2. Artificial Intelligence Midterm 고려대학교 컴퓨터학과 자연어처리 연구실 임 해 창

3. KU NLP Artificial Intelligence2 Number of Problems  Chapter 15 LISP (1 Problem): Homework 이해  Chapter 12.2 Resolution (1 Problem):algorithm 이해  Chapter 14 PROLOG (1 Problem)  Chapter 3 State Space Search (1 Problem)  Chapter 4 Heuristic Search (2 Problems) total = 6 Problems

4. KU NLP Artificial Intelligence3 Chapter 2 Predicate Calculus  Propositional Calculus  Predicate Calculus  Verify Sentence  Inference Rules  Unification (Lisp Homework)  A Logical System

5. KU NLP Artificial Intelligence4 Chapter 15 LISP  Recursion Templates  Single or Double_Test Tail Recursion  Single-Test Augmenting Recursion  List_Consing Recursion  Simultaneous Recursion on Several Variables  Conditional Augmentation  Multiple Recursion  CAR/CDR Recursion  Unification (Homework) unify, match_var, occursp, is-constant-p, varp add-substitution, get-binding, get-binding-value  Verify_sentence(Homework)

6. KU NLP Artificial Intelligence5 12.2 Resolution Theorem Proving  12.2.1 Introduction - Resolution Principle  12.2.2 Producing the Clause Form  12.2.3 Resolution Proof Procedure  12.2.4 Strategies for Resolution  12.2.5 Answer Extraction

7. KU NLP Artificial Intelligence6 14 Introduction to PROLOG  14.1.1 Representing Facts and Rules  14.1.2 Creating, Changing, and Monitoring the PROLOG Environment  14.1.3 Lists and Recursion in PROLOG  14.1.4 Recursive Search in PROLOG  14.1.5 The Use of Cut  Homework (Parser with generating Parse tree)

8. KU NLP Artificial Intelligence7 Ch 3. State Space Search  3.1 Graph Theory  Structures for state space search  state space representation of problems  3.2 Strategies for state space search  Data-Driven and Goal-Driven Search  Depth-First and Breadth-First Search  3.3 Using the State Space to Represent Reasoning with the Predicate Calculus  State Space Description of a Logical System  AND/OR graphs  Examples and Applications

9. KU NLP Artificial Intelligence8 Ch 4. Heuristic Search  4.0 Introduction(Heuristic)  4.1 An Algorithm for Heuristic Search  4.1.1 Implementing Best-First Search  4.1.2 Implementing Heuristic Evaluation Functions  4.1.3 Heuristic Search and Expert Systems  4.3 Using Heuristics in Games  4.3.1 Minimax Procedure  4.3.2 Minimaxing to Fixed Ply Depth  4.3.3 Alpha-Beta Procedure