Inference of Strategic Behavior based on Incomplete Observation Data

Antti Kangasrääsiö; Samuel Kaski

Inference of Strategic Behavior based on Incomplete Observation Data

Antti Kangasrääsiö, Samuel Kaski

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference contribution › Professional

100 Lataukset (Pure)

Abstrakti

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.

Alkuperäiskieli	Englanti
Otsikko	NIPS17 Workshop: Learning in the Presence of Strategic Behavior
Kustantaja	Carnegie Mellon University
Sivumäärä	4
Tila	Julkaistu - 8 jouluk. 2017
OKM-julkaisutyyppi	D3 Ammatillisen konferenssin julkaisusarja
Tapahtuma	ANNUAL CONFERENCE ON NEURAL INFORMATION PROCESSING SYSTEMS - Long Beach, Yhdysvallat Kesto: 4 jouluk. 2017 → 9 jouluk. 2017 Konferenssinumero: 31

Conference

Conference	ANNUAL CONFERENCE ON NEURAL INFORMATION PROCESSING SYSTEMS
Lyhennettä	NIPS
Maa/Alue	Yhdysvallat
Kaupunki	Long Beach
Ajanjakso	04/12/2017 → 09/12/2017

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PreprintAccepted author manuscript, 382 KB

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Interaktiivinen koneoppiminen useista biodatalähteistä
Sundin, I., Kaski, S., Afrabandpey, H., Chen, Y., Aushev, A., Honkamaa, J., Blomstedt, P., Hegde, P., Siren, J., Pesonen, H., Kangas, J., Qin, X., Shen, Z., Peltola, T., Celikok, M. M., Daee, P., Eranti, P., Jälkö, J. & Reinvall, J.
01/01/2016 → 31/12/2018
Projekti: Academy of Finland: Other research funding
Interaktiivinen koneoppiminen useista biodatalähteistä
Kaski, S. & Filstroff, L.
01/01/2016 → 31/08/2021
Projekti: Academy of Finland: Other research funding

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@inproceedings{453d68b453fd4f53800dbffaa4d4f994,

title = "Inference of Strategic Behavior based on Incomplete Observation Data",

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.",

keywords = "Inverse reinforcement learning, Bayesian Inference, Approximate Bayesian computation, Monte Carlo simulation",

author = "Antti Kangasr{\"a}{\"a}si{\"o} and Samuel Kaski",

year = "2017",

month = dec,

day = "8",

language = "English",

booktitle = "NIPS17 Workshop: Learning in the Presence of Strategic Behavior",

publisher = "Carnegie Mellon University",

address = "United States",

note = "IEEE Conference on Neural Information Processing Systems, NIPS ; Conference date: 04-12-2017 Through 09-12-2017",

}

TY - GEN

T1 - Inference of Strategic Behavior based on Incomplete Observation Data

AU - Kangasrääsiö, Antti

AU - Kaski, Samuel

N1 - Conference code: 31

PY - 2017/12/8

Y1 - 2017/12/8

N2 - 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.

AB - 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.

KW - Inverse reinforcement learning

KW - Bayesian Inference

KW - Approximate Bayesian computation

KW - Monte Carlo simulation

UR - https://www.cs.cmu.edu/~nhaghtal/mlstrat/

M3 - Conference contribution

BT - NIPS17 Workshop: Learning in the Presence of Strategic Behavior

PB - Carnegie Mellon University

T2 - IEEE Conference on Neural Information Processing Systems

Y2 - 4 December 2017 through 9 December 2017

ER -

Inference of Strategic Behavior based on Incomplete Observation Data

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Conference

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Interaktiivinen koneoppiminen useista biodatalähteistä

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