A Framework for Knowledge Management and Automated Reasoning Applied on Intelligent Transport Systems

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by  Aneta Vulgarakis Feljan

A Framework for Knowledge Management and Automated Reasoning
Applied on Intelligent Transport Systems
AnetaVulgarakisFeljan1,AthanasiosKarapantelakis1,LeonidMokrushin1,HongxinLiang1,Rafia
Inam1,ElenaFersman1,CarlosR.B.Azevedo2,KlausRaizer2 andRicardoS.Souza2
1EricssonResearch,Sweden
2EricssonResearch-ARBB,Brazil
Email:fi[email protected]
Keywords: Knowledgemodel,Reasoning,Ontology,Modeltransformation,IntelligentTransportSystems
7
1 Abstract: Cyber-PhysicalSystemsingeneral,andIntelligentTransportSystems(ITS)inparticularuseheterogeneous
0
datasourcescombinedwithproblemsolvingexpertiseinordertomakecriticaldecisionsthatmayleadtosome
2
formofactionse.g.,drivernotifications,changeoftrafficlightsignalsandbrakingtopreventanaccident.Cur-

n rently,amajorpartofthedecisionprocessisdonebyhumandomainexperts,whichistime-consuming,tedious
a anderror-prone.Additionally,duetotheintrinsicnatureofknowledgepossessionthisdecisionprocesscannot
J
beeasilyreplicatedorreused.Therefore,thereisaneedforautomatingthereasoningprocessesbyproviding

1 computationalsystemsaformalrepresentationofthedomainknowledgeandasetofmethodstoprocessthat
1 knowledge.Inthispaper,weproposeaknowledgemodelthatcanbeusedtoexpressbothdeclarativeknowl-

edgeaboutthesystems’components,theirrelationsandtheircurrentstate,aswellasproceduralknowledge
]
I representingpossiblesystembehavior.Inaddition,weintroduceaframeworkforknowledgemanagementand
A automatedreasoning(KMARF).TheideabehindKMARFistoautomaticallyselectanappropriateproblem
. solver based on formalized reasoning expertise in the knowledge base, and convert a problem definition to
s
c thecorrespondingformat. Thisapproachautomatesreasoning,thusreducingoperationalcosts,andenables
[ reusabilityofknowledgeandmethodsacrossdifferentdomains.Weillustratetheapproachonatransportation

planningusecase.
1
v
0
0
1 INTRODUCTION perts. Moreover, in order to be able to provide sup-
0
3 portforinformationinteroperabilitybetweenapplica-
0 tions, ITS-related information needs to be semanti-
IntelligentTransportSystems(ITS)presentasubclass
. cally enriched in other words identified, categorized
1 of Cyber-Physical Systems (CPS) due to the inter-
0 action between physical systems (vehicles) and dis- anddescribedexplicitly.Eventhoughstandardization
7 tributed information acquisition and propagation in- activities have facilitated certain network-level inter-
1 operability (IEEE Standards Association, 2010) and
frastructure (wired/wireless networks, sensors, actu-
: Quality of Service (QoS), there are still inconsisten-
v ators, processors and software). They are designed
i toofferinnovativeservicestotransportationnetwork ciesbetweenITSgroupsonhowdataaremodeledon
X
a semantic level, as each organization (e.g., govern-
users and managers alike, and cover a broad area of
r mentregulator,commercialprovider,academicinsti-
a advancedapplications,rangingfromimprovingman-
tution)hascreateddomain-specificinformationmod-
agement and safety of the transportation network, to
elsforitsrespectiveITSapplications.
