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Learning HMM-based cognitive load models for supporting human-agent teamwork Xiaocong Fan, Po-Chun Chen, John Yen. 소프트컴퓨팅연구실 황주원. Overview . Introduction HMM-based load models - A human-centered teamwork model - Computational cognitive capacity model - Agent processing load model

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Learning HMM-based cognitive load models for supporting human-agent teamworkXiaocong Fan, Po-Chun Chen, John Yen

소프트컴퓨팅연구실

황주원

overview
Overview
  • Introduction
  • HMM-based load models

- A human-centered teamwork model

- Computational cognitive capacity model

- Agent processing load model

- HAP’s processing load model

  • Cognitive task design and data collection
  • Learning cognitive load models

- Learning procedure

- The model space of cognitive load

- Properties of ‘Good’ cognitive load models

- The number of hidden states

introduction
Introduction
  • Goal
    • How shared cognitive structures can enhance human-agent team performance
    • To develop a computational cognitive capacity model to facilitate the establishment of shared mental models
  • Human-centered teamwork
    • Establishing situation awareness
    • Developing shared mental models
introduction1
Introduction
  • Human and autonomous agents
    • Human are limited by their cognitive capacity in information processing
    • Autonomous agents can learn expertise problem-solving knowledge
  • Shared mental model
    • To predict others’ needs and coordinate behaviors
    • The establishment of shared mental models among human and agent team members
    • Concept of shared mental models include
      • Role assignment and its dynamics
      • Teamwork schemas and progresses
      • Communication patterns and intentions
hmm based load models
HMM-based load models
  • HMM-based load models
    • A human-centered teamwork model
    • Computational cognitive capacity model
    • Agent processing load model
    • HAP’s processing load model
hmm based load models1
HMM-based load models
  • A human-centered teamwork model
  • Human partner model
    • Human’s cognitive states (goals, intentions, trust)
  • Processing Model & Communication Model
    • Dynamically updates models of other HAPs
  • Assumption
    • An agent do not knows all the information/intentions
    • Agent’s processing capacity is limited by computing resources
hmm based load models2
HMM-based load models
  • Computational cognitive capacity model
  • Hidden Markov model
    • A statistical approach to modeling systems

that can be viewed as a Markov

process with unknown hidden parameters

  • In this study
    • Cognitive load has a dynamic nature
    • HMM approach demands

that the system being modeled

(human’s cognitive capacity)

  • Secondary task performance
    • Observable signals to estimate the hidden

cognitive load state

  • Miller’s 7 ± 2 rule
    • Observable state range : 0~9

5-state HMM model

hmm based load models3
HMM-based load models
  • Agent processing load model
  • Load state based
    • Resource-bounded agents

-> a realistic information processing strategy

  • Schema theory
    • Multiple elements of information can be chunked as single elements in cognitive schemas.
hmm based load models4
HMM-based load models
  • HAP’s processing load model
  • The processing load of a HAP can thus be modeled as the co-effect of the processing load of the agent
  • HMMs for HAP processing load
  • The number of hypothetical hidden states is a critical parameter for modeling both human’s cognitive load and agent’s processing load.
cognitive task design and data collection
Cognitive task design and data collection
  • The goal of a team
    • To share information among members in a timely manner to develop global situation awareness
  • Shared belief map
    • A table with color-coded info-cells
    • Row : model of one team member
    • Column : information type
    • Concept : development of global situation
learning cognitive load models
Learning cognitive load models
  • Learning procedure
  • Subfigure
    • Top, middle, bottom components
    • 3 log-likelihood
      • log-likelihood in training
      • log-likelihood in testing
      • Standard deviation of log-likelihood in testing
    • Indicate
      • Maxima of each model space (from 3 to 10) form a 3-layer structure
      • Better trained models lead to better testing log-likelihood
      • Better trained models incur lower deviations.
learning cognitive load models3
Learning cognitive load models
  • The model space of cognitive load
  • First
    • Each model space (from 3 to 10) has a 3-layer structure,

which means the log-likelihood maxima are clustered in three levels

  • Second
    • Better trained models performed better in testing:

the trend of the log-likelihoods in fitting is consistent with

the trend of the log-likelihoods in training

  • Third
    • Better models produced lower deviation in testing.
    • Also, as the number of hidden states increased from 3 to 10,

the fraction of models at the middle and bottom levels reduced with the fraction of models at the top level increased.

learning cognitive load models4
Learning cognitive load models
  • Properties of ‘Good’ cognitive load models
  • ‘Good’ models -> Top-layer

An example 5-state HMM

Transitionprobability distributions

learning cognitive load models5
Learning cognitive load models
  • Properties of ‘Good’ cognitive load models
learning cognitive load models6
Learning cognitive load models
  • The number of hidden states
  • How many hidden states are appropriate for modeling cognitive load using HMMS?
learning cognitive load models7
Learning cognitive load models
  • The number of hidden states

. Blue : human’s instantaneous cognitive loads

. Red : processing loads of a HAP as a whole