Muutke küpsiste eelistusi

Building Intelligent Agents: An Apprenticeship, Multistrategy Learning Theory, Methodology, Tool and Case Studies [Pehme köide]

(Romanian Academy)
  • Formaat: Paperback / softback, 320 pages, kõrgus x laius: 229x152 mm, kaal: 6330 g
  • Ilmumisaeg: 12-Jun-1998
  • Kirjastus: Morgan Kaufmann Publishers In
  • ISBN-10: 0126851255
  • ISBN-13: 9780126851250
Teised raamatud teemal:
Building Intelligent Agents: An Apprenticeship, Multistrategy Learning Theory, Methodology, Tool and Case StudiesSuurem pilt
  • Formaat: Paperback / softback, 320 pages, kõrgus x laius: 229x152 mm, kaal: 6330 g
  • Ilmumisaeg: 12-Jun-1998
  • Kirjastus: Morgan Kaufmann Publishers In
  • ISBN-10: 0126851255
  • ISBN-13: 9780126851250
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780126851250.html
Märksõnad:
Presents an approach for building software agents that can be trained to assist users in a number of ways for example, by helping or teaching the users to perform their tasks, or by performing tasks on a user's behalf. Topics include theory that allows a user to train an agent by giving examples and explanations, and by supervising and correcting its behavior; and a practical methodology and the Disciple shell that implement the agent building theory. In addition, four complex case studies (two assessment and tutoring agents for students, an engineering design assistant, and a virtual military agent) are included. Annotation c. by Book News, Inc., Portland, Or.

Building Intelligent Agents is unique in its comprehensive coverage of the subject. The first part of the book presents an original theory for building intelligent agents and a methodology and tool that implement the theory. The second part of the book presents complex and detailed case studies of building different types of agents: an educational assessment agent, a statistical analysis assessment and support agent, an engineering design assistant, and a virtual military commander. Also featured in this book is Disciple, a toolkit for building interactive agents which function in much the same way as a human apprentice. Disciple-based agents can reason both with incomplete information, but also with information that is potentially incorrect. This approach, in which the agent learns its behavior from its teacher, integrates many machine learning and knowledge acquisition techniques, taking advantage of their complementary strengths to compensate for each others weakness. As a consequence, it significantly reduces (or even eliminates) the involvement of a knowledge engineer in the process of building an intelligent agent.
Preface x(6)
About the Contributing Writers xvi(4)
Notation xx
1. Intelligent Agents
1(12)
1.1 What is an Intelligent agent?
1(1)
1.2 What is a learning agent?
2(1)
1.3 Phases in the development of an intelligent agent's knowledge base
3(1)
1.4 Approaches to knowledge base development
4(9)
1.4.1 Knowledge acquisition approaches
4(1)
1.4.2 Machine learning approaches
5(1)
1.4.3 Multistrategy learning approaches
6(3)
1.4.4 Complementary nature of machine learning and knowledge acquisition
9(1)
1.4.5 Integrated machine learning and knowledge acquisition approaches
10(3)
2. General Presentation of the Disciple Approach for Building Intelligent Agents
13(20)
2.1 An overview of the Disciple approach
13(3)
2.2 Building a manufacturing assistant
16(7)
2.3 Building an assessment agent for higher-order thinking skills
23(10)
3. Knowledge Representation and Reasoning
33(46)
3.1 Knowledge representation
33(17)
3.1.1 Introduction
3.1.1.1 What is a knowledge representation
33(1)
3.1.1.2 General features of a knowledge representation
34(1)
3.1.1.3 Hybrid knowledge representation
35(1)
3.1.2 Semantic network representation of objects
36(1)
3.1.2.1 Characterization of instances and concepts
36(1)
3.1.2.2 Intuitive definition of generalization
37(1)
3.1.2.3 Properties and relations
38(1)
3.1.2.4 Definition of instances and concepts
39(3)
3.1.2.5 General characterization of the semantic network
42(1)
3.1.3 General concepts and rules
42(1)
3.1.3.1 Representation language for general concepts and rules
42(2)
3.1.3.2 General concepts
44(1)
3.1.3.3 Rules
45(3)
3.1.3.4 Plausible Version Space rules
48(2)
3.2 Generalization in the representation language of the agent
50(16)
3.2.1 Formal definition of generalization
