Knowledge Engineering and Agent Technology

1. Knowledge Engineering and Agent Technology H-C Wu hsu-che.wu@warwick.ac.uk

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

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

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

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 Intelligence4: 349-355.

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. http://www.multiagent.com/arch/bdi/index.html

29. Agent Software

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: p115-152.