Artificial Intelligence for Advanced Problem Solving Techniques
- 9h 38m
- Dimitris Vrakas, Ioannis PL. Vlahavas (eds)
- IGI Global
- 2008
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.
About the Editors
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.
In this Book
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Multi-Vehicle Missions—Architecture and Algorithms for Distributed Online Planning
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Extending Classical Planning for Time—Research Trends in Optimal and Suboptimal Temporal Planning
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Principles of Constraint Processing
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Stratified Constraint Satisfaction Networks in Synergetic Multi-Agent Simulations of Language Evolution
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Soft-Constrained Linear Programming Support Vector Regression for Nonlinear Black-Box Systems Identification
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Reinforcement Learning and Automated Planning—A Survey
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Induction as a Search Procedure
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Single- and Multi-Order Neurons for Recursive Unsupervised Learning
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Optimising Object Classification—Uncertain Reasoning-Based Analysis Using CaRBS Systematic Research Algorithms
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Application of Fuzzy Optimization in Forecasting and Planning of Construction Industry
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Rank Improvement Optimization Using PROMETHEE and Trigonometric Differential Evolution
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Parallelizing Genetic Algorithms—A Case Study
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Using Genetic Programming to Extract Knowledge from Artificial Neural Networks
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Compilation of References
