Modelling Human Decision-making based on Aggregate Observation Data
Loading...
Access rights
openAccess
acceptedVersion
URL
Journal Title
Journal ISSN
Volume Title
A4 Artikkeli konferenssijulkaisussa
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
Authors
Date
2017
Department
Major/Subject
Mcode
Degree programme
Language
en
Pages
4
Series
Human In The Loop-ML Workshop at ICML
Abstract
Being able to infer the goals, preferences and limitations of humans is of key importance in designing interactive systems. Reinforcement learning (RL) models are a promising direction of research, as they are able to model how the behavioural patterns of users emerge from the task and environment structure. One limitation with traditional inference methods for RL models is the strict requirements for observation data; both the states of the environment and the actions of the agent need to be observed at each step of the task. This has prevented RL models from being used in situations where such fine-grained observations are not available. In this extended abstract we present results from a recent study where we demonstrated how inference can be performed for RL models even when the observation data is significantly more coarse-grained. The idea is to solve the inverse reinforcement learning (IRL) problem using approximate Bayesian computation sped up with Bayesian optimization.Description
Keywords
Other note
Citation
Kangasrääsiö, A & Kaski, S 2017, Modelling Human Decision-making based on Aggregate Observation Data . in Human In The Loop-ML Workshop at ICML . Human in the Loop Machine Learning, Sydney, Human in the Loop Machine Learning; ICML Workshop, Sydney, Australia, 11/08/2017 .