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Exploring Knowledge Engineering Strategies in Designing and Modelling a Road Traffic Accident Management Domain

Shah, Mohammad Munshi Shahin, Chrpa, Lukáš, Kitchin, Diane E., McCluskey, T.L. and Vallati, Mauro (2013) Exploring Knowledge Engineering Strategies in Designing and Modelling a Road Traffic Accident Management Domain. In: Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. IJCAI 3-9 August 2013 . AAAI Press / International Joint Conferences on Artificial Intelligence, Beijing, China, pp. 2373-2379. ISBN 978-1-57735-633-2

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Abstract

Formulating knowledge for use in AI Planning engines
is currently something of an ad-hoc process,
where the skills of knowledge engineers and the
tools they use may significantly influence the quality
of the resulting planning application. There is
little in the way of guidelines or standard procedures,
however, for knowledge engineers to use
when formulating knowledge into planning domain
languages such as PDDL. This paper seeks to investigate
this process using as a case study a road
traffic accident management domain.
Managing road accidents requires systematic,
sound planning and coordination of resources to
improve outcomes for accident victims. We have
derived a set of requirements in consultation with
stakeholders for the resource coordination part
of managing accidents. We evaluate two separate
knowledge engineering strategies for encoding the
resulting planning domain from the set of requirements:
(a) the traditional method of PDDL experts
and text editor, and (b) a leading planning GUI with
built in UML modelling tools.
These strategies are evaluated using process and
product metrics, where the domain model (the
product) was tested extensively with a range of
planning engines. The results give insights into the
strengths and weaknesses of the approaches, highlight
lessons learned regarding knowledge encoding,
and point to important lines of research for
knowledge engineering for planning.

Item Type: Book Chapter
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Schools: School of Computing and Engineering
School of Computing and Engineering > High-Performance Intelligent Computing > Planning, Autonomy and Representation of Knowledge
School of Computing and Engineering > High-Performance Intelligent Computing > Planning, Autonomy and Representation of Knowledge
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Depositing User: Mauro Vallati
Date Deposited: 22 Apr 2013 11:37
Last Modified: 02 Dec 2016 08:37
URI: http://eprints.hud.ac.uk/id/eprint/17280

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