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Chunking with Confidence

Chunking with Confidence. John Laird University of Michigan June 17, 2005 25 th Soar Workshop laird@umich.edu. Basics of Chunking. Compile processing in subgoal into a single rule Rule will fire in future and make subgoal unnecessary. superstate. superstate. substate. substate.

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Chunking with Confidence

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  1. Chunking with Confidence John Laird University of Michigan June 17, 2005 25th Soar Workshop laird@umich.edu

  2. Basics of Chunking • Compile processing in subgoal into a single rule • Rule will fire in future and make subgoal unnecessary.

  3. superstate superstate substate substate substate substate result superstate superstate Basics of Chunking

  4. Original Philosophy Behind Chunking • Chunks cache the processing in a subgoal • Replace problem solving with recognition • Originally assumed goal test was complete • Any path from initial state to goal was valid • Could ignore control knowledge - just impacts efficiency

  5. Changes in Soar • Control knowledge is used to constrain behavior • Goal test may be simplified and “assume” control knowledge • Results are returned immediately when produced • No guaranteed that they fulfill goal • Wandering around looking for food – random decisions in subgoal Wander straight right straight left

  6. Proposal: Symbolic Preferences • Include in chunks the rules that create preferences: they incorporate the “goal concept”. • Not just operator proposals and applications • Impact: • Some chunks will be more specific. • Will not have any impact on selection space chunks. • Data chunking will be more difficult.

  7. Proposal: Numeric Indifferent Preferences • Create chunks only if there is high confidence in result. • Result confidence = confidence in each selected operator for every indifferent decisions on path to result • Include conditions of rules computing expected value • Confidence determined by reinforcement learning • Decayed trailing standard deviation of best choice • Impact: • Chunking will not be immediate when there is indifference • Time to chunk will vary • Won’t chunk over decisions with no clear best choice • Can overcome by deliberately converting to symbolic preferences • Chunking “freezes” high confidence solutions

  8. Nuggets and Coal • Nugget: • Resolves final issues in chunking • Could lead to the promised land = ubiquitous chunking • Coal: • Not implemented • Depends on implementation of reinforcement learning • Must have rewards = goal tests in subgoals

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