1. Knowledge Engineering and Agent Technology
H-C Wu

2. Outline Study and Traveling in UK How to Research
Knowledge Engineering
Problem in Knowledge Transfer
Ontology , Ontology Engineering
Mature Methodology CommonKADS (KE+KM)
Agent Definition
Knowledge Level in Agent System
Practical Reasoning Agent
BDI Architecture
Agent Tool
Reference

3. Study in UK IELS MA , Msc , MBA , Msc by Research M.Phil PhD , D. Phil
New Route PhD , EngD
Condition offer , Unconditional offer

4. Traveling in UK London Oxford, Cambridge Strafford Upon Avon York
Newcastle upon Tyne
Manchester
Liverpool
Edinburgh
Glasgow

5. Research Process Motivation: Why this research is important
Research Question: What are you going to study?
Research sub-questions: Break down your research question in several simpler questions
Literature Review: What is the relevance of your research question
Research Methodology: How are you going to answer your research question ?
Scope: Which issues are you not going to study?
Success Criteria: How are you going to evaluate when you are down?
Benchmark examples: Give some typical examples of your research problem ?

6. Business Application Using Intelligent System
Knowledge Base System
Case Based Reasoning
Intelligent Agent
Fuzzy System
Neural Network
Genetic Algorithms
Hybrid System

7. Knowledge Engineering
It is the art of building complex computer programs that represent and reason with knowledge of the world (Feigenbaum and McCorduck [1983])
Process of eliciting, structuring, formalizing, operational zing (Schreiber, Akkermans et al. 2000)
information and knowledge involved in a knowledge-intensive problem domain,
in order to construct a program that can perform a difficult task adequately
Errors in a knowledge-base can cause serious problems

8. Transfer View of KE Extracting knowledge from a human expert
“mining the jewels in the expert’s head”’
Transferring this knowledge into KS.
expert is asked what rules are applicable
translation of natural language into rule format

9. Problems with transfer view
The knowledge providers, the knowledge engineer and the knowledge-system developer should share
a common view on the problem solving process and
a common vocabulary
in order to make knowledge transfer a viable way of knowledge engineering

10. What Is An Ontology
An ontology is a specification of a conceptualization
An ontology is an explicit description of a domain:
concepts
properties and attributes of concepts
Constraints on properties and attributes
An ontology defines
a shared understanding
a common vocabulary
It defines the formal vocabularies for representing knowledge about engineering artefacts and processes

11. What Is “Ontology Engineering”?
Ontology Engineering: Defining terms in the domain and relations among them
Defining concepts in the domain (classes)
Arranging the concepts in a hierarchy (subclass-superclass hierarchy)
Defining which attributes and properties (slots) classes can have and constraints on their values
Defining individuals and filling in slot values

12. The Protégé Ontology Editor and Knowledge Acquisition System
Protégé is an ontology editor and a knowledge-base editor.
Protégé is also an open-source, Java tool that provides an extensible architecture for the creation of customized knowledge-based applications.

13. A Short History of Knowledge Systems

14. CommonKADS Model Set Organization Model Task Agent Knowledge
Communication
Design
Context
Concept
Artefact

15. Agent Levels of Abstraction
Social Level
Communication
Negotiation
Knowledge Level
Symbol level (Information Processing)
Knowledge ,Goals, Actions and Principle of Rationality
Mechanism Level
Circuit Level (Logical Behavior Computation)
Device Level ( Physical Behavior)

16. Agent Agency (代辦) Delegation (委任) Proactive(積極自發), Deliberative (三思而行)
(其他 AI 沒有的特性 )
Agent Intelligent Behavior (Practical Reasoning)
Intelligence is related to quantity and quality of knowledge

17. Agent Applications
“in 10 years time most new IT development will be affected, and many consumer products will contain embedded agent-based systems” (Guilfoyle 1995)

18. Agent Definition (Wooldridge and Jennings 1995)
An Agent is a computer system situated in some environment, and that is capable of autonomous action in this environment in order to meet its design objects.
Autonomy - Decision Control
Reactivity - Interactive with environment
Proactiveness - Exhibit goal-directed behaviour
Social Ability -Interacting with other agents

19. Caglayan and Harrison (1997)
Agent is a computing entity that performs user delegated tasks Autonomously. An agent implies a personal assistant metaphor where the agent performs tasks on behalf of a user.

