Distributed Artificial Intelligence, Agent Technology, and Collaborative Applications (Advances in Intelligent Information Technologies)

✍ Scribed by Vijayan Sugumaran

• Publisher: Information Science Reference
• Year: 2008
• Tongue: English
• Leaves: 451
• Edition: 1
• Category: Library

✦ Synopsis

Cutting-edge developments in artificial intelligence are now driving applications that are only hinting at the level of value they will soon contribute to organizations, consumers, and societies across all domains. Distributed Artificial Intelligence, Agent Technology, and Collaborative Applications offers an enriched set of research articles in artificial intelligence (AI), covering significant AI subjects such as information retrieval, conceptual modeling, supply chain demand forecasting, and machine learning algorithms. This comprehensive collection provides libraries with a one-stop resource to equip the academic, industrial, and managerial communities with an in-depth look into the most pertinent AI advances that will lead to the most valuable applications.

✦ Table of Contents

Title......Page 2
Table of Contents......Page 6
Detailed Table of Contents......Page 10
Preface......Page 18
Designing Multi-Agent Systems from Logic Specifications: A Case Study......Page 26
Multi-Agent Architecture for Knowledge-Driven Decision Support......Page 53
A Decision Support System for Trust Formalization......Page 72
Using Misunderstanding and Discussion in Dialog as a Knowledge Acquisition or Enhancement Process......Page 90
Improving E-Trade Auction Volume by Consortium......Page 116
Extending Loosely Coupled Federated Information Systems Using Agent Technology......Page 141
Modeling Fault Tolerant and Secure Mobile Agent Execution in Distributed Systems......Page 157
Search Engine Performance Comparisons......Page 173
A User-Centered Approach for Information Retrieval......Page 190
Classification and Retrieval of Images from Databases Using Rough Set Theory......Page 204
Supporting Text Retrieval by Typographical Term Weighting......Page 224
Web Mining by Automatically Organizing Web Pages into Categories......Page 239
Mining Matrix Pattern from Mobile Users......Page 257
Conceptual Modeling of Events for Active Information Systems......Page 286
Information Modeling and the Problem of Universals......Page 298
Empirical Inference of Numerical Information into Causal Strategy Models by Means of Artificial Intelligence......Page 315
Improving Mobile Web Navigation Using N-Grams Prediction Models......Page 339
Forecasting Supply Chain Demand Using Machine Learning Algorithms......Page 353
Supporting Demand Supply Network Optimization with Petri Nets......Page 391
Compilation of References......Page 409
About the Contributors......Page 441
Index......Page 448