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  5. Demonstrating Social Error Recovery with AgentFactory
 
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Demonstrating Social Error Recovery with AgentFactory

Author(s)
O'Hare, G. M. P. (Greg M. P.)  
Collier, Rem  
Ross, Robert  
Uri
http://hdl.handle.net/10197/6249
Date Issued
2004-07
Date Available
2014-12-17T15:51:53Z
Abstract
Exception handling is a well established method of error recovery through the alteration of plans in situ . This method relies on recovery routines existing in advance, which - we
argue - is simply shorthand for more detailed plan descriptions. However, in practice, agents rarely act alone in their environment - other agents may exist, and potentially provide help in times of need. We argue that social error recovery is a particular class of exception handling that allows agents to resolve erroneous situations that are beyond their direct control. In our opinion, agent oriented programming languages must directly provide agents with abilities
like social error recovery. Consequently, we introduce revisions to the AgentFactory framework, and more specifically, the programming language (AF-APL), which facilitate
the rapid development of agents with in-built social error recovery. The use of these abilities are illustrated via an example of a social error recovery scenario for a mobile robot working as an office assistant.
Sponsorship
Enterprise Ireland
Other Sponsorship
DFG SFB/TR 6023 project on Spatial Cognition - Subproject I3-SharC
Type of Material
Conference Publication
Publisher
IEEE
Copyright (Published Version)
2004 IEEE
Subjects

AgentFactory Programm...

Belief-desire-intenti...

Error recovery

DOI
10.1109/AAMAS.2004.103
Language
English
Status of Item
Peer reviewed
Journal
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Conference Details
3rd International Joint Conference on Autonomous Agents and Multi Agent Systems (AAMAS04), New York, USA, 19-23 July 2004
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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aamas2004a.pdf

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663.39 KB

Format

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Checksum (MD5)

12ab9e411c72ca78c5c40ed408b06842

Owning collection
Computer Science Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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