20. Agent Technology Factors
Mechinery
Inferencing
Learning validation
representation
Content
Rules, context,
Application ontologies
& grammars
Security
Mutual
Public authentication
Privacy
payment
Access
To applications
Data & Service
Networking
Mobility
Intelligence
Agency

21. How are agents built and why it is hard
Intelligent Agent
Domain
Knowledge
Inference Engine
Expert
Engineer
Dialog
Programming
Knowledge Base
Results
The knowledge engineer attempts to understand how the subject matter expert reasons and solves problems and then encodes the acquired expertise into the agent's knowledge base.
This modeling and representation of expert’s knowledge is long, painful and inefficient (known as the “knowledge acquisition bottleneck”).
Tecuci, G. (1998). Building Intelligent Agents : An Apprenticeship Multistrategy Learning Theroy, Methodology, Tool and Case Studies, ACADEMIC PRESS.
Tecuci, G., M. Boicu, et al. (2004). Development and use of Intelligent Decision Making Assistants:The Disciple Approach, Learning Agents Center
We are using the term agent as a metaphor for a modern Artificial Intelligence system. Let us consider the problem of developing an agent that exhibits the problem solving expertise of a subject matter expert.
This diagram illustrates the current practice of building such an agent. It involves a subject matter expert and a knowledge engineer.
The knowledge engineer attempts to understand how the subject matter expert reasons and solves problems and then encodes the acquired expertise into the agent's knowledge base.
This modeling and representation of expert’s knowledge is long, painful and inefficient, being well-known as the “knowledge acquisition bottleneck” of agent development. Moreover, any future adaptation and development of the agent requires an even greater effort from the part of the subject matter expert and the knowledge engineer.

22. Practical Reasoning Decision Making Process
Weighting Conflicting Consideration
Bratman, M. E., D. J. Israel, et al. (1988). "Plans and resource-bounded practical reasoning." Computational Intelligence 4:

23. Practical Reasoning Deliberation (What to Achieve)
Option generation(= desires)
Filtering
Mean-Ends Reasoning (How to Achieve)
Computational Process
Take Place Under Resource Bounds (Limit Size, Time Constraint)
Plan, Recipe

24. Implementing Practical Reasoning Agents
Agent Control Loop Version 1
1. while true
2. observe the world;
3. update internal world model;
4. deliberate about what intention to achieve next;
5. use means-ends reasoning to get a plan for the intention;
6. execute the plan
7. end while

25. Implementing Practical Reasoning Agents

26. State in Intelligent Agents
Beliefs
What the world is like now
Desires (Goals)
What we would like the world to be
Intentions (Plans)
What we actually choose to carry out
Belief-Desire-Intention (BDI)
Based upon practical reasoning.
Decide what goals to achieve and how to achieve them.

27. Sensor
Input
BDI
Architecture
Belief Revision Function (brf)
Beliefs
Generate Options
Desires
Filter
Intentions
Action
Output
Action

28. BDI Architecture
An advantage is that BDI provides a reasoning capability similar to humans.
Intuitive
Provides a clear functional decomposition
A disadvantage to BDI is determining the commitment level to intentions.
Efficiently implementing the algorithms.

29. Agent Software Product Company Language AgentBuilder
Reticular Systems, Inc
Java
JACK Intelligent Agents (BDI)
Agent Oriented Software Pty. Ltd
JACK Agent Language
MadKit
Madkit Development Group
Java,Jess
Zeus (BDI) BT
Jade
Telecom Italia Lab
Open source Platfrom p2p, Java
JAM (BDI)
Intelligent Reasoning Systems

30. Reference
Bratman, M. E. (1987). Intention , Plan and Practical Reason, Harvard University Press.
Caglayan, A. and C. Harrison (1997). Agent Sourcebook, John Wiley & Sons.
Luck, M. (2003). "A Roadmap for Agent Based Computing." AgentLinkII: pp9-10.
Schreiber, G., H. Akkermans, et al. (2000). Knowledge Engineering and Management : The CommonKADS Methodology, MIT Press.
Wooldridge, M. (2002). An Introduction to MultiAgent Systems. John Wiley & Sons.
Wooldridge, M. and N. R. Jennings (1995). "Intelligent agents: Theory and practice." The Knowledge Engineering: p