Learning Models of Sequential Decision-Making without Complete State Specification using Bayesian Nonparametric Inference and Active Querying
Name
MIT-CSAIL-TR-2018-015.pdf
Size
238.68 KB
Format
Adobe PDF
Checksum (MD5)
99b8775516383773bf49436b2e00c00a
Download all files submitted through automated deposit
Author(s)
Unhelkar, Vaibhav V.
Shah, Julie A.
Advisor(s)
Julie A Shah
Date Issued
May 17, 2018
Series/Report no.
MIT-CSAIL-TR-2018-015
Abstract
Learning models of decision-making behavior during sequential tasks is useful across a variety of applications, including human-machine interaction. In this paper, we present an approach to learning such models within Markovian domains based on observing and querying a decision-making agent. In contrast to classical approaches to behavior learning, we do not assume complete knowledge of the state features that impact an agent's decisions. Using tools from Bayesian nonparametric inference and time series of agents decisions, we first provide an inference algorithm to identify the presence of any unmodeled state features that impact decision making, as well as likely candidate models. In order to identify the best model among these candidates, we next provide an active querying approach that resolves model ambiguity by querying the decision maker. Results from our evaluations demonstrate that, using the proposed algorithms, an observer can identify the presence of latent state features, recover their dynamics, and estimate their impact on decisions during sequential tasks.
Subjects
Decision Making, Graphical Models, Human-AI Collaboration
Persistent DSpace Link
