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Handbook of Knowledge Representation
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  • Handbook of Knowledge Representation
ID: 172716
Frank van Harmelen, Vladimir Lifschitz, Bruce Porter
Wycofany
Knowledge Representation, which lies at the core of Artificial Intelligence, is concerned with encoding knowledge on computers to enable systems to reason automatically.

The Handbook of Knowledge Representation is an up-to-date review of twenty-five key topics in knowledge representation, written by the leaders of each field.

This book is an essential resource for students, researchers and practitioners in all areas of Artificial Intelligence.

* Make your computer smarter
* Handle qualitative and uncertain information
* Improve computational tractability to solve your problems easily

Part I: General Methods in Knowledge Representation and Reasoning

1. Knowledge Representation and Classical Logic
2. Satisfiability Solvers
3. Description Logics
4. Constraint Programming
5. Conceptual Graphs
6. Nonmonotonic Reasoning
7. Answer Sets
8. Belief Revision
9. Qualitative Modeling
10. Model-Based Problem Solving
11. Bayesian Networks

Part II: Classes of Knowledge and Specialized Representations

12. Temporal Representation and Reasoning
13. Spatial Reasoning
14. Physical Reasoning
15. Reasoning about Knowledge and Belief
16. Situation Calculus
17. Event Calculus
18. Temporal Action Logics
19. Nonmonotonic Causal Logic Part III: Knowledge Representation in Applications
20. Knowledge Representation and Question Answering
21. The Semantic Web: Webizing Knowledge Representation
22. Automated Planning
23. Cognitive Robotics
24. Multi-Agent Systems
25. Knowledge Engineering
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