report on knowledge modeling in various applications in traffic systems

2014

Faculty of Computers and Information Department of Computer Sciences Cairo University

Knowledge Modeling in Various applications in Traffic Systems Report Submitted by: Yomna Mahmoud Ibrahim Hassan Report Submitted to : Prof. Dr. Hesham Hassan

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Table of Contents

Introduction page 3

Knowledge Modeling Techniques page 3

Generic Tasks page 3

CommonKADS page 5

Traffic Systems page 7

CommonKADS and Traffic Systems page 7

MAS- CommonKADS page 11

Dynamic Virtual Organization page 13

Conclusions page 13

References page 14


Knowledge Modeling in Various

Applications in Traffic Systems

Introduction

This report entails a detailed study of the knowledge modeling techniques utilized in

previous research in the traffic domain. The finalized model reached within this report

was a combination of research presented in multiple papers with the goal of reaching

a comprehensive technique for knowledge modeling traffic systems. The techniques

used were enhanced over the year in order to incorporate changes within current

traffic systems. This report is divided into the following sections, we first start with an

introduction where we give a general idea about knowledge modeling within traffic

systems and why do we need to model such a system within a knowledge base.

Within the second section, we give an introduction about traffic systems components.

Afterwards, each section will include part of the knowledge model combined from

research papers. Conclusions are finally described in the last section.

Section 1: Knowledge Modeling Techniques

Knowledge engineering (KE) is considered as the discipline of identifying a structure

that can be re-utilized for any knowledge based system (KBS). The basic structure of

a KBS is described in Figure 1. A systematic design for the knowledge structure of

the system was to be achieved, later on called knowledge modeling. Two of the most

prominent knowledge modeling techniques are Generic Tasks (GT) and

CommonKADS.

Figure 1: Knowledge Based system structure

a- Generic Tasks

Generic tasks, created in the late 80s [1], are templates of problem-solving activities

that can be configured together to describe any intelligent activity. At least five

different types of knowledge can be identified within generic tasks. These types are:


- Tasks

- Problem-solving method

- Inferences

- Ontologies

- Domain Knowledge

These different knowledge model components are related as shown in Figure 2. Tasks

within GT are each domain and problem independent. Each Generic Task is a

specialized type of problem solving which is described functionally. By describing a

task functionally, a method for solving that task is fairly easy to construct. The

method then dictates the types of knowledge required to solve that problem.

Once you have decided to that a certain GT is fit to solve your problem and model its

knowledge base, it is fairly easy to apply related problem solving technique on this

problem without significant changes. However, one of the shortcomings of GTs is that

it is very difficult to reformat/ change if you need to update your system structure

(you might need to utilize a completely different GT with different problem solving

techniques for the new update).

Figure 2: Relations between Knowledge model components

Tasks- Goalsg

Problem Solving methodsg

GENERATEg

Task instancesg

INVOKEg

Inferencesg

REFER TOg

Ontologiesg

DESCRIBEg

Domain Knowledgeg


Another drawback for GTs is that they mainly focus on the knowledge part of the

system. More enhanced knowledge models have been designed to overcome this. We

will mainly focus on CommonKADS as one of these knowledge models.

b-CommonKADS

CommonKADS is a comprehensive methodology for KBS systems. It has been

gradually developed and has been validated by many companies and universities in

the context of the European ESPRIT IT Programme [2]. CommonKADS includes the

organizational system of the whole system. CommonKADS is also adaptive to

existence of intelligence within the system structure. The CommonKADS model is

shown in Figure 3.

Figure 3: The CommonKADS model

The concept partition resonates to the knowledge model described by GT. The context

represents the interface with the user. The artifact part shows the specifications and

requirements for the utilized platform, software modules, .. etc. Table 1 shows the

definition for each model. Relationship description between each model is shown in

Figure 4.

Definition Model It describes and analyzes the main activities of an

enterprise. Organization Model

It analyzes the organization's global subprocess scheme:

input, output, preconditions, performance criteria,

resources, and competencies.

Task Model

Agent characteristics description as task executors:

competencies, authorizations, and restrictions. Agent Model

Conceptual description of agent transactions involved in

a task. Communication Model

Description of knowledge types and structures used in a

task and the role of these knowledge components in the

task resolution, but implementation independent.

Knowledge Model

Starting with the previous models, this one describes the

technical specifications such as architecture,

implementation platform, software modules, etc., in

order to get the functionality specified in Knowledge and

Communication models.

Design Model


Table 1: Definitions of CommonKADS models

Figure 4: Relationship description between CommonKADS models

There are different knowledge categories within CommonKADS. These

categories are divided as:

Task knowledge

o goal-oriented

o functional decomposition

o Controls inference knowledge

Inference knowledge

o basic reasoning steps that can be made in the domain knowledge and are applied by tasks

o Uses Domain Knowledge

Domain knowledge

o relevant domain knowledge and information

o static

In order to build a structure for a specific system utilizing the CommonKADS model,

there are main steps that have to be performed. These steps can be summarized as:

1- Knowledge Elicitation

2- Identify domain knowledge

3- Identify task decomposition structure

4- Identify Inference knowledge model


Within the following section we will give a description of a traffic system, and how

we utilize CommonKADS for modeling.