50(1)
3.2.1.1 Term generalization
50(1)
3.2.1.2 Clause generalization
50(2)
3.2.1.3 Conjunctive formula generalization
52(1)
3.2.1.4 Disjunctive formula generalization
53(1)
3.2.2 Generalization rules
54(1)
3.2.2.1 Turning constants into variables
54(1)
3.2.2.2 Turning occurrences of a variable into different variables
55(1)
3.2.2.3 Climbing the generalization hierarchies
56(1)
3.2.2.4 Dropping conditions
57(1)
3.2.2.5 Adding alternatives
57(1)
3.2.2.6 Generalizing numbers to intervals
57(1)
3.2.2.7 Using theorems
57(1)
3.2.3 Other definitions of generalizations
58(4)
3.2.4 Rules as generalizations of examples of problem solving episodes
62(4)
3.3 Elementary problem solving methods
66(13)
3.3.1 Use of transitivity and inheritance
67(1)
3.3.1.1 Properties of ISA, INSTANCE-OF and IS
67(1)
3.3.1.2 Inheritance of features
67(1)
3.3.1.3 Default inheritance
68(1)
3.3.1.4 Multiple inheritance
69(1)
3.3.2 Network matching
69(3)
3.3.3 Example generation
72(1)
3.3.4 Rule matching
72(5)
3.3.5 Reasoning with plausible version space rules
77(2)
4. Knowledge Acquisition and Learning
79(68)
4.1 Knowledge elicitation
80(5)
4.1.1 Knowledge elicitation goals and principles
80(1)
4.1.2 Implicit associations between the knowledge elements
81(3)
4.1.3 Knowledge elicitation processes
84(1)
4.2 Rule learning
85(16)
4.2.1 The rule learning problem
85(4)
4.2.2 The rule learning method
89(1)
4.2.2.1 What is an explanation of an example
89(3)
4.2.2.2 The explanation generation method
92(2)
4.2.2.3 Analogical reasoning
94(2)
4.2.2.4 The analogy-based generalization method
96(3)
4.2.3 Characterization of the learned PVS rule
99(2)
4.3 Rule refinement
101(29)
4.3.1 The rule refinement problem
101(1)
4.3.2 The rule refinement method
102(1)
4.3.3 Rule refinement through active experimentation with its plausible upper bound
103(1)
4.3.3.1 The active experimentation process
103(2)
4.3.3.2 Experimentation and verification
105(3)
4.3.3.3 Refining the PVS rule with a positive example
108(7)
4.3.3.4 Refining the PVS rule with a negative example
115(12)
4.3.4 Rule verification through active experimentation with its plausible lower bound
127(1)
4.3.5 Rule refinement with external examples
127(1)
4.3.6 Characterization of the refined rule
128(2)
4.4 Exception-driven knowledge base refinement
130(12)
4.4.1 The exception-driven knowledge base refinement problem
130(1)
4.4.2 The consistency driven knowledge base refinement method
131(4)
4.4.3 Consistency driven discovery and elicitation of features
135(2)
4.4.4 Consistency driven discovery and elicitation of concepts
137(1)
4.4.5 The completeness driven knowledge base refinement method
138(3)
4.4.6 Completeness driven elicitation of features
141(1)
4.4.7 Completeness driven discovery and elicitation of concepts
141(1)
4.5 An analysis of the expert-agent interactions
142(4)
4.5.1 Types of interactions
142(2)
4.5.2 The utility of explanations
144(2)
4.6 Final remarks
146(1)
5. The Disciple Shell and Methodology
147(32)
5.1 Architecture of the Disciple shell
147(4)
5.1.1 Knowledge acquisition and learning component
148(1)
5.1.2 Problem-solving component
149(1)
5.1.3 Knowledge base manager
149(1)
5.1.3.1 Dictionary
150(1)
5.1.3.2 Knowledge query language
150(1)
5.2 The methodology for building intelligent agents
151(3)
5.2.1 An overview of the Disciple methodology
152(1)
5.2.2 Issues in developing Disciple agents
153(1)
5.3 Expert-agent interactions during the knowledge elicitation process
154(7)
5.3.1 Overview of the interaction process
155(1)
5.3.2 Interactions with the concept browser
156(2)
5.3.3 Interactions with the concept editor
158(1)
5.3.4 Interactions with the dictionary browser/editor
158(3)
5.3.5 Interactions with the association browser
161(1)
5.4 Expert-agent interactions during the rule learning process
161(9)
5.4.1 Overview of the interaction process
162(2)
5.4.2 Interactions with the example editor
164(2)
5.4.3 Interactions with the rule learner
166(1)
5.4.4 Interactions with the rule browser
166(3)
5.4.5 Interactions with the rule editor
169(1)
5.5 Expert-agent interactions during the rule refinement process
170(8)
5.5.1 Overview of the interaction process
171(1)
5.5.2 Interactions with the rule refiner
171(5)
5.5.3 Interactions with the explanation grapher
176(2)
5.6 Final remarks
178(1)
6. Case Study: Assessment Agent for Higher-Order Thinking Skills in History
