Inference of Strategic Behavior based on Incomplete Observation Data

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Volume Title
Conference article in proceedings
Date
2017-12-08
Major/Subject
Mcode
Degree programme
Language
en
Pages
4
Series
NIPS17 Workshop: Learning in the Presence of Strategic Behavior
Abstract
Inferring the goals, preferences and restrictions of strategically behaving agents is a common goal in many situations, and an important requirement for enabling computer systems to better model and understand human users. Inverse reinforcement learning (IRL) is one method for performing this kind of inference based on observations of the agent's behavior. However, traditional IRL methods are only applicable when the observations are in the form of state-action paths -- an assumption which does not hold in many real-world modelling settings. This paper demonstrates that inference is possible even with an arbitrary observation noise model.
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Keywords
Inverse reinforcement learning, Bayesian Inference, Approximate Bayesian computation, Monte Carlo simulation
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Citation
Kangasrääsiö , A & Kaski , S 2017 , Inference of Strategic Behavior based on Incomplete Observation Data . in NIPS17 Workshop: Learning in the Presence of Strategic Behavior . Carnegie Mellon University , IEEE Conference on Neural Information Processing Systems , Long Beach , California , United States , 04/12/2017 .