Applications of learning & planning methods
- editor, Nikolaos G. Bourbakis.
- Singapore ; Teaneck, N.J. : World Scientific, 1991.
- Physical description
- 1 online resource
- Series in computer science ; v. 26.
- Bourbakis, Nikolaos G.
- Includes bibliographical references.
- Ch 1. Embedding learning in a general frame-based architecture / T. Tanaka and T.M. Mitchell
- ch. 2. Connectionist learning with Chebychev networks and analyses of its internal representation / A. Narnatame
- ch. 3. Layered inductive learning algorithms and their computational aspects / H. Madala
- ch. 4. An approach to combining explanation-based and neural learning algorithms / J.W. Shavlik and G.G. Towell
- ch. 5. The application of symbolic inductive learning to the acquisition and recognition of noisy texture concepts / P.W. Pachowicz
- ch. 6. Automating technology adaptation in design synthesis / J.R. Kipps and D.D. Gajski
- ch. 7. Connectionist production systems in local and hierarchical representation / A. Sohn and J.-L. Gaudiot
- ch. 8. A parallel architecture for AI nonlinear planning / S. Lee and K. Chung
- ch. 9. Heuristic tree search using nonparametric statistical inference methods / W. Zhang and N.S.V. Rao
- ch. 10. An A* approach to robust plan recognition for intelligent interfaces / R.J. Calistri-Yeh
- ch. 11. Differential A*: an adaptive search method illustrated with robot path planning for moving obstacles & goals, and an uncertain environment / K.I. Trovato
- ch. 12. Path planning under uncertainty / F. Yegenoglu and H.E. Stephanou
- ch. 13. Knowledge-based acquisition in real-time path planning in unknown space / N.G. Bourbakis
- ch. 14. Path planning for two cooperating robot manipulators / Q. Xue and P.C.-Y. Sheu.
- Publisher's summary
Learning and planning are two important topics of artificial intelligence. Learning deals with the algorithmic processes that make a computing machine able to "learn" and improve its performance during the process of complex tasks. Planning on the other hand, deals with decision and construction processes that make a machine capable of constructing an intelligent plan for the solution of a particular complex problem.This book combines both learning and planning methodologies and their applications in different domains. It is divided into two parts. The first part contains seven chapters on the ongoing research work in symbolic and connectionist learning. The second part includes seven chapters which provide the current research efforts in planning methodologies and their application to robotics.
(source: Nielsen Book Data)
- Machine learning.
- Artificial intelligence.
- COMPUTERS > Enterprise Applications > Business Intelligence Tools.
- COMPUTERS > Intelligence (AI) & Semantics.
- Maschinelles Lernen
- Computerunterstütztes Verfahren
- Künstliche Intelligenz
- Apprentissage automatique.
- Publication date
- Title variation
- Applications of learning and planning methods
- World scientific series in computer science ; vol. 26
- 9789812812414 (electronic bk.)
- 9812812415 (electronic bk.)
- 1299742580 (ebk)
- 9781299742581 (ebk)
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