Section 2: Traffic Systems

In most of the research related to modeling the knowledge base of a traffic system [3],

the system is identified as follows: a centralized traffic control center which

views/manages different feeds from cameras in the streets. There are also vehicle

detectors on the ground. According to the information captured from the cameras and

other devices, the centralized traffic control center takes decisions to control the

traffic. This system is shown graphically in figure 5.

Figure 5: Generic Traffic System

As mentioned in section 1(b), in order to build the traffic system knowledge model

according to CommonKADS, we need to follow these four steps: Knowledge

Elicitation, Identify domain knowledge, Identify task decomposition structure, and

Identify Inference knowledge model. Each step is described within the following sub-

sections.

Section 3: CommonKADS and Traffic Systems

Knowledge Elicitation

In [4], a re-usable elicitation method has been identified for traffic systems. In this

modeling context, an activity based on reusable experiences (i.e cases) was utilized.

In order to reach these cases, the following functions need to be applied on the

incoming data input:

1- Identify relevant descriptors of the incident case model.


2- Identify discriminant index to organize the case base.

3- Define a similarity metric for matching.

4- Register knowledge necessary to adapt solution part of the selected

case, in order to solve the current problem.

Elicitation sessions of the expertise 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.

From interviews with traffic management experts, the authors within the paper 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.

Identify domain knowledge

In order to build a complete intelligent knowledge system, we have to identify the

main concepts/classes and functions to be done within a traffic system [5]. Figure 6

entails the domain knowledge concepts and its interaction with other models such as

the task and agent models.

Figure 6: Traffic system Domain layer and its interaction with knowledge

models.


On the other hand, Figure 7 details the main functionalities performed within a traffic

system (as described in [6]). We can see that at the sensors side, a video processing

kernel is included with the communication protocol with the camera devices. This

module is then followed by the methods done in order to achieve certain set of

statistics to be finally displayed to the user through the user interface as requested.

Figure 7: Traffic system Domain architecture

Identify task decomposition structure

There are three main composite tasks that we can identify to be happening in our

system: Diagnostic, prediction, and configuration tasks [3], all of which are re-

iterative and may occur in run-time. Each of these tasks is then decomposed into sub-

tasks until we reach the elementary non-composite tasks such as estimating the global

traffic demand (Figure 8).

Identify Inference knowledge model

Each of the before mentioned non-composite tasks (called an inference) has an input

and output role. They also utilize specific domain knowledge. The inference

knowledge model for the same example is shown in Figure 9.


Figure 8: Task decomposition structure

Although we have shown that CommonKADS has been used extensively in modeling

the knowledge base of traffic systems, some shortcomings have been discovered.

These shortcomings include:

* It focuses 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 modification has been applied to CommonKADS to adapt to the

existence of new traffic systems following the multi-agent model, called Multi-agent

systems (MAS) CommonKADS.


Figure 9: Inference knowledge model

Section 4: MAS-CommonKADS

MAS-CommonKADS extends the knowledge engineering methodology

CommonKADS with techniques from object oriented and protocol engineering

methodologies. In order to adapt to the existence of multiple agents, a new model is

added to CommonKADS which is the "coordination model" [7, 8]. Figure 10 shows

the new model hierarchy.

Figure 10: MAS CommonKADS model hierarchy

In figure 11, we can see the detailed structure of the new added coordination model

and how it interacts with other models within the CommonKADS. We can see that the


coordination model relies on defining an organized communication window, where

each agent-agent communication happens through a certain session with a specific

protocol.

Figure 11: Coordination model relations

With the addition of the coordination model, there are certain knowledge categories

that need to be added to the standard CommonKADS knowledge types. Shown in

Figure 12 under the coordination knowledge hierarchy are the knowledge criteria

suggested by [3].

Research has been able to reach an adaptive knowledge model for traffic system

which realized the existence of multiple interacting agents as we have seen through

the report. However, current traffic systems has been effectively decentralized, where

crowd-sourced data made it possible for each user to change the organization structure

of the traffic system by representing a sensor model on their own [8]. As a result, an

addition is suggested within the following section.


Figure 12: Coordination knowledge

Section 5: Dynamic Virtual organization

A dynamic virtual organization (DVO) identifies the collaboration between partners

and the technological agility that are essential to the new trends of businesses [9], and

there exists research that worked extensively on building and automating the

knowledge model for the DVO creation [9]. In addition, one of the applications that

can extensively rely on DVOs is an intelligent traffic system [10]. Therefore, it is only

logical to incorporate the CommonKADS model for DVOs within the knowledge

model of an intelligent traffic system in order to take into consideration the variation

on the organization level.

Conclusions:

This report focuses on the incorporation of CommonKADS as a knowledge modeling

technique for traffic systems. We have been able to identify the main model and

analyze its shortcomings. Accordingly, we have identified related research that

worked on fixing similar issues and how it makes the suggested knowledge model

more adaptive to the current changes within traffic systems that is acting more and

more as an independent intelligent system. This report shows a great potential for

future work within the field of knowledge modeling traffic systems.


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