Artificial Intelligence for Advanced Problem Solving Techniques
Dimitris Vrakas, Ioannis Vlahavas
IGI Global Snippet, Jan 1, 2008 - Computers - 370 pages
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.
59 pages matching theory in this book
Results 1-3 of 59
What people are saying - Write a review
We haven't found any reviews in the usual places.
Extending Classical Planning for Time Research Trends in Optimal and Suboptimal Temporal Planning
Constraint Satisfaction and Scheduling
Principles of Constraint Processing
16 other sections not shown
agent application approach arc consistency Artiﬁcial Intelligence assignment Automated Planning backtracking BOEs CaRBS chapter classiﬁcation clause clustering computer science Conference on Artiﬁcial conﬂict constraint graph constraint programming constraint satisfaction constraint satisfaction problems criteria values deﬁned deﬁnition distribution domain dynamic efﬁcient evaluation example Figure ﬁnal ﬁnd ﬁnding ﬁrst ﬂow values fuzzy genetic algorithms goal Graphplan heuristic hypothesis identiﬁcation ILP systems inductive logic programming input instantiated International Conference knowledge language lexical linguistic LP-SVR machine learning methods Muggleton mutex neural networks neuron node objective function operators optimal parameters path consistency patterns performance planning graph POCL population prediction problem Proceedings proposed Raedt rank position recursive reinforcement learning RI WKH satisﬁed search space semantic simulation solution solving speciﬁc Springer-Verlag structure support vector support vector regression synergetic techniques temporal planning theory tion UCAV unsupervised learning variables WKDW