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Test Bank for Artificial Intelligence: Foundations of Computational Agents (2nd Edition) by David L. Poole

By: David L. Poole , Alan K. Mackworth
ISBN-10: 110719539X
/ ISBN-13: 9781107195394

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Format: Downloadable ZIP Fille
Authors: David L. Poole , Alan K. Mackworth
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Table of content:

Title
Copyright
Contents
Figures
Preface
Part I Agents in the World: What are Agents and How Can They be Built?
Chapter 1: Artificial Intelligence and Agents
1.1 What is Artificial Intelligence?
1.1.1 Artificial and Natural Intelligence
1.2 A Brief History of Artificial Intelligence
1.2.1 Relationship to Other Disciplines
1.3 Agents Situated in Environments
1.4 Designing Agents
1.4.1 Design Time, Offline and Online Computation
1.4.2 Tasks
1.4.3 Defining a Solution
1.4.4 Representations
1.5.1 Modularity
1.5 Agent Design Space
1.5.2 Planning Horizon
1.5.3 Representation
1.5.4 Computational Limits
1.5.5 Learning
1.5.6 Uncertainty
1.5.7 Preference
1.5.8 Number of Agents
1.5.9 Interaction
1.5.10 Interaction of the Dimensions
1.6 Prototypical Applications
1.6.1 An Autonomous Delivery Robot
1.6.2 A Diagnostic Assistant
1.6.3 An Intelligent Tutoring System
1.6.4 A Trading Agent
1.6.5 Smart House
1.7 Overview of the Book
1.8 Review
1.9 References and Further Reading
1.10 Exercises
Chapter 2: Agent Architectures and Hierarchical Control
2.1 Agents
2.2 Agent Systems
2.3 Hierarchical Control
2.4 Acting with Reasoning
2.4.1 Agents Modeling the World
2.4.2 Knowledge and Acting
2.4.3 Design Time and Offline Computation
2.4.4 Online Computation
2.5 Review
2.6 References and Further Reading
2.7 Exercises
Part II Reasoning, Planning and Learning with Certainty
Chapter 3: Searching for Solutions
3.1 Problem Solving as Search
3.2 State Spaces
3.3 Graph Searching
3.3.1 Formalizing Graph Searching
3.4 A Generic Searching Algorithm
3.5 Uninformed Search Strategies
3.5.1 Breadth-First Search
3.5.2 Depth-First Search
3.5.3 Iterative Deepening
3.5.4 Lowest-Cost-First Search
3.6 Heuristic Search
3.6.1 A∗ Search
3.6.2 Designing a Heuristic Function
3.7 Pruning the Search Space
3.7.1 Cycle Pruning
3.7.2 Multiple-Path Pruning
3.7.3 Summary of Search Strategies
3.8 More Sophisticated Search
3.8.1 Branch and Bound
3.8.2 Direction of Search
3.8.3 Dynamic Programming
3.9 Review
3.10 References and Further Reading
3.11 Exercises
Chapter 4: Reasoning with Constraints
4.1 Possible Worlds, Variables, and Constraints
4.1.1 Variables and Worlds
4.1.2 Constraints
4.1.3 Constraint Satisfaction Problems
4.2 Generate-and-Test Algorithms
4.3 Solving CSPs Using Search
4.5 Domain Splitting
4.6 Variable Elimination
4.7 Local Search
4.7.1 Iterative Best Improvement
4.7.2 Randomized Algorithms
4.7.3 Local Search Variants
4.7.4 Evaluating Randomized Algorithms
4.7.5 Random Restart
4.8 Population-Based Methods
4.9 Optimization
4.9.1 Systematic Methods for Optimization
4.9.2 Local Search for Optimization
4.10 Review
4.11 References and Further Reading
4.12 Exercises
Chapter 5: Propositions and Inference
5.1 Propositions
5.1.1 Syntax of Propositional Calculus
5.1.2 Semantics of the Propositional Calculus
5.2 Propositional Constraints
5.2.1 Clausal Form for Consistency Algorithms
5.2.2 Exploiting Propositional Structure in Local Search
5.3 Propositional Definite Clauses
5.3.1 Questions and Answers
5.3.2 Proofs
5.4 Knowledge Representation Issues
