Extended Markov Tracking with Ensemble Actions
Extended Markov Tracking with Ensemble Actions
In this paper we extend the control methodology based on Extended Markov Tracking (EMT) by providing the control algorithm with capabilities to calibrate and even partially reconstruct the environment's model. This enables us to resolve the problem of performance deterioration due to model incoherence, a negative problem in all model based control methods. The new algorithm, Ensemble Actions EMT (EA-EMT), utilises the initial environment model as a library of state transition functions and applies a variation of prediction with experts to assemble and calibrate a revised model. By so doing, this is the first control algorithm that enables on-line adaptation within the Dynamics Based Control (DBC) framework. In our experiments, we performed a range of tests with increasing model incoherence induced by three types of exogenous environment perturbations: catastrophic, periodic and deviating. The results show that EA-EMT resolved model incoherence and significantly outperformed the best currently available DBC solution by up to $95\%$.
Rabinovich, Zinovi
573422bf-523d-466b-a047-7a92917102e7
Jennings, Nick
ab3d94cc-247c-4545-9d1e-65873d6cdb30
13 July 2009
Rabinovich, Zinovi
573422bf-523d-466b-a047-7a92917102e7
Jennings, Nick
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Rabinovich, Zinovi and Jennings, Nick
(2009)
Extended Markov Tracking with Ensemble Actions.
International Workshop on Hybrid Control of Autonomous Systems.
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Conference or Workshop Item
(Paper)
Abstract
In this paper we extend the control methodology based on Extended Markov Tracking (EMT) by providing the control algorithm with capabilities to calibrate and even partially reconstruct the environment's model. This enables us to resolve the problem of performance deterioration due to model incoherence, a negative problem in all model based control methods. The new algorithm, Ensemble Actions EMT (EA-EMT), utilises the initial environment model as a library of state transition functions and applies a variation of prediction with experts to assemble and calibrate a revised model. By so doing, this is the first control algorithm that enables on-line adaptation within the Dynamics Based Control (DBC) framework. In our experiments, we performed a range of tests with increasing model incoherence induced by three types of exogenous environment perturbations: catastrophic, periodic and deviating. The results show that EA-EMT resolved model incoherence and significantly outperformed the best currently available DBC solution by up to $95\%$.
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Published date: 13 July 2009
Venue - Dates:
International Workshop on Hybrid Control of Autonomous Systems, 2009-07-13
Organisations:
Agents, Interactions & Complexity
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Local EPrints ID: 267337
URI: http://eprints.soton.ac.uk/id/eprint/267337
PURE UUID: 869d81f5-adca-4961-adcd-725641df75f9
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Date deposited: 07 May 2009 11:55
Last modified: 14 Mar 2024 08:48
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Contributors
Author:
Zinovi Rabinovich
Author:
Nick Jennings
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