소프트컴퓨팅연구실 황주원

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

2. 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

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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.

9. 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.

10. 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

11. 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.

12. Learning cognitive load models • Learning procedure

13. Learning cognitive load models • Learning procedure

14. 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.

15. Learning cognitive load models • Properties of ‘Good’ cognitive load models • ‘Good’ models -> Top-layer An example 5-state HMM Transitionprobability distributions

16. Learning cognitive load models • Properties of ‘Good’ cognitive load models

17. Learning cognitive load models • The number of hidden states • How many hidden states are appropriate for modeling cognitive load using HMMS?

18. Learning cognitive load models • The number of hidden states . Blue : human’s instantaneous cognitive loads . Red : processing loads of a HAP as a whole