5.4.1 Background Knowledge and Observations
5.4.2 Querying the User
5.4.3 Knowledge-Level Explanation
5.4.4 Knowledge-Level Debugging
5.5 Proving by Contradiction
5.5.1 Horn Clauses
5.5.2 Assumables and Conflicts
5.5.3 Consistency-Based Diagnosis
5.5.4 Reasoning with Assumptions and Horn Clauses
5.6 Complete Knowledge Assumption
5.6.1 Non-monotonic Reasoning
5.6.2 Proof Procedures for Negation as Failure
5.7 Abduction
5.8 Causal Models
5.9 Review
5.10 References and Further Reading
5.11 Exercises
Chapter 6: Planning with Certainty
6.1 Representing States, Actions, and Goals
6.1.1 Explicit State-Space Representation
6.1.2 The STRIPS Representation
6.1.3 Feature-Based Representation of Actions
6.1.4 Initial States and Goals
6.2 Forward Planning
6.3 Regression Planning
6.4 Planning as a CSP
6.4.1 Action Features
6.5 Partial-Order Planning
6.6 Review
6.7 References and Further Reading
6.8 Exercises
Chapter 7: Supervised Machine Learning
7.1 Learning Issues
7.2 Supervised Learning
7.2.1 Evaluating Predictions
7.2.2 Types of Errors
7.3 Basic Models for Supervised Learning
7.3.1 Learning Decision Trees
7.3.2 Linear Regression and Classification
7.4 Overfitting
7.4.2 Regularization
7.4.3 Cross Validation
7.5 Neural Networks and Deep Learning
7.6 Composite Models
7.6.1 Random Forests
7.6.2 Ensemble Learning
7.7 Case-Based Reasoning
7.8 Learning as Refining the Hypothesis Space
7.8.1 Version-Space Learning
7.8.2 Probably Approximately Correct Learning
7.9 Review
7.10 References and Further Reading
7.11 Exercises
Part III Reasoning, Learning and Acting with Uncertainty
Chapter 8: Reasoning with Uncertainty
8.1 Probability
8.1.1 Semantics of Probability
8.1.2 Axioms for Probability
8.1.3 Conditional Probability
8.1.4 Expected Values
8.2 Independence
8.3 Belief Networks
8.3.1 Observations and Queries
8.3.2 Constructing Belief Networks
8.4 Probabilistic Inference
8.4.1 Variable Elimination for Belief Networks
8.4.2 Representing Conditional Probabilities and Factors
8.5 Sequential Probability Models
8.5.1 Markov Chains
8.5.2 Hidden Markov Models
8.5.3 Algorithms for Monitoring and Smoothing
8.5.4 Dynamic Belief Networks
8.5.5 Time Granularity
8.6 Stochastic Simulation
8.6.1 Sampling from a Single Variable
8.6.2 Forward Sampling in Belief Networks
8.6.3 Rejection Sampling
8.6.4 Likelihood Weighting
8.6.5 Importance Sampling
8.6.6 Particle Filtering
8.6.7 Markov Chain Monte Carlo
8.7 Review
8.8 References and Further Reading
8.9 Exercises
Chapter 9: Planning with Uncertainty
9.1 Preferences and Utility
9.1.1 Axioms for Rationality
9.1.2 Factored Utility
9.1.3 Prospect Theory
9.2 One-Off Decisions
9.2.1 Single-Stage Decision Networks
9.3 Sequential Decisions
9.3.1 Decision Networks
9.3.2 Policies
9.3.3 Variable Elimination for Decision Networks
9.4 The Value of Information and Control
9.5 Decision Processes
9.5.1 Policies
9.5.2 Value Iteration
9.5.3 Policy Iteration
9.5.4 Dynamic Decision Networks
9.5.5 Partially Observable Decision Processes
9.6 Review
9.7 References and Further Reading
9.8 Exercises
Chapter 10: Learning with Uncertainty
10.1 Probabilistic Learning
10.1.1 Learning Probabilities
10.1.2 Probabilistic Classifiers
10.1.3 MAP Learning of Decision Trees
10.1.4 Description Length
10.2 Unsupervised Learning
10.2.1 k-Means
10.2.2 Expectation Maximization for Soft Clustering
10.3 Learning Belief Networks
10.3.1 Learning the Probabilities
10.3.2 Hidden Variables
10.3.3 Missing Data
10.3.4 Structure Learning
10.3.5 General Case of Belief Network Learning
