1. Outline Intro to Representation and Heuristic Search Machine Learning (Clustering) and My Research

2. Introduction to Representation The representation function is to capture the critical features of a problem and make that information accessible to a problem solving procedure Expressiveness (the result of the feature abstracted) and efficiency (the computational complexity) are major dimensions for evaluating knowledge representation

3. Introduction to Search Consider “tic-tac-toe” Starting with an empty board, The first player can place a X on any one of nine places Each move yields a different board that will allow the opponent 8 possible responses and so on…

4. Introduction to Search We can represent this collection of possible moves by regarding each board as a state in a graph The link of the graph represent legal move The resulting structure is a state space graph

5. “tic-tac-toe” state space graph

6. Introduction to Search Human use intelligent search Human do not do exhaustive search The rules are known as heuristics, and they constitute one of the central topics of AI search

7. State Space Representation State space search characterizes problem solving as the process of finding a solution path form the start state to a goal A goal may describe a state, such as winning board in tic-tac-toe

8. Introduction Consider heuristic in the game of tic-tac-toe A simple analysis put the total number of states for 9! Symmetry reduction decrease the search space Thus, there are not 9 but 3 initial moves: to a corner to the center of a side to the center of the grid

9. Introduction

10. Use of symmetry on the second level further reduces the number of path to 3* 12 * 7! A simple heuristic, can almost eliminate search entirely: we may move to the state in which X has the most winning opportunity In this case, X takes the center of the grid as the first step

11. Introduction

13. Outline Intro to Representation and Heuristic Search Machine Learning (Clustering) and My Research

14. Clustering Clustering is trying to find similar groups based on given dimensions It is know as unsupervised learning

15. K-means Clustering

20. Experiment setup: HSSP matrix: 1b25

21. Representation of Segment Sliding window size: 9 Each window corresponds to a sequence segment, which is represented by a 9 × 20 matrix plus additional nine corresponding secondary structure information obtained from DSSP. More than 560,000 segments (413MB) are generated by this method. DSSP: Obtain 2 nd Structure information

22. HSSP-BLOSUM62 Measure

23. Research Topics

24. Future Works