An Ontological Formulation and an OPM profile for Causality in Planning Applications

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

[email protected]

mailto:[email protected]

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Summary Introduction Planning metamodel OWL formalization OPM mapping Inference over the model Use case – PANDORA Conclusions and future work

5/12/2011 JIST 2011, Hangzhou, China

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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

5/12/2011

hasRuleCondition

Component

PlanningRule

ActionisActionOf

hasReferenceComponent

hasReferenceActionRuleTarge

t

hasRuleTarget

hasActionValue

isRuleEffectActionOf

actionTriggersAction

ruleTriggersRule

Rule Conditio

n

JIST 2011, Hangzhou, China

http://swa.cefriel.it/ontologies/tplanning

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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


http://spinrdf.org/

http://spinrdf.org/

http://spinrdf.org/

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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


http://pandoraproject.eu/

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


mailto:[email protected]

http://www.cefriel.it/