10.4 Bayesian Learning
10.5 Review
10.6 References and Further Reading
10.7 Exercises
Chapter 11; Multiagent Systems
11.1 Multiagent Framework
11.2 Representations of Games
11.2.1 Normal Form Games
11.2.2 Extensive Form of a Game
11.2.3 Multiagent Decision Networks
11.3 Computing Strategies with Perfect Information
11.4 Reasoning with Imperfect Information
11.4.1 Computing Nash Equilibria
11.5 Group Decision Making
11.6 Mechanism Design
11.7 Review
11.8 References and Further Reading
11.9 Exercises
Chapter 12; Learning to Act
12.1 Reinforcement Learning Problem
12.2 Evolutionary Algorithms
12.3 Temporal Differences
12.4 Q-learning
12.5 Exploration and Exploitation
12.6 Evaluating Reinforcement Learning Algorithms
12.7 On-Policy Learning
12.8 Model-Based Reinforcement Learning
12.9 Reinforcement Learning with Features
12.9.1 SARSA with Linear Function Approximation
12.10 Multiagent Reinforcement Learning
12.10.1 Perfect-Information Games
12.10.2 Learning to Coordinate
12.11 Review
12.12 References and Further Reading
12.13 Exercises
Part IV Reasoning, Learning and Acting with Individuals and Relations
Chapter 13: Individuals and Relations
13.1 Exploiting Relational Structure
13.2 Symbols and Semantics
13.3 Datalog: A Relational Rule Language
13.3.2 Interpreting Variables
13.3.3 Queries with Variables
13.4 Proofs and Substitutions
13.4.1 Instances and Substitutions
13.4.2 Bottom-up Procedure with Variables
13.4.3 Unification
13.4.4 Definite Resolution with Variables
13.5 Function Symbols
13.5.1 Proof Procedures with Function Symbols
13.6 Applications in Natural Language
13.6.1 Using Definite Clauses for Context-Free Grammars
13.6.5 Enforcing Constraints
13.6.6 Building a Natural Language Interface to a Database
13.6.7 Limitations
13.7 Equality
13.7.1 Allowing Equality Assertions
13.7.2 Unique Names Assumption
13.8 Complete Knowledge Assumption
13.8.1 Complete Knowledge Assumption Proof Procedures
13.9 Review
13.10 References and Further Reading
13.11 Exercises
Chapter 14: Ontologies and Knowledge-Based Systems
14.1 Knowledge Sharing
14.2 Flexible Representations
14.2.1 Choosing Individuals and Relations
14.2.2 Graphical Representations
14.2.3 Classes
14.3 Ontologies and Knowledge Sharing
14.3.1 Uniform Resource Identifiers
14.3.2 Description Logic
14.3.3 Top-Level Ontologies
14.4 Implementing Knowledge-Based Systems
14.4.1 Base Languages and Metalanguages
14.4.2 A Vanilla Meta-Interpreter
14.4.3 Expanding the Base Language
14.4.4 Depth-Bounded Search
14.4.5 Meta-Interpreter to Build Proof Trees
14.4.6 Delaying Goals
14.5 Review
14.6 References and Further Reading
14.7 Exercises
Chapter 15: Relational Planning, Learning,and Probabilistic Reasoning
15.1 Planning with Individuals and Relations
15.1.1 Situation Calculus
15.1.2 Event Calculus
15.2 Relational Learning
15.2.1 Structure Learning: Inductive Logic Programming
15.2.2 Learning Hidden Properties: Collaborative Filtering
15.3 Statistical Relational Artificial Intelligence
15.3.1 Relational Probabilistic Models
15.4 Review
15.5 References and Further Reading
15.6 Exercises
Part V Retrospect and Prospect
Chapter 16: Retrospect and Prospect
16.1 Dimensions of Complexity Revisited
16.2 Social and Ethical Consequences
16.3 References and Further Reading
16.4 Exercises
Appendix A: Mathematical Preliminaries and Notation
A.1 Discrete Mathematics
A.2 Functions, Factors and Arrays
A.3 Relations and the Relational Algebra
References
Index


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