Computational Intelligence: A Logical Approach

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9780195102703: Computational Intelligence: A Logical Approach

This introductory textbook on Artificial Intelligence (AI) is aimed at junior/senior undergraduate and graduate level students. The book will weave a unifying theme among the core concepts that underlie the discipline of AI. While the text makes use of Prolog as its primary programming language, class testers have successfully substituted a Lisp-like pseudocode. The book encourages the student to expore, implement and experiment with a series of progressively richer logic-based representations that can capture the essential features of more and more demanding tasks and environments. The unifying theme will feature an intelligent agent acting in its own environment. This will serve to place the core concepts of AI in a coherent and cohesive framework, making it easier to teach and learn from. This approach will clarify and integrate representation and reasoning fundamentals and lead the students from simple to complex ideas with clear motivation. The authors have developed AI representation schemes and describe their use for interesting and popular applications, such as natural language vision, robotics, game playing, and expert systems. The manuscript has been class tested in a number of different courses in Canada, Japan, and Europe. Virtually every university and college has an interdisciplinary course on artificial intelligence. The enrollment in such courses is rising, as many interdisciplinary programs, such as cognitive science, psychology, linguistics, engineering, medicine, business and philosophy, recommend the course.

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Contenuti:

  • Preface
  • 1: Computational Intelligence and Knowledge:
  • 1.1: What is Computational Intelligence?
  • 1.2: Agents in the World
  • 1.3: Representation and Reasoning
  • 1.4: Applications
  • 1.5: Overview
  • 1.6: References and Further Reading
  • 1.7: Exercises
  • 2: A Representation and Reasoning System:
  • 2.1: Introduction
  • 2.2: Representation and Reasoning Systems
  • 2.3: Simplifying assumptions of the initial RRS
  • 2.4: Datalog
  • 2.5: Semantics
  • 2.6: Questions and Answers
  • 2.7: Proofs
  • 2.8: Extending the Language with Functional Symbols
  • 2.9: References and Further Reading
  • 2.10: Exercises
  • 3: Using Definite Knowledge:
  • 3.1: Introduction
  • 3.2: Case Study: House Wiring
  • 3.3: Discussion
  • 3.5: Case-Study: Repesenting Abstract Concepts
  • 3.6: Applications in Natural Language Processing
  • 3.7: References and Further Reading
  • 3.8: Exercises
  • 4: Searching:
  • 4.1: Why Search?
  • 4.2: Graph Searching
  • 4.3: A Generic Searching Algorithm
  • 4.4: Blind Search Strategies
  • 4.5: Heuristic Search
  • 4.6: Refinements to Search Strategies
  • 4.7: Constraint Satisfaction Problems
  • 4.8: References and Further Reading
  • 4.9: Exercises
  • 5: Representing Knowledge:
  • 5.1: Introduction
  • 5.2: Defining a solution
  • 5.3: Choosing a Representation Language
  • 5.4: Mapping a problem to representation
  • 5.5: Choosing an inference procedure
  • 5.6: References and Further Reading
  • 5.7: Exercises
  • 6: Knowledge Engineering:
  • 6.1: Introduction
  • 6.2: Knowledge-Based System Architecture
  • 6.3: Meta-Interpreters
  • 6.4: Querying the User
  • 6.5: Explanation
  • 6.6: Debugging Knowledge Bases
  • 6.7: A Meta-Interpreter with Search
  • 6.8: Unification
  • 6.9: References and Further Reading
  • 6.10: Exercises
  • 7: Beyond Definite Knowledge:
  • 7.1: Equality
  • 7.2: Integrity Constraints
  • 7.3: Complete Knowledge Assumption
  • 7.4: Disjunctive Knowledge
  • 7.5: Explicit Quantification
  • 7.6: First-order predicate calculus
  • 7.7: Modal Logic
  • 7.8: References and Further Reading
  • 7.9: Exercises
  • 8: Actions and Planning:
  • 8.1: Introduction
  • 8.2: Representations of Actions and Change
  • 8.3: Reasoning with World Representations
  • 8.4: References and Further Reading
  • 8.5: Exercises
  • 9: Assumption-based Reasoning:
  • 9.1: Introduction
  • 9.2: An Assumption-Based Reasoning Framework
  • 9.3: Default Reasoning
  • 9.4: Abduction
  • 9.5: Evidential and Causal Reasoning
  • 9.6: Algorithms for Assumption-based Reasoning
  • 9.7: References and Further Reading
  • 9.8: Exercises
  • 10: Using Uncertain Knowledge:
  • 10.1: Introduction
  • 10.2: Probability
  • 10.3: Independence Assumptions
  • 10.4: Making Decisions Under Uncertainty
  • 10.5: References and Further Reading
  • 10.6: Exercises
  • 11: Learning:
  • 11.1: Introduction
  • 11.2: Learning as choosing the best representation
  • 11.3: Case-based reasoning
  • 11.4: Learning as refining the hypothesis space
  • 11.5: Learning Under Uncertainty
  • 11.6: Explanation-based Learning
  • 11.7: References and Further Reading
  • 11.8: Exercises
  • 12: Building Situated Robots:
  • 12.1: Introduction
  • 12.2: Robotic Systems
  • 12.3: The Agent function
  • 12.4: Designing Robots
  • 12.5: Uses of Agent models
  • 12.6: Robot Architectures
  • 12.7: Implementing a Controller
  • 12.8: Robots Modelling the World
  • 12.9: Reasoning in Situated Robots
  • 12.10: References and Further Reading
  • 12.11: Exercises
  • Appendices
  • A Glossary
  • B The Prolog Programming Language
  • B.1 Introduction
  • B.2 Interacting with Prolog
  • B.3 Syntax
  • B.5 Database Relations
  • B.6 Returning All Answers
  • B.7 Input and Output
  • B.8 Controlling Search
  • C.Some more Implemented Systems
  • C.1 Bottom-Up Interpreters
  • C.2 Top-down Interpreters
  • C.3 A Constraint Satisfaction Problem Solver
  • C.4 Neural Network Learner
  • C.5 Partial-Order Planner
  • C.6 Implementing Belief Networks
  • C.7 Robot Controller

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