Learning HMM-based cognitive load models for supporting

human-agent teamwork

Xiaocong Fan, Po-Chun Chen, John Yen

소프트컴퓨팅연구실황주원

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

2

Introduction

• Goal– How shared cognitive structures can enhance human-agent

team performance– To develop a computational cognitive capacity model to fa-

cilitate the establishment of shared mental models

• Human-centered teamwork– Establishing situation awareness– Developing shared mental models

3

Introduction

• Human and autonomous agents– Human are limited by their cognitive capacity in informa-

tion 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

4

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

5

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HMM-based load modelsA 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

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HMM-based load modelsComputational 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

• Load state based– Resource-bounded agents -> a realistic information processing strategy

• Schema theory– Multiple elements of information can be chunked as sin-

gle elements in cognitive schemas.

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HMM-based load modelsAgent 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.

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HMM-based load modelsHAP’s processing load model

Cognitive task design and data collec-tion

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• 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

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Learning cognitive load modelsLearning 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.

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Learning cognitive load modelsLearning procedure

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Learning cognitive load modelsLearning procedure

• 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 in-creased.

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Learning cognitive load modelsThe model space of cognitive load

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Learning cognitive load modelsProperties of ‘Good’ cognitive load models• ‘Good’ models -> Top-layer

An example 5-state HMM Transition probability distributions

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Learning cognitive load modelsProperties of ‘Good’ cognitive load models

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Learning cognitive load modelsThe number of hidden states

• How many hidden states are appropriate for modeling cognitive load using HMMS?

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Learning cognitive load modelsThe number of hidden states

. Blue : human’s instantaneous cognitive loads

. Red : processing loads of a HAP as a whole