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Basic intuition
An intelligent system should select or invent an effective approach to a given problem.
Challenges
• Select among available search algorithms (or invent a new algorithm)
• Restate the problem, to improve the efficiency of the selected algorithm
Search algorithm
searchproblem solution ?
A search module inputs a problem and runs until finding a solution, failing, or reaching a time limit.
Speed-up learning algorithm
speed-uplearning
problem
An algorithm inputs a problem and generates additional knowledge, which improves search efficiency.
problem andnew knowledge
search
… or static-analysis algorithm
staticanalysis
problem
An algorithm inputs a problem and generates additional knowledge, which improves search efficiency.
problem andnew knowledge
search
Goals• Construct an architecture for integration of
multiple search and learning modules• Centralized architecture• Integration tools
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
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, ...
Shaper system
Three main parts:
• Library of search modules
• Library of learning modules
• Top-level control mechanism
System outline
Manualcontrol
Automaticcontrol
Control center
Given a new problem:• Select appropriate modules• Apply them to solve the problem• Repeat if necessary
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
Features of the automatic control
• Several learning mechanisms
• Symbolic and statistical techniques
• Combining exploitation and exploration
Automatic control
Which learner to apply?Which past results to use?
Solve the problem or learn additional knowledge?
Automatic control
Which learner to apply?Which past results to use?
Invoke the selectedlearning module
Solve the problem or learn additional knowledge?
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 selectedlearning module
Solve the problem or learn additional knowledge?
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 selectedlearning module
Invoke the selectedsearch module
Wait for the next problem
Solve the problem or learn additional knowledge?
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 selectedlearning module
Invoke the selectedsearch module
Wait for the next problem
Solve the problem or learn additional knowledge?
Performance exampleSolving a series of 50 problems, and learning which modules are the best.
order of solving problems
Performance exampleSolving a series of 50 problems, and learning which modules are the best.
order of solving problems
Performance exampleSolving a series of 500 problems, and learning which modules are the best.
order of solving problems