infotainmentservices(Karagiannisetal.,2011). ITS
in general use a vast amount of heterogeneous data In order to build intelligent systems one needs to
streamsandinformation(e.g.,behavioralmodels)that startbymodelingandformalizingtheknowledgethat
need to be analyzed, combined and actioned upon, exists (such as domain concepts, behaviors and con-
whichcreatesacomplexitythatgoesfarbeyondmere text) and is retrieved from human experts. Knowl-
human management. Hence, automation is a must, edgerepresentationandreasoning(KR&R)isafield
which in its turn requires formalization of knowl- in artificial intelligence that aims at building intelli-
edgeextractedfromthedifferentsourcessuchassen- gentsystemsthatknowabouttheirworldandareable
sor networks, documents, tools and from system ex- toautomaticallydrawconclusionsandactuponthem,
ashumansdo(Baral,2003). Afundamentalassump-
State State State
1 2 3
tion in KR&R is that knowledge is represented in a
is_bus(b25) is_bus(b25) is_bus(b25)
tangible form (usually via ontologies), suitable for is_bus_stop(bs1) is_bus_stop(bs1) is_bus_stop(bs1)
is_passeger(p1) is_passeger(p1) is_passeger(p1)
processingbydedicatedreasoningengines. However, at(p1, bs1) at(p1, bs1) at(b25, bs1)
up to today, there is a lack of frameworks for gen- capacity(b25, 23) at(b25, bs1) at(p1, b25)
waiting(p1, min(2)) capacity(b25, 23) capacity(b25, 22)
eralintelligencethatwillsolveseveralclassesofrea- waiting(p1, min(5))
soning problems, such as planning, verification and
Figure1:Specificationofthreeexamplestates.
optimization. Inaddition,itisstillnotcleartothein-
telligentsoftwarecommunityhowtoeffectivelycope
withtheintegrationofbothdeclarativeandprocedu- • An architecture of a generic framework for
ralknowledgeandsomeauthorsadvocateforkeeping knowledge management and automated reason-
the behavior descriptions separated from the seman- ing(KMARF)(Section2.2),relyingontheintro-
tic,staticdomainknowledge(Ghallabetal.,2014). ducedknowledgemodel.
In this paper, we present a Knowledge Man-
• A taxonomy of ITS domain concepts that can be
agement and Automated Reasoning Framework
usedwhenreasoningaboutITSspecificproblems
(KMARF), which targets multiple reasoning prob-
(Section3).
lems. The purpose for conceiving KMARF is to (a)
reducesystemdevelopmentanddeploymenttime,by • A prototype implementation of the KMARF ar-
reusing as much knowledge as possible, such as do- chitecture (Section 4) and an evaluation of the
main models, behaviors and reasoning mechanisms, benefits of KMARF in terms of reducing the ef-
and(b)reduceoperationalcostsbyrequiringlesshu- fort required for formalizing the knowledge and
man involvement in system operations. The strong performingthereasoningbasedonit(Section5).
point of KMARF is that it relies on a knowledge
modelthatcombinesbothdeclarativeandprocedural
knowledge. Thiscombinationallowsustodoexten- 2 KMARF – FRAMEWORK FOR
siveanalysisandprovideanswerstodifferentreason-
KNOWLEDGE MANAGEMENT
ing questions, such as “what is the optimal strategy
AND AUTOMATED
for reaching a desired system state?” or “which ac-
tionshaveledthesystemtoagivenstate?”. Inorder REASONING
touseproblemsolversspecializedinsolvingspecific
classes of reasoning problems, KMARF can be ex-
In this section, we introduce our Knowledge Man-
tendedwithmodeltransformationrulesthattranslate
agement and Automated Reasoning Framework
from our knowledge model to the targeted problem
(KMARF)byshowingitsarchitectureandtheknowl-
solvermodel.
edge model that it relies on. KMARF is targeting
KMARF is specifically targeting CPS, but it can multiple reasoning problem classes (such as plan-
beappliedinotherareasaswell. Inthispaper,wefo- ning,verificationandoptimization)thatcansharethe
cusononeoftheCPSdomainswhereKMARFmay same underlying state representation. This enables
be applied – the ITS domain. As an illustration ex- reusability of knowledge and methods across differ-
ample, we consider a transportation planning prob- entdomains.
lemi.e.,transportpassengersorgoodswithaminimal
cost.Acostcanbee.g.,thetraveleddistance,thetime
2.1 TheKnowledgeModel
neededfortransportationofeachofthepassengersor
goods, or the number of buses or trucks required for
Syntax We model declarative knowledge by de-
transportation. Theanswertothetaskmaybeaplan
scribing discrete states of a system. One such state
i.e., asequenceofstepsforthesystemtoperformin
may represent the current state, and the others may
order to reach the goal state. In case the task cannot
describeeitherprevioussystemstatesorhypothetical
be performed the answer from KMARF could be a
statesthatthesystemmayendupinthefuture.Inthis
reason why the task cannot be performed, as well as
context we do not strictly apply the notion of time,
the possible solution, which could be to increase the
i.e., the system may change its state instantly. How-
numberofvehicles.
