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Algorithms and Architectures of Artificial Intelligence.
Title:
Algorithms and Architectures of Artificial Intelligence.
Author:
Tyugu, E.
ISBN:
9781607502623
Personal Author:
Physical Description:
1 online resource (184 pages)
Series:
Frontiers in Artificial Intelligence and Applications
Contents:
Title page -- Contents -- Introduction -- Language of algorithms -- Knowledge Handling -- Knowledge and knowledge systems -- Abstract representation of knowledge systems -- Examples of deductive systems -- Brute force deduction and value propagation -- Clausal calculi and resolution -- Language -- Inference rule - resolution -- Resolution strategies -- Pure Prolog -- Nonmonotonic theories -- Production rules -- Decision tables -- Rete algorithm -- Semantic networks -- Frames -- Knowledge architecture -- Hierarchical connection -- Semantic connection -- Union -- Operational connection -- Examples of knowledge architectures -- Ontologies and knowledge systems -- Summary -- Exercises -- Search -- Search problem -- Exhaustive search methods -- Breadth-first search -- Depth-first search -- Search on binary trees -- Heuristic search methods -- Best-first search -- Beam search -- Hill-climbing -- Constrained hill-climbing -- Search with backtracking -- Search on and-or trees -- Search with dependency-directed backtracing -- Branch-and-bound search -- Stochastic branch and bound search -- Minimax search -- Alpha-beta pruning -- Specific search methods -- A* algorithm -- Unification -- Dictionary search -- Simulated annealing -- Discrete dynamic programming -- Viterby algorithms -- Forward search and backward search -- Hierarchy of search methods -- Exercises -- Learning and Decision Making -- Learning for adaptation -- Parametric learning -- Adaptive automata -- Symbolic learning -- Concept learning as search in a hypothesis space -- Specific to general concept learning -- General to specific concept learning -- Inductive inference -- Learning with an oracle -- Inductive logic programming -- Learning by inverting resolution -- Massively parallel learning in genetic algorithms -- Learning in neural nets -- Perceptrons -- Hopfield nets -- Hamming nets.

Comparator -- Carpenter-Grossberg classifier -- Kohonen's feature maps -- Bayesian networks -- Taxonomy of neural nets -- Data clustering -- Sequential leader clustering -- K-means clustering -- Specific learning algorithms -- Learning decision trees from examples -- Learning productions from examples -- Discovering regularities in monotonous systems -- Discovering relations and structure -- Summary -- Exercises -- Problem Solving and Planning -- Constraint satisfaction problem -- Consistency algorithms -- Binary consistency -- Path consistency -- Propagation algorithms -- Functional constraint networks -- Computational problems and value propagation -- Equational problem-solver -- Minimizing an algorithm -- Lattice of problems on functional constraint network -- Higher-order constraint propagation -- Special algorithms of constraint solving -- Clustering of equations -- Interval propagation -- Program synthesis -- Deductive synthesis of programs -- Inductive synthesis of programs -- Transformational synthesis of programs -- Structural synthesis of programs -- Planning -- Scheduling -- Intelligent agents -- Agent architectures -- Agent communication languages -- Implementation of agents -- Reflection -- Exercises -- References -- Subject Index.
Abstract:
This book gives an overview of methods developed in artificial intelligence for search, learning, problem solving and decision-making. It gives an overview of algorithms and architectures of artificial intelligence that have reached the degree of maturity when a method can be presented as an algorithm, or when a well-defined architecture is known, e.g. in neural nets and intelligent agents. It can be used as a handbook for a wide audience of application developers who are interested in using artificial intelligence methods in their software products. Parts of the text are rather independent, so that one can look into the index and go directly to a description of a method presented in the form of an abstract algorithm or an architectural solution. The book can be used also as a textbook for a course in applied artificial intelligence. Exercises on the subject are added at the end of each chapter. Neither programming skills nor specific knowledge in computer science are expected from the reader. However, some parts of the text will be fully understood by those who know the terminology of computing well.
Local Note:
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2017. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
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