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ASCoL: Automated Acquisition of Domain Specific Static Constraints from Plan Traces

Jilani, Rabia, Crampton, Andrew, Kitchin, Diane E. and Vallati, Mauro (2014) ASCoL: Automated Acquisition of Domain Specific Static Constraints from Plan Traces. In: The UK Planning and Scheduling Special Interest Group (UK PlanSIG) 2014, 15th December 2014, Teeside, UK.

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Abstract

Domain-independent planning systems require that domain
constraints and invariants are specified as part of
the input domain model. In AI Planning, the generated
plan is correct provided the constraints of the world
in which the agent is operating are satisfied. Specifying
operator descriptions by hand for planning domain
models that also require domain specific constraints is
time consuming, error prone and still a challenge for the
AI planning community.
The LOCM (Cresswell, McCluskey, and West 2013)
system carries out automated generation of the dynamic
aspects of a planning domain model from a set of example
training plans. We enhance the output domain
model of the LOCM system to capture static domain
constraints from the same set of input training plans as
used by LOCM to learn dynamic aspects of the world.
In this paper we propose a new framework ASCoL (Automated
Static Constraint Learner), to make constraint
acquisition more efficient, by observing a set of training
plan traces. Most systems that learn constraints automatically
do so by analysing the operators of the planning
world. Out proposed system will discover static
constraints by analysing plan traces for correlations in
the data. To do this an algorithm is in the process of
development for graph discovery from the collection of
ground action instances used in the input plan traces.
The proposed algorithm will analyse the complete set of
plan traces, based on a predefined set of constraints, and
deduces facts from it. We then augment components of
the LOCM generated domain with enriched constraints.

Item Type: Conference or Workshop Item (Paper)
Subjects: Q Science > Q Science (General)
Schools: 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
Related URLs:
Depositing User: Mauro Vallati
Date Deposited: 16 Dec 2014 15:17
Last Modified: 09 Dec 2016 17:29
URI: http://eprints.hud.ac.uk/id/eprint/22802

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