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Jaedeug Choi
by Jaedeug Choi published Apr 09, 2013 last modified May 08, 2013 01:10 오전
Located in People
Inproceedings Reference Octet Stream Bayesian Nonparametric Feature Construction for Inverse Reinforcement Learning
by Jaedeug Choi published Apr 09, 2013 last modified May 28, 2013 11:44 오후
  Most of the algorithms for inverse reinforcement learning (IRL) assume that the reward function is a linear function of the pre-defined state and action features. However, it is often difficult to manually specify the set of features that can make the true reward function representable as a linear function. We propose a Bayesian nonparametric approach to identifying useful composite features for learning the reward function. The composite features are assumed to be the logical conjunctions of the predefined atomic features so that we can represent the reward function as a linear function of the composite features. We empirically show that our approach is able to learn composite features that capture important aspects of the reward function on synthetic domains, and predict taxi drivers’ behaviour with high accuracy on a real GPS trace dataset.
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Jaedeug Choi (최재득)
by Jaedeug Choi published Apr 09, 2013 last modified Feb 12, 2014 10:34 오후
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PDF File PDF document Bayesian Nonparametric Feature Construction for Inverse Reinforcement Learning
by Jaedeug Choi published May 08, 2013 last modified Feb 13, 2015 04:29 오전
PDF of Choi, J and Kim, K (2013): In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI). Located at portal path: http://localhost:8080/aipr/papers/CK2013a
Located in Papers / PDFs
Inproceedings Reference Engineering Statistical Dialog State Trackers: A Case Study on DSTC
by Jaedeug Choi published Aug 20, 2013
We describe our experience with engineering the dialog state tracker for the first Dialog State Tracking Challenge (DSTC). Dialog trackers are one of the essential components of dialog systems which are used to infer the true user goal from the speech processing results. We explain the main parts of our tracker: the observation model, the belief refinement model, and the belief transformation model. We also report experimental results on a number of approaches to the models, and compare the overall performance of our tracker to other submitted trackers. An extended version of this paper is available as a technical report (Kim et al., 2013).
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PDF File PDF document Engineering Statistical Dialog State Trackers: A Case Study on DSTC
by Jaedeug Choi published Aug 27, 2013 last modified Feb 13, 2015 04:29 오전
PDF of Kim, D, Choi, J, Kim, K, Lee, J, and Sohn, J (2013): In: Proceedings of the SIGDIAL 2013 Conference, pp. 462-466. Located at portal path: http://localhost:8080/aipr/papers/KCKLS2013
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Techreport Reference Octet Stream Engineering Statistical Dialog State Trackers: A Case Study on DSTC
by Jaedeug Choi published Aug 20, 2013
We describe our experience with engineering the dialog state tracker for the first Dialog State Tracking Challenge (DSTC). Dialog trackers are one of the essential components of dialog systems which are used to infer the true user goal from the speech processing results. We explain the main parts of our tracker: the observation model, the belief refinement model, and the belief transformation model. We also report experimental results on a number of approaches to the models, and compare the overall performance of our tracker to other submitted trackers. This technical report is a companion to the shortened version presented at SIGDIAL 2013.
Located in Papers
PDF File PDF document Engineering Statistical Dialog State Trackers: A Case Study on DSTC
by Jaedeug Choi published Aug 27, 2013 last modified Feb 13, 2015 04:29 오전
PDF of Kim, D, Choi, J, Kim, K, Lee, J, and Sohn, J (2013): Department of Computer Science, KAIST, Technical Report(CS-TR-2013-379). Located at portal path: http://localhost:8080/aipr/papers/KCKLS2013a
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File PRMLSS 2013 - IRL
by Jaedeug Choi last modified Aug 26, 2013 02:08 오후
Tutorial on IRL: Nonparametric Bayesian Inverse Reinforcement Learning
Located in People / Jaedeug Choi
Inverse reinforcement learning in partially observable environments
by Jaedeug Choi published Aug 27, 2013 last modified Sep 18, 2019 07:06 오후
Java implementation of IRL for POMDP [ChoiKim.09; ChoiKim.11].
Located in Codes & Demos