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Machine Learning Methods for Parameter Acquisition in a Human ...

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Machine Learning Methods for Parameter Acquisition in a Human ...

  1. 1. Machine Learning Approaches to Cognitive Parameter Acquisition Terran Lane University of New Mexico [email_address] Chris Forsythe, Patrick Xavier Sandia National Labs {jcforsy,pgxavie}@sandia.gov
  2. 2. Sandia’s Cognitive Modeling Framework <ul><li>Computational models of human decision-makers </li></ul><ul><li>Models attention, perceptual cues, situational awareness, decision making </li></ul><ul><li>Based on oscillatory models of activation </li></ul><ul><li>Spreading activation networks and feedback loops between functional elements </li></ul><ul><li>Applications -- data analysis, security, tutoring… </li></ul><ul><li>Bottleneck : models hand-built/tuned </li></ul><ul><ul><li>Expensive and slow! </li></ul></ul>
  3. 3. The Big Picture World Cue 0  0 Cue 1  1 Cue N  N Situation 0 Situation 1 Situation M Actions/ Decisions  01  10  N1  NM 
  4. 4. Automated Model Acquisition <ul><li>High predictive accuracy </li></ul><ul><ul><li>87% correct prediction of operator’s interpretation of scenario (incl. relevance) </li></ul></ul><ul><ul><li>91% correct in recognizing situation only </li></ul></ul><ul><li>Insights into operator decision-making process </li></ul><ul><li>Models are task & user specific </li></ul><ul><ul><li>Only 26% overlap between users </li></ul></ul><ul><ul><li>Large effort in building and tuning models </li></ul></ul><ul><li>Project goal: (semi-)automate acquisition of parameters, network topologies, etc. </li></ul><ul><li>Prediction accuracy secondary concern </li></ul>
  5. 5. Roles for Machine Learning <ul><li>Parameter acquisition </li></ul><ul><ul><li>Interconnection weights </li></ul></ul><ul><ul><li>Activation levels </li></ul></ul><ul><ul><li>Oscillator frequencies </li></ul></ul><ul><li>Network topologies </li></ul><ul><ul><li>Inter-cue spreading activation network </li></ul></ul><ul><ul><li>Cue <-> situation relations </li></ul></ul><ul><ul><li>Feedbacks </li></ul></ul><ul><li>Cues and situation identification </li></ul>
  6. 6. Parameter Acquisition World Cue 0  0 Cue 1  1 Cue N  N Situation 0 Situation 1 Situation M Actions/ Decisions  01  10  N1  NM 
  7. 7. Parameter Acquisition: Issues <ul><li>Superficially supervised learning </li></ul><ul><ul><li>Observe features/cues and operator actions; induce params (find  s.t. f  :C  A) </li></ul></ul><ul><ul><li>Similar to ANN backprop, EM, etc. </li></ul></ul><ul><ul><li>Many effective, well understood techniques </li></ul></ul><ul><li>Problem: not just high-likelihood params </li></ul><ul><li>Actually want params used by human operator </li></ul><ul><li>Much harder – observable stimuli don’t directly reflect operator’s internal state </li></ul><ul><li>Cognitive plausibility constraint </li></ul>
  8. 8. Parameter Acquisition: Approaches <ul><li>Additional instrumentation </li></ul><ul><ul><li>Measure characteristics of operator </li></ul></ul><ul><ul><li>Biometrics – eye tracking, MEG, etc. </li></ul></ul><ul><ul><li>Expensive, not widespread </li></ul></ul><ul><ul><li>Maybe not informative to params anyway </li></ul></ul><ul><li>Utility elicitation techniques </li></ul><ul><ul><li>Software queries user about why decisions were made / state of attention </li></ul></ul><ul><ul><li>Picks questions to maximally improve model </li></ul></ul><ul><ul><li>Emulates expert knowledge engineer </li></ul></ul>
  9. 9. Network Topology Induction World Cue 0  0 Cue 1  1 Cue N  N Situation 0 Situation 1 Situation M Actions/ Decisions  01  10  N1  NM 
  10. 10. Topology Induction: Issues <ul><li>Find structure of interconnections between variables (I.e., cues, situations) </li></ul><ul><li>Much harder than parameter acquisition </li></ul><ul><li>Formally, maximum likelihood/MAP search through all possible networks </li></ul>
  11. 11. Topology Induction: Issues <ul><li>Find structure of interconnections between variables (I.e., cues, situations) </li></ul><ul><li>Much harder than parameter acquisition </li></ul><ul><li>Formally, maximum likelihood/MAP search through all possible networks </li></ul>L=137
  12. 12. Topology Induction: Issues <ul><li>Find structure of interconnections between variables (I.e., cues, situations) </li></ul><ul><li>Much harder than parameter acquisition </li></ul><ul><li>Formally, maximum likelihood/MAP search through all possible networks </li></ul>L=137 L=238
  13. 13. Topology Induction: Issues <ul><li>Find structure of interconnections between variables (I.e., cues, situations) </li></ul><ul><li>Much harder than parameter acquisition </li></ul><ul><li>Formally, maximum likelihood/MAP search through all possible networks </li></ul>L=137 L=238 L=493
  14. 14. Topology Induction: Issues <ul><li>Find structure of interconnections between variables (I.e., cues, situations) </li></ul><ul><li>Much harder than parameter acquisition </li></ul><ul><li>Formally, maximum likelihood/MAP search through all possible networks </li></ul>L=137 L=238 L=493 L=318
  15. 15. Topology Induction: Issues <ul><li>Find structure of interconnections between variables (I.e., cues, situations) </li></ul><ul><li>Much harder than parameter acquisition </li></ul><ul><li>Formally, maximum likelihood/MAP search through all possible networks </li></ul>L=137 L=238 L=493 L=318
  16. 16. Topology Induction: Approaches <ul><li>Principles of structure search well understood </li></ul><ul><li>Gradient ascent, annealing, genetic search, constrained search, etc. </li></ul><ul><li>Difficult in practice </li></ul><ul><ul><li>Computationally intractable </li></ul></ul><ul><ul><li>Resulting models very sensitive to data </li></ul></ul><ul><ul><li>Spurious likelihood spikes  low confidence models </li></ul></ul><ul><li>Compounded by cognitive plausibility constraint </li></ul><ul><li>Can get leverage from cognitive plausibility, though </li></ul>
  17. 17. Cue and Situation Identification World Cue 0  0 Cue 1  1 Cue N  N Situation 0 Situation 1 Situation M Actions/ Decisions  01  10  N1  NM 
  18. 18. Cue and Situation Identification: Issues <ul><li>Discern cues and whole environmental situations employed by user </li></ul><ul><li>Related to constructive feature induction, nonlinear projection identification, relational learning, etc. </li></ul><ul><li>Search across all possible nodes/relations </li></ul>N=2 N=3
  19. 19. Cue and Situations: Approaches <ul><li>Cutting-edge ML problem </li></ul><ul><li>Direct elicitation is probably most promising approach </li></ul><ul><li>Formulating search space/uncertainty reduction not straightforward </li></ul><ul><li>Even user interface is difficult (naming synthetic nodes/relations) </li></ul>
  20. 20. Conclusions <ul><li>Decrease time/effort/cost to construct and tune cognitive model </li></ul><ul><li>Constrained to correspond to human’s internal model </li></ul><ul><ul><li>Both bane and boon to automated model construction </li></ul></ul><ul><ul><li>Insights into operator’s mental state/decision-making process </li></ul></ul><ul><li>Requires/drives novel ML algorithms </li></ul><ul><li>Future work: all of it… </li></ul>
  21. 21. Questions?

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