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Hierarchical Representation Learning for Markov Decision Processes
[article]
2021
arXiv
pre-print
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In this paper we present a novel method for learning hierarchical representations of Markov decision processes. ...
We empirically validate the method, by showing that it can successfully learn a useful hierarchical representation in a navigation domain. ...
Background In this section we define Markov decision processes and hierarchical reinforcement learning in the form of the options framework. ...
arXiv:2106.01655v2
fatcat:bzje2xuahreg3f255rk2zgaagu
Detecting and Responding to Concept Drift in Business Processes
2022
Algorithms
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In this paper, we model a response to concept drift as a sequential decision making problem by combing a hierarchical Markov model and a Markov decision process (MDP). ...
Concept drift, which refers to changes in the underlying process structure or customer behaviour over time, is inevitable in business processes, causing challenges in ensuring that the learned model is ...
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. ...
doi:10.3390/a15050174
fatcat:3n4gnl33dfeatok6r6ofvzg42a
Object-Oriented Representation and Hierarchical Reinforcement Learning in Infinite Mario
2012
2012 IEEE 24th International Conference on Tools with Artificial Intelligence
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We then use this representation combined with the hierarchical reinforcement learning model as a learning framework. ...
With the help of experimental results, we show that this approach facilitates faster and efficient learning for this domain. (Abstract) ...
Semi-Markov Decision Process The semi-Markov decision process (SMDP) is a generalization of the MDP which incorporates temporally extended actions. ...
doi:10.1109/ictai.2012.152
dblp:conf/ictai/JoshiKSDM12
fatcat:dkj4fj6jonb57oxcsvtkwb67qy
Higher-Order Logic
[chapter]
2017
Encyclopedia of Machine Learning and Data Mining
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Starting from the concept of regular Markov models we introduce the concept of hidden Markov model, and the issue of estimating the output emission and transition probabilities between hidden states, for ...
We mention typical application in which hidden Markov models play a central role, and mention a number of popular implementations. ...
Semi-Markov Decision Problem Formalism The common underlying formalism in hierarchical reinforcement learning is the semi-Markov decision process (SMDP). ...
doi:10.1007/978-1-4899-7687-1_126
fatcat:oya3su4vhjh3lk77kbsw2qe2y4
A Hierarchical Approach For The Design Of Gesture-To-Sound Mappings
2012
Proceedings of the SMC Conferences
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We thank all members of the Real Time Musical Interactions team and members of the Legos project for fruitful discussions. ...
Similarly, Jordà [19, 20] argued for the need of considering different control levels allowing for either intuitive or compositional decisions. ...
GESTURE SEGMENTATION USING HIERARCHICAL MARKOV MODELS We explain in this section how Hierarchical Hidden Markov Models can be used in our framework. ...
doi:10.5281/zenodo.850029
fatcat:vwwtrmarpremvhuozfssd42qb4
Learning agents for uncertain environments (extended abstract)
1998
Proceedings of the eleventh annual conference on Computational learning theory - COLT' 98
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This seems to be a very interesting problem for the COLT, UAI, and ML communities, and has been addressed in econometrics under the heading of structural estimation of Markov decision processes. ...
I will discuss methods for learning the parameters and structure of such representations from sensory inputs, and for computing posterior probabilities. ...
Reinforcement learning (RL) methods are essentially online algorithmd for solving Markov decision processes (MDPs). ...
doi:10.1145/279943.279964
dblp:conf/colt/Russell98
fatcat:bnv35r7dzfdzfdhch3qea6amdy
:{unav)
2012
Discrete event dynamic systems
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Common to these approaches is a reliance on the theory of semi-Markov decision processes, which we emphasize in our review. ...
We then discuss extensions of these ideas to concurrent activities, multiagent coordination, and hierarchical memory for addressing partial observability. ...
We follow Sutton and Barto (1998) in denoting the reward for the action at stage t by r t 1 instead of the more usual r t . 2. ...
doi:10.1023/a:1025696116075
fatcat:l7go63ebc5abrf2je75k5h6fau
Structured Prediction of Sequences and Trees Using Infinite Contexts
[chapter]
2015
Lecture Notes in Computer Science
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To facilitate learning of this large and unbounded model, we use a hierarchical Pitman-Yor process prior which provides a recursive form of smoothing. ...