179(50)
6.1 Characterization of two types of assessment agents
179(2)
6.2 Developing a customized Disciple agent
181(9)
6.2.1 Analyzing the problem domain and defining agent requirements
181(1)
6.2.1.1 Application domain
181(1)
6.2.1.2 Use of the reporter paradigm
181(1)
6.2.1.3 Types of test questions
182(1)
6.2.1.4 Modes of operation and feedback provided to the student
182(1)
6.2.1.5 Dynamic and context-sensitive generation of tests
182(1)
6.2.2 Developing the top level ontology of the knowledge base
183(5)
6.2.3 Developing domain dependent modules
188(1)
6.2.3.1 The source viewer
188(2)
6.2.3.2 The customized example editor
190(1)
6.2.3.3 The example viewer
190(1)
6.3 Building the initial knowledge base
190(3)
6.3.1 Defining the history curriculum
191(1)
6.3.2 Selecting and representing historical sources
191(2)
6.3.3 Populating the semantic network with other necessary concepts and instances
193(1)
6.4 Teaching the agent how to judge the relevancy of a source with respect to a task
193(9)
6.4.1 Giving the agent an example of a task and a relevant source
193(1)
6.4.2 Helping the agent understand why the source is relevant
194(4)
6.4.3 Supervising the agent as it evaluates the relevance of other sources to similar tasks
198(1)
6.4.3.1 Confirming the agent's evaluation
198(1)
6.4.3.2 Rejecting agent's evaluation and helping it to understand its mistake
199(3)
6.5 Developing the assessment engine and the graphical user interface
202(13)
6.5.1 Augmenting and adjusting the patterns associated with the learned rules
206(1)
6.5.2 Sample agent-student interaction during an If-relevant test question
207(1)
6.5.3 Generation of If-relevant test questions with relevant sources
207(5)
6.5.4 Generation of If-relevant test questions with irrelevant sources
212(1)
6.5.5 Sample agent-student interaction during a Which-relevant test question
212(1)
6.5.6 Generation of Which-relevant test questions
213(1)
6.5.7 Sample agent-student interaction during a Why-relevant test question
213(1)
6.5.8 Generation of Why-relevant test questions
213(2)
6.6 Verifying, validating and maintaining the agent
215(7)
6.7 Developing the integrated assessment agent
222(5)
6.8 Final remarks
227(2)
7. Case Study: Statistical Analysis Assessment and Support Agent
229(27)
7.1 The Natural World-the course
230(2)
7.2 Assessment in The Natural World
232(2)
7.3 Sample interactions between the agent and the student
234(8)
7.3.1 Example 1 -- analysis of cigarette data
234(4)
7.3.2 Example 2 -- analysis of iris data
238(2)
7.3.3 Example 3 -- analysis of flies data
240(2)
7.4 Building of the initial knowledge base
242(3)
7.5 Teaching the agent to generate test questions
245(8)
7.6 Final remarks
253(3)
8. Case Study: Design Assistant for Configuring Computer Systems
256(18)
8.1 Disciple methodology applied to engineering domains
256(3)
8.2 Defining the initial knowledge base of the design assistant
259(5)
8.3 The shared expertise method of problem solving and learning
264(3)
8.4 Training and using the design assistant
267(6)
8.4.1 Learning a heuristic design rule from a new creative design
267(2)
8.4.2 Extending the RDS
269(1)
8.4.3 Improving the IDS
270(1)
8.4.4 Handling exceptions
271(2)
8.5 Final remarks
273(1)
9. Case Study: Virtual Agent for Distributed Interactive Simulations
274(23)
9.1 Distributed interactive simulations
274(2)
9.2 Developing a virtual armored company commander
276(2)
9.3 Defining the tasks and the top-level ontology of the agent
278(1)
9.4 Developing the semantic network
278(4)
9.5 Teaching the agent how to accomplish a defensive mission
282(11)
9.5.1 Giving the agent an example
282(1)
9.5.2 Helping the agent to understand why the positioning is correct
282(2)
9.5.3 Supervising the agent as it generates other defensive placements
284(3)
9.5.3.1 Rejecting agent's solution and helping it to understand its mistake
287(4)
9.5.3.2 Confirming the agent's solution
291(2)
9.6 Final remarks
293(4)
Selected Bibliography of Machine Learning, Knowledge Acquisition, and Intelligent Agents Research 297(19)
Index 316


Gheorghe Tecuci is Professor of Computer Science at George Mason University and a full member of the Romanian Academy. He has had around 100 scientific papers published, and is best known for his contributions to multistrategy learning and integrated machine learning and knowledge acquisition.