ever, the order of states is important as it describes
Inbrief,ourcontributionisfourfold:
howthesystemevolvesandmayexplainthereasons
• A model for representing both declarative and behinditsprogress.
proceduralknowledgefromCPSandITSdomains A state is represented by an (implicitly conjunc-
inamachine-readableform(Section2.1). tive)setofpredicates{P ,P ,...}expressingthefacts
1 2
knownaboutthestate. Eachpredicateisacompound Transition1
term that has a form of P(a ,a ,...,a ), where P is
i 1 2 n i
Precondition Computation Action
a predicate’s functor specified as a literal, i.e., a se-
is_bus(B)
quence of characters, and a |j∈[1..n] are the argu- is_bus_stop(S) less_than(Y, 20) delete(waiting(P, U(Y))
j is_passeger(P) greater_than(C, 0) delete(at(P, S))
ments. The number of arguments n is called arity at(P, S) decrease(C, 1, NC) add(at(P, B))
at(B, S) delete(capacity(B, C))
of the predicate. If n=0, then Pi denotes a simple capacity(B, C) add(capacity(B, NC))
atomicfact.Ifn>0,thenP denotesafactualrelation waiting(P, min(Y))
i
betweenitsnarguments.Theargumentsofpredicates
Figure2:Anexampleofatransitionspecification.
mayinclude:
• numbersdenotingliteralquantity;
evolvefromstateState tostateState definedinFig-
2 3
• literals denoted by sequence of alphanumeric ure1. Aftermatchingtheprecondition,thecomputa-
characters that start from a lower case character tion checks if a passenger has been waiting for less
{a,b,c,...} that represent objects or concepts in than20minutesandifthereisenoughcapacitytoon-
thedomain(e.g.,carmayrepresent“acar”); boardapassenger,itdecreasesthebuscapacityby1.
The action removes the passenger waiting predicate,
• compoundtermsoftheformT(a ,a ,...,a )that
i 1 2 n
updatespassengerlocationandavailablebuscapacity
may have one or many arguments, which can
value.
be numbers, literals or compound terms (e.g.,
velocity(car,kmh(50)) may stand for “the veloc-
ityofthecarcaris50km/h”). Semantics Weformalizesemanticsofliterals,com-
pound terms and predicates by associating meanings
An example shown in Figure 1 contains a
to their symbols. For example, we use the predicate
state State defined using a set of six predicates
1
at(p1,bs1)tomodelthefactthatapassenger p1isat
{is bus,is bus stop,is passeger,at,capacity,waiting}.
thebusstopbs1. Wesaythatthemeaningofat isto
The arguments of the first three predicates declare
representaclosespacialrelationshipbetweenitstwo
existence of bus b25, bus stop bs1, and passenger
arguments.
p1 accordingly. The fourth predicate states that
We define semantics of our knowledge model in
passenger p1 is at bus stop bs1. The fifth predicate
terms of a transition system. Formally, a transition
indicates that bus b25 has 23 available places. The
systemisatuple(S,T,→),whereSisasetofstates,
last predicate declares a fact that passenger p1 has
T isasetoftransitionspecificationnames,and→is
beenwaitingatthebusstopfromlasttwominutes.
setofstatetransitions(i.e.,asubsetofS×T×S).The
Theproceduralknowledgeismodeledasacollec-
fact that (State ,Transition ,State )∈→ is written
tionofspecificationsofpotentialtransitionsbetween 1 1 2
states. A transition specification consists of a pre- as State −T−ra−n−si−ti−on→1 State , and represents a transi-
1 2
condition, a computation, and an action. Precondi- tion between a source state State and a destina-
1
tionandactionbothhavethesamesyntaxasthestate, tion state State by applying transition specification
2
i.e.,theyarerepresentedasasetofpredicates,except Transition .
