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Generalised Domain Model Acquisition from Action Traces

Cresswell, Stephen and Gregory, Peter (2011) Generalised Domain Model Acquisition from Action Traces. In: ICAPS 2011, 11th-16th June 2011, Freiburg, Germany.

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Abstract

One approach to the problem of formulating domain models
for planning is to learn the models from example action sequences.
The LOCM system demonstrated the feasibility of
learning domain models from example action sequences only,
with no observation of states before, during or after the plans.
LOCM uses an object-centred representation, in which each
object is represented by a single parameterised state machine.
This makes it powerful for learning domains which fit within
that representation, but there are some well-known domains
which do not.
This paper introduces LOCM2, a novel algorithm in which
the domain representation of LOCM is generalised to allow
multiple parameterised state machines to represent a single
object. This extends the coverage of domains for which an
adequate domain model can be learned. The LOCM2 algorithm
is described and evaluated by testing domain learning
from example plans from published results of past International
Planning Competitions.

Item Type: Conference or Workshop Item (Paper)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Schools: School of Computing and Engineering > Informatics Research Group > Knowledge Engineering and Intelligent Interfaces
School of Computing and Engineering
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Depositing User: Peter Gregory
Date Deposited: 10 Jan 2012 09:35
Last Modified: 14 Feb 2012 11:17
URI: http://eprints.hud.ac.uk/id/eprint/12213

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