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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
Study in UK
IELS MA , Msc , MBA , Msc by Research M.Phil PhD , D. Phil New Route PhD , EngD Condition offer , Unconditional offer
Traveling in UK London Oxford, Cambridge Strafford Upon Avon York Newcastle upon Tyne Manchester Liverpool Edinburgh Glasgow
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 ?
Business Application Using Intelligent System Knowledge Base System Case Based Reasoning Intelligent Agent Fuzzy System Neural Network Genetic Algorithms Hybrid System
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
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
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
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
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
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.
A Short History of Knowledge Systems
1965 19851975 1995
general-purpose search engines
(GPS)
first-generation rule-based systems
(MYCIN, XCON)
emergence of structured methods
(early KADS)
mature methodologies
(CommonKADS)
=> from art to discipline =>
CommonKADS Model Set
OrganizationModel
TaskModel
AgentModel
KnowledgeModel
CommunicationModel
DesignModel
Context
Concept
Artefact
Agent Levels of Abstraction
Social Level1. Communication
2. Negotiation
Knowledge Level1. Symbol level (Information Processing)
2. Knowledge ,Goals, Actions and Principle of Rationality
Mechanism Level1. Circuit Level (Logical Behavior Computation)
2. Device Level ( Physical Behavior)
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
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)
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
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.
Agent Technology Factors
Mechinery
InferencingLearning validationrepresentation
Content
Rules, context,Application ontologies& grammars
Security
MutualPublic authenticationPrivacypayment
Access
To applicationsData & ServiceNetworkingMobility
Intelligence Agency
•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
How are agents built and why it is hard
KnowledgeEngineer
DomainExpert
Knowledge Base
Inference Engine
Intelligent Agent
ProgrammingDialog
Results
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: 349-355.
Practical Reasoning
Deliberation (What to Achieve)1. Option generation(= desires)2. Filtering
Mean-Ends Reasoning (How to Achieve)1. Computational Process2. Take Place Under Resource Bounds (Limit Size, Time
Constraint)3. Plan, Recipe
Implementing Practical Reasoning Agents
Agent Control Loop Version 11. while true2. 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 plan7. end while
Implementing Practical Reasoning Agents
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.
Belief Revision Function (brf)
Beliefs
Generate Options
Desires
Filter
Intentions
Action
SensorInput
ActionOutput
BDIArchitecture
BDI Architecture
An advantage is that BDI provides a reasoning capability similar to humans. IntuitiveProvides 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
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 Java
JadeTelecom Italia Lab Open source Platfrom p2p, Java
JAM (BDI) Intelligent Reasoning Systems
Java
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.
