1. Integration of Search and Learning Algorithms Eugene Fink

2. Basic intuition An intelligent system should select or invent an effective approach to a given problem.

3. Challenges Select among available search algorithms (or invent a new algorithm) Restate the problem, to improve the efficiency of the selected algorithm

4. Search algorithm search problem

5. Search algorithm search problemsolution ?

6. Search algorithm search problemsolution ? A search module inputs a problem and runs until finding a solution, failing, or reaching a time limit.

7. Speed-up learning algorithm speed-up learning problem

8. Speed-up learning algorithm speed-up learning problem problem and new knowledge

9. Speed-up learning algorithm speed-up learning problem problem and new knowledge search

10. Speed-up learning algorithm speed-up learning problem An algorithm inputs a problem and generates additional knowledge, which improves search efficiency. problem and new knowledge search

11. … or static-analysis algorithm static analysis problem An algorithm inputs a problem and generates additional knowledge, which improves search efficiency. problem and new knowledge search

12. Goals Construct an architecture for integration of multiple search and learning modules Centralized architecture Integration tools

13. Goals Construct an architecture for integration of multiple search and learning modules Develop a control mechanism that chooses appropriate modules for each problem Centralized architecture Integration tools Top-level control decisions Reuse of accumulated knowledge

14. Related work AI systems with multiple learning and search modules: Soar, Prodigy, … Integration tools: Multiagent Planning Architecture (Wilkins and Myers), Plan++ (Yang et al.) Selection among available algorithms: Horvitz, Breese, Russell, Minton,...

15. Shaper system Three main parts: Library of search modules Library of learning modules Top-level control mechanism

16. System outline

18. Control center

19. System outline Manual control Automatic control Control center

20. System outline Manual control Automatic control Control center Given a new problem: Select appropriate modules Apply them to solve the problem Repeat if necessary

21. Features of the top-level control Fast: Less than a second per problem General: Independent of specific modules Allows participation of a human expert: Initial knowledge and interactive advice

22. Features of the automatic control Several learning mechanisms Symbolic and statistical techniques Combining exploitation and exploration

23. Automatic control Solve the problem or learn additional knowledge?

24. Automatic control Which learner to apply? Which past results to use? Solve the problem or learn additional knowledge?

25. Automatic control Which learner to apply? Which past results to use? Invoke the selected learning module Solve the problem or learn additional knowledge?

26. Automatic control Which learner to apply? Which past results to use? Solve or skip the problem? With which search module? Which learned data to use? Invoke the selected learning module Solve the problem or learn additional knowledge?

27. Automatic control Which learner to apply? Which past results to use? Solve or skip the problem? With which search module? Which learned data to use? Invoke the selected learning module Invoke the selected search module Wait for the next problem Solve the problem or learn additional knowledge?

28. Automatic control Which learner to apply? Which past results to use? Solve or skip the problem? With which search module? Which learned data to use? Invoke the selected learning module Invoke the selected search module Wait for the next problem Solve the problem or learn additional knowledge?

29. Performance example Solving a series of 50 problems, and learning which modules are the best. order of solving problems

30. Performance example Solving a series of 50 problems, and learning which modules are the best. order of solving problems

31. Performance example Solving a series of 500 problems, and learning which modules are the best. order of solving problems

32. Applications Past: AI planning (Prodigy) Present: Hardware design (power estimates) Future: Biomedical data, vision,...