not computer-computer
interactions
 A restricted form of dynamic task assignment can be
done
 Multi-partner transactions not dealt with naturally
 Therefore a new model: coordination model is
proposed
Disadvantages of CommonKADS in
Multi-agent systems

15.  MAS-CommonKADS extends the knowledge
engineering methodology CommonKADS with
techniques from object oriented and protocol
engineering methodologies.
MAS-CommonKADS

18. Mas commonkads

19.  Existence of dynamic traffic systems
 Crowd-sourcing dependent
Dynamic Virtual
Organization Creation for Traffic
Systems

20. The CommonKADS control structure
models for DVO identification and
formation phases

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References