Diễn đàn tin học, học tập và giải trí

I. ARTIFICIAL INTELLIGENCE: ITS ROOTS AND SCOPE.

1. AI: History and Applications.

From Eden to ENIAC: Attitudes toward intelligence, knowledge and human artifice.

Overview of AI application areas.

Artificial intelligence-a summary.

II. ARTIFICIAL INTELLIGENCE AS REPRESENTATION AND SEARCH.

2. The Predicate Calculus.

Introduction.

The propositional calculus.

The predicate calculus.

Using inference rules to produce predicate calculus expressions.

Application: a logic-based financial advisor.

3. Structures and Strategies for State Space Search.

Introduction.

Graph theory.

Strategies for state space search.

Using the state space to represent reasoning with the predicate calculus.

4. Heuristic Search.

Introduction.

An algorithm for heuristic search.

Admissibility, monotonicity, and informedness.

Using heuristics in games.

Complexity issues.

5. Control and Implementation of State Space Search.

Introduction.

Recursion-based search.

Pattern-directed search.

Production systems.

The blackboard architecture for problem solving.

III. REPRESENTATION AND INTELLIGENCE: THE AI CHALLENGE.

6. Knowledge Representation.

Issues in knowledge representation.

A brief history of AI representational systems.

Conceptual graphs: a network language.

Alternatives to explicit representation.

Agent based and distributed problem solving.

7. Strong Method Problem Solving.

Introduction.

Overview of expert systems technology.

Rule-based expert systems.

Model-based, case based, and hybrid systems.

Planning.

8. Reasoning in Uncertain Situations.

Introduction.

Logic-based adductive inference.

Abduction: alternatives to logic.

The stochastic approach to uncertainty.

IV. MACHINE LEARNING.

9. Machine Learning: Symbol-Based.

Introduction.

A framework for symbol-based learning.

Version space search.

The ID3 decision tree induction algorithm.

Inductive bias and learnability.

Knowledge and learning.

Unsupervised learning.

Reinforcement learning.

10. Machine Learning: Connectionist.

Introduction.

Foundations for connectionist networks.

Perceptron learning.

Backpropagation learning.

Competitive learning.

Hebbian coincidence learning.

Attractor networks or "Memories."

11. Machine Learning: Social and Emergent.

Social and emergent models of learning.

The genetic algorithm.

Classifier systems and genetic programming.

Artificial life and society-based learning.

V. ADVANCED TOPICS FOR AI PROBLEM SOLVING.

12. Automated Reasoning.

Introduction to weak methods in theorem proving.

The general problem solver and difference tables.

Resolution theorem proving.

PROLOG and automated reasoning.

Further issues in automated reasoning.

13. Understanding Natural Language.

Role of knowledge in language understanding.

Deconstructing language: a symbolic analysis.

Syntax.

Syntax and knowledge with ATN parsers.

Stochastic tools for language analysis.

Natural language applications.

VI. LANGUAGES AND PROGRAMMING TECHNIQUES FOR ARTIFICIAL INTELLIGENCE.

14. An Introduction to PROLOG.

Introduction.

Syntax for predicate calculus programming.

Abstract data types (ADTs) in PROLOG.

A production system example in PROLOG.

Designing alternative search strategies.

A PROLOG planner.

PROLOG: ****-predicates, types, and unification.

****-interpreters in PROLOG.

Learning algorithms in PROLOG.

Natural language processing in PROLOG.

15. An Introduction to LISP.

Introduction.

LISP: a brief overview.

Search in LISP: a functional approach to the farmer, wolf, goat, and cabbage problem.

Higher-order functions and procedural abstraction.

Search strategies in LISP.

Pattern matching in LISP.

A recursive unification function.

Interpreters and embedded languages.

Logic programming in LISP.

Streams and delayed evaluation.

An expert system shell in LISP.

Semantic networks and inheritance in LISP.

Object-oriented programming using CLOS.

Learning in LISP: the ID3 algorithm.

VII. EPILOGUE.

16. Artificial Intelligence as Empirical Enquiry.

Introduction.

Artificial intelligence: a revised definition.

The science of intelligent systems.

AI: current issues and future directions.

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Artificial Intelligence: Structures and Strategies for Complex Problem Solving