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Constructing intelligent agents using JAVA / Joseph P. Bigus, Jennifer Bigus.
By: Bigus, Joseph P
.
Contributor(s): Bigus, Jennifer.
Material type: 
BookSeries: Professional developer's guide series.Publisher: New York : Wiley, 2001Edition: 2nd ed.Description: xxii, 408 p. : ill. ; 24 cm.ISBN: 047139601X.Subject(s): Intelligent agents (Computer software) | Java (Computer program language)DDC classification: 006.3
Contents:
Introduction -- Problem solving using search -- Knowledge representation -- Reasoning systems -- Learning systems -- Agents and multiagent systems -- Intelligent agent framework -- Personal agent manager Application -- Infofilter application -- Marketplace application -- Java-based agent environments.
| Item type | Current library | Call number | Copy number | Status | Barcode | |
|---|---|---|---|---|---|---|
| General lending | MTU Bishopstown Library Lending | 006.3 (Browse shelf(Opens below)) | 1 | Available | 00158288 |
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Enhanced descriptions from Syndetics:
A state-of-the-art guide on how to build intelligent Web-based applications using Java
Joseph and Jennifer Bigus update and significantly expand their book on building intelligent Web-based applications using Java. Geared to network programmers or Web developers who have previously programmed agents in Smalltalk or C++, this practical book explains in detail how to construct agents capable of learning and competing, including both design principles and actual code for personal agents, network or Web agents, multi-agent systems and commercial agents. New and revised coverage includes agent tools, agent uses for Web applications (including personalization, cross-selling, and e-commerce), and additional AI technologies such as fuzzy logic and genetic algorithms.
Bibliography: (pages 389-393) and index.
Introduction -- Problem solving using search -- Knowledge representation -- Reasoning systems -- Learning systems -- Agents and multiagent systems -- Intelligent agent framework -- Personal agent manager Application -- Infofilter application -- Marketplace application -- Java-based agent environments.
Table of contents provided by Syndetics
- Foreword to the Second Edition (p. xi)
- Preface (p. xiii)
- Acknowledgments (p. xxi)
- About the Authors (p. xxii)
- Chapter 1 Introduction (p. 1)
- Artificial Intelligence (p. 1)
- Basic Concepts (p. 2)
- Symbol Processing (p. 3)
- Neural Networks (p. 4)
- The Internet and the Web (p. 5)
- Intelligent Agents (p. 7)
- Events, Conditions, and Actions (p. 7)
- Taxonomies of Agents (p. 9)
- Agency, Intelligence, and Mobility (p. 9)
- Processing Strategies (p. 10)
- Processing Functions (p. 12)
- Agents and Human-Computer Interfaces (p. 14)
- Using Java for Intelligent Agents (p. 14)
- Overview of the Java Language (p. 14)
- Autonomy (p. 16)
- Intelligence (p. 17)
- Mobility (p. 17)
- Summary (p. 18)
- Chapter 2 Problem Solving Using Search (p. 21)
- Defining the Problem (p. 21)
- State Space (p. 22)
- Search Strategies (p. 23)
- Breadth-First Search (p. 24)
- Depth-First Search (p. 26)
- The SearchNode Class (p. 27)
- Search Application (p. 31)
- Breadth-First Search (p. 40)
- Depth-First Search (p. 41)
- Improving Depth-First Search (p. 42)
- Heuristic Search (p. 44)
- Genetic Algorithms (p. 50)
- Summary (p. 64)
- Exercises (p. 66)
- Chapter 3 Knowledge Representation (p. 69)
- From Knowledge to Knowledge (p. 69)
- Representation (p. 69)
- Procedural Representation (p. 70)
- Relational Representation (p. 71)
- Hierarchical Representation (p. 71)
- Predicate Logic (p. 72)
- Resolution (p. 74)
- Unification (p. 74)
- Frames (p. 75)
- Semantic Nets (p. 76)
- Representing Uncertainty (p. 77)
- Knowledge Interchange Format (p. 78)
- Building a Knowledge Base (p. 80)
- Summary (p. 81)
- Exercises (p. 82)
- Chapter 4 Reasoning Systems (p. 83)
- Reasoning with Rules (p. 83)
- Forward Chaining (p. 86)
- A Forward-Chaining Example (p. 88)
- Backward Chaining (p. 90)
- Fuzzy Rule Systems (p. 92)
- The Rule Application (p. 94)
- Rules (p. 97)
- Clauses (p. 98)
- Variables (p. 101)
- Rule Variables (p. 102)
- Boolean Rule Base (p. 105)
- Forward-Chaining Implementation (p. 107)
- Backward-Chaining Implementation (p. 111)
- The FuzzyRuleBase Classes (p. 113)
- FuzzyRule (p. 113)
- FuzzyClauses (p. 115)
- ContinuousFuzzyRuleVariable (p. 116)
- FuzzySet (p. 119)
- Trapezoid Fuzzy Set Class (p. 125)
