Joint International Technology Conference (JIST2011)
Hangzhou, China, December 5, 2011
An Ontological Formulation and an OPM profile for Causality in
Planning Applications
Irene Celino and Daniele Dell’Aglio
CEFRIEL – Politecnico di Milano, Italy
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Summary Introduction Planning metamodel OWL formalization OPM mapping Inference over the model Use case – PANDORA Conclusions and future work
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Planning Problem Definition of sequences of actions to reach a
desired goal Automated planning & scheduling in AI
The task requires a domain theory – a model with the knowledge useful to generate plans Agents, actions, causal relationships, etc. Defined by a modeller
Our research focus on helping the modeller in checking the coherence and rationality of the domain theory
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Domain theory – core elements 1/2 Component: physical or logical subsystem of
interest for the planning Controllable vs Uncontrollable Agent vs Resource
Action: temporally tagged event Event: an action determined by the planner
(related to a controllable component) Decision: an action taken by an uncontrollable
component
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Domain theory – core elements 2/2 Planning Rule: representation of actions’
causality – specifies the consequences of actions Reference Action Rule Targets: actions that could be “caused” by
the reference action Rule Conditions: requirements on the actions
involved in a planning rule, expressed through rule relations: Temporal conditions Constraints Assignments
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An OWL2 FormulationCore elements
The whole ontology is available at: http://swa.cefriel.it/ontologies/tplanning
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hasRuleCondition
Component
PlanningRule
ActionisActionOf
hasReferenceComponent
hasReferenceActionRuleTarge
t
hasRuleTarget
hasActionValue
isRuleEffectActionOf
actionTriggersAction
ruleTriggersRule
Rule Conditio
n
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An OWL2 Formulation Rule conditions
Temporal rule relations were modelled using Allen’s Interval Algebra
The three kinds of rule conditions are defined extending SPIN vocabulary (SPARQL Inference Notation, http://spinrdf.org/)5/12/2011
hasRuleCondition
PlanningRule
Rule Conditio
nrdfs:subClassOf
AssignmentCondition
Constraint
Condition
TemporalConditio
n
rdfs:subClassOf
sp:Filter
sp:Function
sp:expressionsp:variablesp:expression
sp:arg1, sp:arg2, ......
sp:Let
rdfs:subClassOf
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Open Provenance Model (OPM) Model for the tracking of the provenance of
artifacts Three main concepts:
OPM Profiles We mapped using the OPM Vocabulary (OPMV)
to define an OPM Profile5/12/2011
opmv:Artifact
opmv:wasTriggeredBy
op
mv:w
asD
eriv
ed
Fro
m
opmv:wasGeneratedBy
opmv:used
opmv:wasControlledByopmv:Agent opmv:Process
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Mapping the planning metamodel and OPMV
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opmv:Artifactopmv:Artifact
opmv:Processopmv:Process
opmv:Agentopmv:Agent
hasRuleCondition
Component
PlanningRule
ActionisActionOf
hasReferenceComponent
hasReferenceActionRuleTarge
t
hasRuleTarget
hasActionValue
isRuleEffectActionOf
actionTriggersAction
ruleTriggersRule
Rule Conditio
nopmv:wasTriggeredBy
opmv:wasDerivedFrom
opmv:wasGeneratedBy
opmv:used
opmv:wasControlledBy
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Checking of the domain theories Meta-model to represent domain theories
Vocabulary Axioms
It is possible to model domain theories using the ontology
Inference processes on domain theories are available
Semi-automated checking to the domain theories Extraction of relevant information from the model
for the modeller
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Domain theory checking Orphan elements 1/2 Extract from the
planning model the orphan elements: Components not involved
in any Action Actions not involved in
any Planning Rule Allow the modeller to
check potential lacks or shortcomings
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C1
C2
C3
C4
A1 A
3A2
A4
P1
P2
C5
A5
Orphan componen
t
Orphan
action
Com
ponents
Actio
ns
Pla
nn
ing ru
les
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Domain theory checking Orphan elements 2/2 Example: Identify orphan components Formal definition of orphan component
Identification done through SPARQL 1.1 queries to the model:
SELECT ?orphanComponentWHERE{
?orphanComponent a tpl:Component.FILTER NOT EXISTS{
?action tpl:isActionOf ?orphanComponent.
}}
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Inference and automated checking Action reachability Reachable action: action with a target role in
one or more planning rules Modeller is interested in finding:
Unreacheble actions: actions generated by controllable components that are never target
Actions triggered by the unrecheable action
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Unreachable Action: A1
A1 dependent actions: A2, A3... Anreferencetarget
triggers
A1
AnA
2
P1
Pn
P2
A3
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Use case – Pandora Application in Simulation Learning for
decision making in a scenario of crisis management
Used in the Pandora EU FP7 project Realization of a platform for the training of gold
commanders Planning is used to simulate learning sessions Support at the design time for the building of
domain theories Additional info on: http://pandoraproject.eu
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Conclusions and future work Use of Semantic Web in planning:
Modelling of domain theories Semi-automated approach to verify the modelling:
Tracking causality Check of elements involvement …
Future work In-depth evaluation Relation with PDDL Analysis of executed plans
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Thank you!
An Ontological Formulation and an OPM profile for Causality in Planning Applications
Daniele Dell’AglioCEFRIEL – ICT Institute of Politecnico di Milano, Italy
e-mail: [email protected]: http://www.cefriel.it
5/12/2011 JIST 2011, Hangzhou, China