**Artificial Intelligence**

Fall 2003

Tuesdays, 16:10 ~19:00 PM

Instructor: Berlin Chen

Topic List and Schedule:

9/9

Course Overview & Introduction

9/16

The Structure of Agents

HW-01: Exercises 2.5. (Due: 9/30)

9/23

Searching: Uninformed Search: DFS, BFS, IDS, etc. 9/30

Searching: Informed Search: Greedy Best-First, A* Search, etc.

HW-02: Implementation of Greedy and A* Search

for the 8-puzzle problems (Due: 10/21)

(device a heuristic function in addition to those mentioned in the textbook)

HW-02: Implementation of Greedy and A* Search

for the 8-puzzle problems (Due: 10/21)

(device a heuristic function in addition to those mentioned in the textbook)10/7

Searching: Informed Search: Local Search, Genetic algorithms, etc.

10/14

Searching: Constraint Satisfaction

10/21

Searching: Constraint Satisfaction

Searching: Adversarial Search

10/28

Midterm 11/4

Searching: Adversarial Search

HW-03: Exercises 5.7 (Computer Programming) (See HW page)HW-03: Exercises 5.7 (Computer Programming) (See HW page) (Due: 11/18)

11/11

Logical Agent & Propositional Logic 11/18

Logical Agent & Propositional Logic

First-Order Logic and Inference

11/25

Paper Presentation

黃立德:

Evolutionary algorithms, simulated annealing and tabu search: a comparative study

郭炯彬:

An Efficient BDD-Based A* Algorithm

鍾淳文:

A hybrid Artificial Intelligence approach with application to games12/2

Paper Presentation:

趙義雄:

Knowledge-Based Search in Competitive Domains

張志豪

Iterative heuristic search algorithm

劉耀才

An evolutionary autonomous agents approach to image feature extraction

陳善泰

Optimization Algorithms for Bulls and Cows12/9

First-Order Logic and Inference

HW-04: Show the logically equivalent relation of the sentences used in the diagnostic rule and causal rule on P. 259 and 260 (Due: 12/19)

12/16

First-Order Logic and Inference

HW-05: Exercises 9.9, 9.10 (Due: 12/26)

12/23

Knowledge Representation & Planning (Preliminary)

12/30

Knowledge Representation & Planning

1/6

Final Exam

Textbook:

1

Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Prentice-Hall, 2003 (新月圖書代理)

References:

Books:

1Nils J. Nilsson. Artificial Intelligence: A New Synthesis. Morgan Kaufmann, 1998 2Ivan Bratko. Prolog Programming for Artificial Intelligence. Addison-Wesley, 2001 3P. R. Harrison. Common Lisp and Artificial Intelligence. Prentice Hall, 1990 (開發代理) 4Franz Inc. Common Lisp: The Reference. Addison-Wesley, 1988 (開發代理) 5T.M. Mitchell. Machine Learning. McGraw-Hill, 1997 6Nils J. Nilsson. Introduction to Machine Learning, September 26, 1996 7I. H. Witten and E. Frank. Data Mining. Morgan Kaufmann, 2000

Papers:

Grading:

1. Midterm or Final: 30%

2. Homework: 25%

3. Project: 30%

4. Attendance/Other: 15%