- TriangleFuzzySet Class (p. 125)
- ShoulderFuzzySet Class (p. 126)
- WorkingFuzzySet Class (p. 127)
- FuzzyRuleBase (p. 127)
- Fuzzy Forward-Chaining Implementation (p. 133)
- Rule Application Implementation (p. 137)
- The Vehicles RuleBase Implementation (p. 138)
- The Motor RuleBase Implementation (p. 143)
- Planning (p. 145)
- Summary (p. 147)
- Exercises (p. 148)
- Chapter 5 Learning Systems (p. 149)
- Overview (p. 149)
- Learning Paradigms (p. 151)
- Neural Networks (p. 152)
- Back Propagation (p. 153)
- Kohonen Maps (p. 155)
- Decision Trees (p. 157)
- Information Theory (p. 157)
- Learn Application (p. 158)
- Continuous Variables (p. 159)
- Discrete Variables (p. 161)
- The DataSet Class (p. 161)
- BackProp Implementation (p. 169)
- Kohonen Map Implementation (p. 177)
- Decision Tree Implementation (p. 183)
- The Learn Application Implementation (p. 194)
- Summary (p. 198)
- Exercises (p. 198)
- Chapter 6 Agents and Multiagent Systems (p. 201)
- Transition from AI to IA (p. 201)
- Perception (p. 203)
- Action (p. 204)
- Multiagent Systems (p. 204)
- Blackboards (p. 206)
- Communication (p. 207)
- Knowledge Query and Manipulation Language (KQML) (p. 208)
- Agent Standards (p. 209)
- FIPA (p. 210)
- OMG (p. 211)
- Cooperating Agents (p. 211)
- Multiagent Planning (p. 212)
- Competing Agents (p. 213)
- Negotiation (p. 213)
- Agent Software Engineering Issues (p. 214)
- Designing Agents (p. 215)
- Summary (p. 216)
- Exercises (p. 218)
- Chapter 7 Intelligent Agent Framework (p. 219)
- Requirements (p. 219)
- Design Goals (p. 220)
- Functional Specifications (p. 221)
- Intelligent Agent Architecture (p. 222)
- The CIAgent Framework (p. 223)
- The CIAgent Base Classes (p. 224)
- CIAgentEvent (p. 235)
- CIAgentEventListener (p. 235)
- CIAgentEventQueue (p. 237)
- BooleanRuleBase Enhancements (p. 239)
- Discussion (p. 244)
- Summary (p. 244)
- Exercises (p. 245)
- Chapter 8 Personal Agent Manager Application (p. 247)
- Introduction (p. 247)
- The FileAgent (p. 248)
- The PAManagerFrame (p. 254)
- FileAgent Example (p. 260)
- The SchedulerAgent (p. 261)
- The UserNotificationAgent (p. 266)
- The AirfareAgent (p. 266)
- Discussion (p. 281)
- Summary (p. 282)
- Exercises (p. 283)
- Chapter 9 InfoFilter Application (p. 285)
- Introduction (p. 285)
- An Example (p. 290)
- InfoFilterFrame Class (p. 292)
- NewsReaderAgent Class (p. 296)
- URLReaderAgent Class (p. 303)
- NewsArticle Class (p. 307)
- FilterAgent Class (p. 307)
- Discussion (p. 322)
- Summary (p. 323)
- Exercises (p. 323)
- Chapter 10 MarketPlace Application (p. 325)
- Introduction (p. 325)
- An Example (p. 327)
- FacilitatorAgent (p. 333)
- BuySellMessage (p. 338)
- BuyerAgent (p. 339)
- SellerAgent (p. 345)
- Enhanced Buyers and Sellers (p. 351)
- MarketPlace Application (p. 359)
- Discussion (p. 362)
- Summary (p. 363)
- Exercises (p. 364)
- Chapter 11 Java-Based Agent Environments (p. 365)
- Agent Building and Learning Environment (ABLE) (p. 365)
- AgentBuilder (p. 366)
- Aglets (p. 366)
- FIPA-OS (p. 367)
- Gossip (p. 367)
- JADE (p. 367)
- JATLite (p. 368)
- Jess (p. 368)
- Voyager (p. 369)
- ZEUS (p. 370)
- Discussion (p. 370)
- Summary (p. 372)
- Appendix A Bugs and Plants Rule Bases (p. 373)
- The Bugs Rule Base Implementation (p. 373)
- The Plants Rule Base Implementation (p. 376)
- Appendix B Training Data Sets (p. 381)
- Vehicles Data (p. 381)
- Vehicles.dfn (p. 381)
- Vehicles.dat (p. 381)
- Xor Data (p. 382)
- Xor.dfn (p. 382)
- Xor.dat (p. 382)
- XorTree Data (p. 382)
- XorTree.dfn (p. 382)
- XorTree.dat (p. 382)
- Animal Data (p. 383)
- Animal.dfn (p. 383)
- Animal.dat (p. 383)
- Ramp2 Data (p. 383)
- Ramp2.dfn (p. 383)
- Ramp2.dat (p. 383)
- Restaurant Data (p. 384)
- Resttree.dfn (p. 384)
- Resttree.dat (p. 384)
- Kmap 1 Data (p. 384)
- Kmap 1.dfn (p. 385)
- Kmap 1.dat (p. 385)
- ColorTree Data (p. 385)
- ColorTree.dfn (p. 385)
- ColorTree.dat (p. 385)
- Appendix C The CD-ROM (p. 387)
- User Assistance and Information (p. 387)
- Bibliography (p. 389)
- Index (p. 395)
Author notes provided by Syndetics
JOSEPH P. BIGUS, PhD, is a senior technical staff member and research project leader at the IBM T. J. Watson Research Center. Dr. Bigus has led the development of neural network, data mining, and agent technologies at IBM.JENNIFER BIGUS is the principal consultant at Bigus Technologies Inc., where she designs and develops Java and e-business applications. Jennifer played a key role in bringing Java and enterprise Java applications to the IBM AS/400 server.