Distributed Artificial Intelligence, Agent Technology and Collaborative Applications, 1st Edition

  • Published By:
  • ISBN-10: 1605661457
  • ISBN-13: 9781605661452
  • Grade Level Range: College Freshman - College Senior
  • 314 Pages | eBook
  • Original Copyright 2008 | Published/Released March 2009
  • This publication's content originally published in print form: 2008

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Provides research in artificial intelligence (AI), covering significant AI subjects such as information retrieval, conceptual modeling, supply chain demand forecasting, and machine learning algorithms.

Table of Contents

Front Cover.
Title Page.
Copyright Page.
Editorial Board.
Table of Contents.
Detailed Table of Contents.
1: Distributed Agent Applications and Decision Support.
2: Designing Multi-Agent Systems from Logic Specifications: A Case Study.
3: Multi-Agent Architecture for Knowledge-Driven Decision Support.
4: A Decision Support System for Trust Formalization.
5: Using Misunderstanding and Discussion in Dialog as a Knowledge Acquisition or Enhancement Procecss.
6: Improving E-Trade Auction Volume by Consortium.
7: Extending Loosely Coupled Federated Information Systems Using Agent Technology.
8: Modeling Fault Tolerant and Secure Mobile Agent Execution in Distributed Systems.
9: Search and Retrieval.
10: Search Engine Performance Comparisons.
11: A User-Centered Approach for Information Retrieval.
12: Classification and Retrieval of Images from Databases Using Rough Set Theory.
13: Supporting Text Retrieval by Typographical Term Weighting.
14: Web Mining by Automatically Organizing Web Pages into Categories.
15: Mining Matrix Pattern from Mobile Users.
16: Information Systems and Modeling.
17: Conceptual Modeling of Events for Active Information Systems.
18: Information Modeling and the Problem of Universals.
19: Empirical Inference of Numerical Information into Causal Strategy Models by Means of Artificial Intelligence.
20: Improving Mobile Web Navigation Using N-Grams Prediction Models.
21: Supply Chain Management.
22: Forecasting Supply Chain Demand Using Machine Learning Algorithms.
23: Supporting Demand Supply Network Optimization with Petri Nets.
Compilation of References.
About the Contributors.