Please use this identifier to cite or link to this item:
|Title:||An agent's activities are controlled by his priorities||Authors:||Huang, Shell Ying
|Keywords:||DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence||Issue Date:||2008||Source:||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).||Abstract:||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.||URI:||https://hdl.handle.net/10356/93821
|DOI:||http://dx.doi.org/10.1007/978-3-540-78582-8_73||Rights:||© 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.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Conference Papers|
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.