An agent's activities are controlled by his priorities

Activity scheduling mechanism plays a critical role in the correct behaviour of BDI agents. For example, a robotic agent to serve at home should carry out the right activities at the right times. However the scheduling of deliberation about new beliefs and the scheduling of intention execution have...

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Main Authors: Huang, Shell Ying, Chang, Yuming, Zhang, Huiliang
Other Authors: School of Computer Engineering
Format: Conference Paper
Language:English
Published: 2011
Subjects:
Online Access:https://hdl.handle.net/10356/93821
http://hdl.handle.net/10220/7288
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author Huang, Shell Ying
Chang, Yuming
Zhang, Huiliang
author2 School of Computer Engineering
author_facet School of Computer Engineering
Huang, Shell Ying
Chang, Yuming
Zhang, Huiliang
author_sort Huang, Shell Ying
collection NTU
description Activity scheduling mechanism plays a critical role in the correct behaviour of BDI agents. For example, a robotic agent to serve at home should carry out the right activities at the right times. However the scheduling of deliberation about new beliefs and the scheduling of intention execution have not been carefully studied in most BDI systems. Usually if there is any differentiation of urgency among different tasks, a constant utility/priority value is used by a task selection fnction. We argue that priorities should be allowed to change with time and a linear function of time may not be the best for all tasks. In this paper, we propose to enrich the BDI framework with an extension which consists of 2 processing components, a PCF (Priority Changing Function) Selector and a Priority Controller. With this extension priorities of desires/intentions may have different initial values and may be changed with time according to the chosen PCFs. We propose a method of constructing PCFs which model the change of priorities in human behaviors when dealing with several things at the same time. We also propose a method to realize the change of the priorities of existing desires/intentions due to the generation of new beliefs/desires/intentions if necessary. We show by simulation experiments that Ramp function and especially the Sigmoid function can control the activities of an agent better than constant priorities with respect to getting tasks of various importance and urgency done with smaller Mean Earliness and smaller Mean Tardiness.
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spelling ntu-10356/938212020-05-28T07:17:16Z An agent's activities are controlled by his priorities Huang, Shell Ying Chang, Yuming Zhang, Huiliang School of Computer Engineering International conference on Agent and multi-agent systems: technologies and applications (2nd : 2008 : Incheon, Korea) DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Activity scheduling mechanism plays a critical role in the correct behaviour of BDI agents. For example, a robotic agent to serve at home should carry out the right activities at the right times. However the scheduling of deliberation about new beliefs and the scheduling of intention execution have not been carefully studied in most BDI systems. Usually if there is any differentiation of urgency among different tasks, a constant utility/priority value is used by a task selection fnction. We argue that priorities should be allowed to change with time and a linear function of time may not be the best for all tasks. In this paper, we propose to enrich the BDI framework with an extension which consists of 2 processing components, a PCF (Priority Changing Function) Selector and a Priority Controller. With this extension priorities of desires/intentions may have different initial values and may be changed with time according to the chosen PCFs. We propose a method of constructing PCFs which model the change of priorities in human behaviors when dealing with several things at the same time. We also propose a method to realize the change of the priorities of existing desires/intentions due to the generation of new beliefs/desires/intentions if necessary. We show by simulation experiments that Ramp function and especially the Sigmoid function can control the activities of an agent better than constant priorities with respect to getting tasks of various importance and urgency done with smaller Mean Earliness and smaller Mean Tardiness. Accepted version 2011-10-17T07:19:06Z 2019-12-06T18:46:08Z 2011-10-17T07:19:06Z 2019-12-06T18:46:08Z 2008 2008 Conference Paper Zhang, H., Huang, S. Y., & Chang, Y. (2008). An Agent's Activities are Controled by His Priorities. Proceedings of the 2nd KES International conference on Agent and multi-agent systems: technologies and applications (2nd:2008:Berlin). https://hdl.handle.net/10356/93821 http://hdl.handle.net/10220/7288 10.1007/978-3-540-78582-8_73 131727 en © 2008 Springer-Verlag Berlin Heidelberg. This is the author created version of a work that has been peer reviewed and accepted for publication by Proceedings of the 2nd KES International conference on Agent and multi-agent systems: technologies and applications (2nd:2008:Berlin), Springer-Verlag Berlin Heidelberg.  It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document.  The published version is available at: http://dx.doi.org/10.1007/978-3-540-78582-8_73. 10 p. application/pdf
spellingShingle DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Huang, Shell Ying
Chang, Yuming
Zhang, Huiliang
An agent's activities are controlled by his priorities
title An agent's activities are controlled by his priorities
title_full An agent's activities are controlled by his priorities
title_fullStr An agent's activities are controlled by his priorities
title_full_unstemmed An agent's activities are controlled by his priorities
title_short An agent's activities are controlled by his priorities
title_sort agent s activities are controlled by his priorities
topic DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
url https://hdl.handle.net/10356/93821
http://hdl.handle.net/10220/7288
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