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A Computational Model of Accelerated Future Learning through Feature Recognition

Building an intelligent agent that simulates human-level learning using machine learning techniques. A Computational Model of Accelerated Future Learning through Feature Recognition. Nan Li Computer Science Department Carnegie Mellon University. Accelerated Future Learning.

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A Computational Model of Accelerated Future Learning through Feature Recognition

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  1. Building an intelligent agent that simulates human-level learning using machine learning techniques A Computational Model of Accelerated Future Learning through Feature Recognition Nan Li Computer Science Department Carnegie Mellon University

  2. Accelerated Future Learning • Accelerated Future Learning • Learning more effectively because of prior learning • Has been observed a lot • How? • Expert vs Novice • Expert  Deep functional feature (e.g. -3x  -3) • Novice  Shallow perceptual feature (e.g. -3x  3)

  3. A Computational Model • Model Accelerated Future Learning • Use Machine Learning Techniques • Acquire Deep Feature • Integrated into a Machine-Learning Agent

  4. An Example in Algebra

  5. Feature Recognition asPCFG Induction • Under lying structure in the problem  Grammar • Feature  Intermediate symbol in a grammar rule • Feature learning task  Grammar induction • Error  Incorrect parsing

  6. Problem Statement • Input is a set of feature recognition records consisting of • An original problem (e.g. -3x) • The feature to be recognized (e.g. -3 in -3x) • Output • A PCFG • An intermediate symbol in a grammar rule

  7. Accelerated Future Learning through Feature Recognition • Extended a PCFG Learning Algorithm (Li et al., 2009) • Feature Learning • Stronger Prior Knowledge: • Transfer Learning Using Prior Knowledge • Better Learning Strategy: • Effective Learning Using Bracketing Constraint

  8. A Two-Step Algorithm • Greedy Structure Hypothesizer: • Hypothesizes the schema structure • Viterbi Training Phase: • Refines schema probabilities • Removes redundant schemas Generalizes Inside-Outside Algorithm (Lary & Young, 1990)

  9. Greedy Structure Hypothesizer • Structure learning • Bottom-up • Prefer recursive to non-recursive

  10. EM Phase • Step One: • Plan parse tree computation • Most probable parse tree • Step Two: • Selection probabilities update • s: aip, ajak

  11. Feature Learning • Build Most Probable Parse Trees • For all observation sequences • Select an Intermediate Symbol that • Matches the most training records as the target feature

  12. Transfer Learning Using Prior Knowledge • GSH Phase: • Build parse trees based on previously acquired grammar • Then call the original GSH • Viterbi Training: • Add rule frequency in previous task to the current task 0.5 0.33 0.5 0.66

  13. Effective Learning Using Bracketing Constraint • Force to generate a feature symbol • Learn a subgrammar for feature • Learn a grammar for whole trace • Combine two grammars

  14. Experiment Design in Algebra

  15. Experiment Result in Algebra • Both stronger prior knowledge and a better learning strategy can yield accelerated future learning • Strong prior knowledge produces faster learning outcomes • L00 generated human-like errors Fig.3. Curriculum two Fig.2. Curriculum one Fig.4. Curriculum three

  16. Learning Speed inSynthetic Domains • Both stronger prior knowledge and a better learning strategy yield faster learning • Strong prior knowledge produces faster learning outcomes with small amount of training data, but not with large amount of data • Learning with subtask transfer shows larger difference, 1) training process; 2) low level symbols

  17. Score with Increasing Domain Sizes • The base learner, L00, shows the fastest drop • Average time spent per training record • Less than 1 millisecond except for L10 (266 milliseconds) • L10: Need to maintain previous knowledge, does not separate trace into small traces • Conciseness: Transfer learning doubled the size of the schema.

  18. Integrating Accelerated Future Learning in SimStudent • A machine-learning agent that • Acquires production rules from • Examples and problem solving experience • Integrate the acquired grammar into production rules • Requires weak operators (non-domain specific knowledge) • Less number of operators x+5

  19. Concluding Remarks • Presented a computational model of human learning that yields accelerated future learning. • Showed • Both stronger prior knowledge and a better learning strategy improve learning efficiency. • Stronger prior knowledge produced faster learning outcomes than a better learning strategy. • Some model generated human-like errors, while others did no make any mistake.

  20. Thank you! 

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