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Logic-Based Artificial Intelligence

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Cover of 'Logic-Based Artificial Intelligence'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction to Logic-Based Artificial Intelligence
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    Chapter 2 Concepts of Logical AI
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    Chapter 3 Two Approaches to Efficient Open-World Reasoning
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    Chapter 4 Declarative Problem-Solving Using the DLV System
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    Chapter 5 The Role of Default Logic in Knowledge Representation
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    Chapter 6 Approximations, Stable Operators, Well-Founded Fixpoints and Applications in Nonmonotonic Reasoning
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    Chapter 7 Getting to the Airport: The Oldest Planning Problem in AI
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    Chapter 8 Encoding Domain and Control Knowledge for Propositional Planning
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    Chapter 9 Functional Strips: A More Flexible Language for Planning and Problem Solving
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    Chapter 10 Planning with Natural Actions in the Situation Calculus
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    Chapter 11 Reinventing Shakey
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    Chapter 12 Reasoning Agents in Dynamic Domains
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    Chapter 13 Dynamic Logic for Reasoning About Actions and Agents
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    Chapter 14 Logic-Based Machine Learning
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    Chapter 15 Decision, Nonmonotonic Reasoning and Possibilistic Logic
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    Chapter 16 The Role(s) of Belief in AI
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    Chapter 17 Modeling the Beliefs of other Agents
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    Chapter 18 The Situations We Talk About
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    Chapter 19 Linear Time Datalog and Branching Time Logic
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    Chapter 20 On the Expressive Power of Planning Formalisms
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    Chapter 21 Extending the Smodels System with Cardinality and Weight Constraints
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    Chapter 22 Nonmonotonic Reasoning in LDL++
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    Chapter 23 Towards a Mechanically Checked Theory of Computation
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    Chapter 24 Logic-Based Techniques in Data Integration
Attention for Chapter 10: Planning with Natural Actions in the Situation Calculus
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