Artificial Intelligence for Advanced Problem Solving Techniques

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Vlahavas, Ioannis
IGI Global, Jan 31, 2008 - Education - 388 pages
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One of the most important functions of artificial intelligence, automated problem solving, consists mainly of the development of software systems designed to find solutions to problems. These systems utilize a search space and algorithms in order to reach a solution.

Artificial Intelligence for Advanced Problem Solving Techniques offers scholars and practitioners cutting-edge research on algorithms and techniques such as search, domain independent heuristics, scheduling, constraint satisfaction, optimization, configuration, and planning, and highlights the relationship between the search categories and the various ways a specific application can be modeled and solved using advanced problem solving techniques.

 

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Contents

Extending Classical Planning for Time Research Trends in Optimal and Suboptimal Temporal Planning
23
Constraint Satisfaction and Scheduling
62
Principles of Constraint Processing
63
Stratified Constraint Satisfaction Networks in Synergetic MultiAgent Simulations of Language Evolution
107
SoftConstrained Linear Programming Support Vector Regression for Nonlinear BlackBox Systems Identification
137
Machine Learning
147
Reinforcement Learning and Automated Planning A Survey
148
Induction as a Search Procedure
166
Optimising Object Classification Uncertain ReasoningBased Analysis Using CaRBS Systematic Research Algorithms
234
Application of Fuzzy Optimization in Forecasting and Planning of Construction Industry
254
Rank Improvement Optimization Using PROMETHEE and Trigonometric Differential Evolution
266
Genetic Algorithms and Programming
283
Parallelizing Genetic Algorithms A Case Study
284
Using Genetic Programming to Extract Knowledge from Artificial Neural Networks
308
Compilation of Reference
328
About the Contributors
359

Single and MultiOrder Neurons for Recursive Unsupervised Learning
217
Optimization
233

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Page xviii - I would like to acknowledge the help of all involved in the collation and review process of the book, without whose support the project could not have been satisfactorily completed.

About the author (2008)

Dr. Ioannis Vlahavas is a professor at the Department of Informatics at the Aristotle University of Thessaloniki. He received his Ph.D. degree in Logic Programming Systems from the same University in 1988. During the first half of 1997 he was a visiting scholar at the Department of CS at Purdue University. He specializes in logic programming, knowledge based and AI systems and he has published over 100 papers, 9 book chapters and co-authored 4 books in these areas. He teaches logic programming, AI, expert systems, and DSS. He has been involved in more than 15 research projects, leading most of them. He was the chairman of the 2nd Hellenic Conference on AI and the local organizer of the 2nd International Summer School on AI Planning. He is leading the Logic Programming and Intelligent Systems Group (LPIS Group, lpis.csd.auth.gr) (more information at www.csd.auth.gr/~vlahavas)

Dimitris Vrakas is finishing his PhD in Distributed Planning and Scheduling at the Dept of Informatics of the Aristotle University of Thessaloniki. His interests also include Machine Learning, Problem Solving and Heuristic Search Algorithms. He has published several articles and presented various papers on important aspects of Automated Planning such as Learning aided Planning Systems. He has taken part in several projects such as PacoPlan, a web–based system combining Planning and Constraint Programming. He is a member of the American Association for Artificial Intelligence, the Association of Greek Informaticians and the Hellenic Society for Artificial Intelligence. [Editor]

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