We propose a novel hierarchical model for structured prediction over sequences and trees which exploits global context by conditioning each generation decision on an unbounded context of prior decisions ...
To facilitate learning of such a large and unbounded model, the predictive distributions associated with tree contexts are smoothed in a recursive manner using a hierarchical Pitman-Yor process. ...
doi:10.1007/978-3-319-23525-7_23
fatcat:kpevaszn65a3xnsbyblskvvlk4
Structured Prediction of Sequences and Trees using Infinite Contexts
[article]
2015
arXiv
pre-print
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To facilitate learning of this large and unbounded model, we use a hierarchical Pitman-Yor process prior which provides a recursive form of smoothing. ...
We propose a novel hierarchical model for structured prediction over sequences and trees which exploits global context by conditioning each generation decision on an unbounded context of prior decisions ...
To facilitate learning of such a large and unbounded model, the predictive distributions associated with tree contexts are smoothed in a recursive manner using a hierarchical Pitman-Yor process. ...
arXiv:1503.02417v1
fatcat:7txfrppwkvc5tnutoteisub6ue
Dimensions of complexity of intelligent agents
2006
Proceedings of the 2006 international symposium on Practical cognitive agents and robots - PCAR '06
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Research has progressed by making simplifying assumptions about the representations of the agents or about the environments the agents act in. ...
For each of these dimensions, there is a simplified case and progressively more complex cases. ...
observable Markov decision processes GT Game theory representations of strategic and extensive form of a game. ...
doi:10.1145/1232425.1232438
fatcat:rda4bue57ndy3frw2trmbu5fjq
Assisted living technologies for older adults
2012
Proceedings of the 2nd ACM SIGHIT symposium on International health informatics - IHI '12
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Decision process (MDP) Opportunity Knocks (OK): public transit assistance [Patterson 2004] iRoute: Learns walking preference of dementia patients [Hossain 2011] Commercial b
■ h d ■ o p ■ ■ ...
Task
# Robots
Social Communication
46
Hobbies
29
New Learning
16
Data from Understanding the potential for robot assistance for older adults in the home environment (HFA-TR-1102). ...
doi:10.1145/2110363.2110478
dblp:conf/ihi/Rashidi12
fatcat:vavobpvbqzfslm4343duxh7yfe
Representational efficiency outweighs action efficiency in human program induction
[article]
2018
arXiv
pre-print
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We introduce a novel problem-solving paradigm that links problem solving and program induction under the Markov Decision Process (MDP) framework. ...
The importance of hierarchically structured representations for tractable planning has long been acknowledged. ...
Background The theory of Markov decision processes (MDPs; Sutton & Barto, 1998 ) provides a computational framework for modeling problem solving as search within a metaphorical problem or state space. ...
arXiv:1807.07134v1
fatcat:mrubjdh5gveubbj4cblx25c5tm
A modularized framework for explaining hierarchical attention networks on text classifiers
2021
Proceedings of the Canadian Conference on Artificial Intelligence
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In this paper, we propose FEHAN, a modularized Framework for Explaining HiErarchical Attention Network trained to classify text data. ...
The last decade has witnessed the rise of a black box society where classification models that hide the logic of their internal decision processes are widely adopted due to their high accuracy. ...
Acknowledgements The authors would like to thank NSERC (Natural Sciences and Engineering Research Council of Canada) for financial support. ...
doi:10.21428/594757db.23db72bf
fatcat:w2vtraapmzc43m53x755572ehi
Subgoal Identifications in Reinforcement Learning: A Survey
[chapter]
2011
Advances in Reinforcement Learning
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In sequential decision making, a common approach is to model the entire task with Markov decision processes (MDPs). ...
The semi-Markov decision process (SMDP) is a framework that extend MDPs to consider temporal-effect. ...
doi:10.5772/13214
fatcat:wxraqw43xng6nnxe5qu5diukz4
Modeling Physicians' Utterances to Explore Diagnostic Decision-making
2017
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
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We propose a hierarchical probabilistic framework to learn domain-specific patterns from the medical concepts in these narratives. ...
These meaningful patterns uncover physicians' diagnostic decision-making processes while parsing the image content. ...
We would like to thank the participating physicians, the reviewers, and Logical Images, Inc. for images. ...
doi:10.24963/ijcai.2017/517
dblp:conf/ijcai/GuoLYH17
fatcat:cmdvnfrfbvds5ldy4asimdzq44
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