Homework Webpage

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 games
12/2		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 Cows
12/9		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:

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