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Fundamentals of Artificial Intelligence : Problem Solving and Automated Reasoning
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- Acknowledgment
- 1 Core AI: Problem Solving and Automated Reasoning
- 1.1 Early Milestones
- 1.2 Problem Solving
- 1.3 Automated Reasoning
- 1.4 Structure and Method
- 2 Blind Search
- 2.1 Motivation and Terminology
- 2.2 Depth-First and Breadth-First Search
- 2.3 Practical Considerations
- 2.4 Aspects of Search Performance
- 2.5 Iterative Deepening (and Broadening)
- 2.6 Practice Makes Perfect
- 2.7 Concluding Remarks
- 3 Heuristic Search and Annealing
- 3.1 Hill Climbing and Best-First Search
- 3.2 Practical Aspects of Evaluation Functions
- 3.3 A-Star and IDA-Star
- 3.4 Simulated Annealing
- 3.5 Role of Background Knowledge
- 3.6 Continuous Domains
- 3.7 Practice Makes Perfect
- 3.8 Concluding Remarks
- 4 Adversary Search
- 4.1 Typical Problems
- 4.2 Baseline Mini-Max
- 4.3 Heuristic Mini-Max
- 4.4 Alpha-Beta Pruning
- 4.5 Additional Game-Programming Techniques
- 4.6 Practice Makes Perfect
- 4.7 Concluding Remarks
- 5.1 Toy Blocks
- 5.2 Available Actions
- 5.3 Planning with STRIPS
- 5.4 Numeric Example
- 5.5 Advanced Applications of AI Planning
- 5.6 Practice Makes Perfect
- 5.7 Concluding Remarks
- 6 Genetic Algorithm
- 6.1 General Schema
- 6.2 Imperfect Copies and Survival
- 6.3 Alternative GA Operators
- 6.4 Potential Problems
- 6.5 Advanced Variations
- 6.6 GA and the Knapsack Problem
- 6.7 GA and the Prisoner?s Dilemma
- 6.8 Practice Makes Perfect
- 6.9 Concluding Remarks
- 7 Artificial Life
- 7.1 Emergent Properties
- 7.2 L-Systems
- 7.3 Cellular Automata
- 7.4 Conways? Game of Life
- 7.5 Practice Makes Perfect
- 7.6 Concluding Remarks
- 8 Emergent Properties and Swarm Intelligence
- 8.1 Ant-Colony Optimization
- 8.2 ACO Addressing the Traveling Salesman
- 8.3 Particle-Swarm Optimization
- 8.4 Artificial-Bees Colony, ABC
- 8.5 Practice Makes Perfect
- 8.6 Concluding Remarks
- 9 Elements of Automated Reasoning
- 9.1 Facts and Queries
- 9.2 Rules and Knowledge-Based Systems
- 9.3 Simple Reasoning with Rules
- 9.4 Practice Makes Perfect
- 9.5 Concluding Remarks
- 10 Logic and Reasoning, Simplified
- 10.1 Entailment, Inference, Theorem Proving
- 10.2 Reasoning with Modus Ponens
- 10.3 Reasoning Using the Resolution Principle
- 10.4 Expressing Knowledge in Normal Form
- 10.5 Practice Makes Perfect
- 10.6 Concluding Remarks
- 11 Logic and Reasoning Using Variables
- 11.1 Rules and Quantifiers
- 11.2 Removing Quantifiers
- 11.3 Binding, Unification, and Reasoning
- 11.4 Practical Inference Procedures
- 11.5 Practice Makes Perfect
- 11.6 Concluding Remarks
- 12 Alternative Ways of Representing Knowledge
- 12.1 Frames and Semantic Networks
- 12.2 Reasoning with Frame-Based Knowledge
- 12.3 N-ary Relations in Frames and SNs
- 12.4 Practice Makes Perfect
- 12.5 Concluding Remarks
- 13 Hurdles on the Road to Automated Reasoning
- 13.1 Tacit Assumptions
- 13.2 Non-Monotonicity
- 13.3 Mycin?s Uncertainty Factors
- 13.4 Practice Makes Perfect
- 13.5 Concluding Remarks
- 14 Probabilistic Reasoning
- 14.1 Theory of Probability (Revision)
- 14.2 Probability and Reasoning
- 14.3 Belief Networks
- 14.4 Dealing with More Realistic Domains
- 14.5 Demspter-Shafer Approach: Masses Instead of Probabilities
- 14.6 From Masses to Belief and Plausibility
- 14.7 DST Rule of Evidence Combination
- 14.8 Practice Makes Perfect
- 14.9 Concluding Remarks
- 15 Fuzzy Sets
- 15.1 Fuzziness of Real-World Concepts
- 15.2 Fuzzy Set Membership
- 15.3 Fuzziness versus Other Paradigms
- 15.4 Fuzzy Set Operations
- 15.5 Counting Linguistic Variables
- 15.6 Fuzzy Reasoning
- 15.7 Practice Makes Perfect
- 15.8 Concluding Remarks
- 16 Highs and Lows of Expert Systems
- 16.1 Early Pioneer: Mycin
- 16.2 Later Developments
- 16.3 Some Experience
- 16.4 Practice Makes Perfect
- 16.5 Concluding Remarks
- 17 Beyond Core AI
- 17.1 Computer Vision
- 17.2 Natural Language Processing
- 17.3 Machine Learning
- 17.4 Agent Technology
- 17.5 Concluding Remarks
- 18 Philosophical Musings
- 18.1 Turing Test
- 18.2 Chinese Room and Other Reservations
- 18.3 Engineer?s Perspective
- 18.4 Concluding Remarks
- Bibliography
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A prospective on mathematics and artificial intelligence: Problem solving=Modeling+Theorem proving
- Published: October 2000
- Volume 28 , pages 17–20, ( 2000 )
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- Harvey J. Greenberg
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This is a prospective on the research in the intersection of mathematics and artificial intelligence that I see as having been the most important over the past 10 years and that I think should be pursued vigorously during this decade. Part of this is drawn from my personal research agenda, part is from vast readings, and part is from my editorial position with the Annals of Mathematics and Artificial Intelligence.
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Greenberg, H.J. A prospective on mathematics and artificial intelligence: Problem solving=Modeling+Theorem proving. Annals of Mathematics and Artificial Intelligence 28 , 17–20 (2000). https://doi.org/10.1023/A:1018935718357
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Issue Date : October 2000
DOI : https://doi.org/10.1023/A:1018935718357
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Artificial intelligence : structures and strategies for complex problem solving
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