Artificial Intelligence for Advanced Problem Solving TechniquesVlahavas, Ioannis, Vrakas, Dimitris 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. |
Contents
| 1 | |
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 |
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 |
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Artificial Intelligence for Advanced Problem Solving Techniques Dimitris Vrakas,Ioannis Vlahavas No preview available - 2008 |
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achieve actions agent algorithm allows applications approach Artificial Intelligence assignment changes chapter classification clause clustering combination complete Conference considered consistency constraint construction defined described distribution domain dynamic effects efficiency evaluation example execution expression extraction Figure final function given global goal graph heuristic hypothesis improve individuals inductive International knowledge language learning linguistic logic programming Machine Learning means methods networks node notes objective obtained operators optimal parameters path patterns performance planners planning planning graph population positive possible prediction preference presented problem Proceedings produced programming proposed rank reasoning recursive relations represent representation respectively rules satisfaction satisfied Science selection shows simulation solution solving space specific step structure Table techniques temporal temporal planning theory tion University values variables
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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.