Prabhas Chongstitvatana

email prabhas at chula dot ac dot th

email prabhas at chula dot ac dot th

Foundations of artificial intelligence, solving problems by
searching, knowledge representation, first-order logic, inference in
first-order logic, language processing, machine learning.

30 Oct 2012 Mid term exam will be given on 6 Nov 2012, 1:15 - 2:15 pm in class

1 Nov 2012 My lecture is updated Philosophy of AI AI as search

20 Nov 2012 lecture note: Natural language Top-down parser

21 Nov 2012 Final exam is on 11 Dec 2012, 2-4pm. lecture note Recursive programming with List

28 Nov 2012 Lecture on Genetic Algorithms

2) Solving Problems by Searching Boonserm slide 1 AI as search

3) Representation, Reasoning, and Logic Boonserm slide 3

4) First-Order Logic

5) Language processing Boonserm slide 4 (NLP) Natural language Top-down parser

6) Speech Recognition

7) Machine Learning Boonserm slide 5 (ML)

8) Computing by evolution Genetic Algorithms

9) Neural Networks Nervous system

Final examination

9 Oct 2012 Complete your State-Space drawing of 15-puzzle, labelling it with values from your heuristic functions.

23 Oct 2012 Try out my puzzle.c Arrange your tool to compile and run it. The give source does blind search with Depth-First-Search. Try to make it Breadth-First-Search. Run both versions and observe the difference in terms of the number of nodes explored and the quality of solutions (the number of move to solve the problem).

30 Oct 2012 Prepare for your midterm. At most four questions will be presented.

13 Nov 2012 Write a naive top-down parser. Use any language that you are comfortable with. See if it can parse the examples in class.

27 Nov 2012 Try applet of GA. Run several examples to get the feeling how GA solve these problems and how to set up parameters.

Final exam 30 %

Midterm exam 20 %

Class work 30 %

Assignments 20 %

Total 100 %

1) This one is a standard AI text used in US.

Stuart Russell, Peter Norvig, "Artificial Intelligence: A Modern
Approach," Prentice Hall, 2010

link to textbook website http://aima.cs.berkeley.edu/

2) If you want to "program" for AI, this one is a must:

Paradigms of Artificial Intelligence Programming: Case Studies in Common
Lisp by Peter Norvig (1991) ISBN-10: 1558601910

3) Boonserm Kijsirikul, "Artificial Intelligence" in Thai, Department of
computer engineering, Chulalongkorn University (free
download here)

4) other AI related free e-book (search on 26 Sept 2012) http://www.e-booksdirectory.com/listing.php?category=28

video of Watson at Jeopardy http://www.youtube.com/watch?v=WFR3lOm_xhE

Example: Video
of a biped robot walking using Evolutionary Algorithm. from the
paper: Chaisukkosol, C. and Chongstitvatana, P., "Evolving control
programs for a biped static walker", IEEE Inter. Conf. on Humanoid Robots,
Waseda, Tokyo, November 22-24, 2001. full
paper

Famous historical AI program : eliza

Application of AI, Data Mining: Using WEKA video (20 min) http://www.youtube.com/watch?v=m7kpIBGEdkI

Genetic
Algorithm Viewer 1.0 is a demonstration applet of the functioning of a
Genetic Algorithm (GA). It aims at showing the power of GA and of the main
mechanisms used while permitting a certain form of visualization of the
general functioning.

http://www.ads.tuwien.ac.at/raidl/tspga/TSPGA.html

Visualisation of Genetic Algorithms for the Traveling Salesman Problem in Java

http://www.stellaralchemy.com/ice/

The applet executes what is called a genetic algorithm (GA). To facilitate understanding, every step in the GA is animated. This particular GA evolves solutions to a simple board game. This program also detects and reports if any irreducibly complex (IC) solutions arise during the evolution of its population of solutions to the board game.

http://www.obitko.com/tutorials/genetic-algorithms/example-function-minimum.php

Minimum of Function: The problem is expressed as looking for extreme of a function. Some function is given and GA tries to find minimum of the function. For other problems we just have to define search space and the fitness function which means to define the function, which we want to find extreme for.

http://www.ads.tuwien.ac.at/raidl/tspga/TSPGA.html

Visualisation of Genetic Algorithms for the Traveling Salesman Problem in Java

http://www.stellaralchemy.com/ice/

The applet executes what is called a genetic algorithm (GA). To facilitate understanding, every step in the GA is animated. This particular GA evolves solutions to a simple board game. This program also detects and reports if any irreducibly complex (IC) solutions arise during the evolution of its population of solutions to the board game.

http://www.obitko.com/tutorials/genetic-algorithms/example-function-minimum.php

Minimum of Function: The problem is expressed as looking for extreme of a function. Some function is given and GA tries to find minimum of the function. For other problems we just have to define search space and the fitness function which means to define the function, which we want to find extreme for.

15-puzzle (preliminary, only Depth-First-Search) puzzle.c (complete) puzzle2.c

Top-down parser package

Recursive programming with List

last update 4 Dec 2012