1
the following two differences. Variables denoted by Inorderforatransitionspecificationtobeapplied
sequencesofalphanumericcharactersstartingfroma its precondition must match the source state and its
capitalletter{X,Y,Z,...}areallowedinargumentsof computationmustsucceed. Thesemanticsofmatch-
predicatesandcompoundtermsinbothprecondition ing the precondition with a state are formalized by
andaction. Theactionpredicatesarerestrictedtothe defining a logical unification between predicates of
set {add,delete}. Intuitively, action predicates de- thepreconditionandthepredicatesofthestate,asfol-
note the procedures performed with a state when a lows. We unify every predicate in the precondition
transition is performed. Computation is an ordered with all the predicates from the state, and use vari-
listofeffectfreefunctioncalls,i.e. theydonotmod- ablessubstitutionsinsubsequentunificationofthere-
ify the state and are only used during the processing mainingpreconditionpredicates. Iftheunificationof
of the transition. The arguments of a function call all precondition predicates with the state predicates
maybenumbers,literals,variables,andfunctions. If succeeds, the computed variable substitution is used
afunctionreturnsavalue,thelastargumentofafunc- in the computation and the action, as explained be-
tioncallisavariablethatholdsit.Theresultofafunc- low. Obviously, there can be multiple matches of a
tion call may be used as an argument in subsequent precondition with a source state. In this case, every
functioncallsofthecomputationorintheaction. match will produce a transition in → given that the
Example in Figure 2 demonstrates specification correspondingcomputationsucceeds.
of a transition Transition that allows a system to The meaning of the computation is evaluation of
1
KMARF
human
sensing Perception Engine predicates Knowledge Base domain and reasoning expertise
expertise
model
meta-reasoning states and transformation
expertise transitions rules
Reasoner
goal query
Inference Engine UI
d
orl user specified
w stop planner problem model checker
al format problem format goal
e
R Planner ... Model Checker property proof
strategy
actuating state changes expected state
Actuation Engine Interpreter
Figure3:Ahighlevel,conceptualviewofthearchitectureofKMARF.
itsfunctionsintheorderofspecification. Weusede- 2.2 Architecture
notationalsemanticstodefinethemeaningofafunc-
tionF asasetoforderedtuples Afterintroducingtheknowledgemodel,wecanmove
ondescribingKMARF.Ahigh-levelconceptualview
{<a1,...,a1 ,a1>,...,<am,...,am ,am>},
1 n−1 n 1 n−1 n of the architecture of the framework is depicted in
where ai,...,ai are function arguments, and ai is Figure 3. The main components of KMARF are the
1 n−1 n Perception Engine, the Knowledge Base, the Rea-
the value returned by F given those arguments. A
soner,theInterpreterandtheActuationEngine.
functioncallF(a ,...,a ,a )istheprocessoffind-
1 n−1 n
TheKnowledgeBaseisresponsibleforrepresent-
ingsucha forgivena ,...,a thatthereisatuple
n 1 n−1
<aj,...,aj ,aj >inthedefinitionofF forsome j. ing aspects of the domain under consideration (such
1 n−1 n asobjectsorconcepts,instancesandstates)andtheir
Ifthereisnosuchtuplefound,thefunctioncallfails.
relations, in well defined, machine processable syn-
Otherwise, the function call succeeds and the value
of aj is assigned to a corresponding variable. If the tax and unambiguous semantics. The format of the
n
knowledge stored in the knowledge base complies
returned value a is of boolean type, i.e. it belongs
n
withtheknowledgemodelintroducedinSection2.1,
totheset{true,false},thenthefunctionsucceedsif
andoneofthepossibleformatsisRDF/OWL.Inad-
a =trueandfailsifa = false. Acomputationsuc-
n n dition, the knowledge base contains meta-reasoning
ceedsifallthefunctioncallsinitsucceed.Otherwise,
expertise, as well as model transformation rules that
thecomputationfails.
aredescribedbelow.
The semantics of the transition action execution
TheReasonerisusedforsolvingreasoningprob-
aredefinedbytwooperations. Thefirstoperationin-
lems. By reasoning we mean solving problems re-
stantiates predicates in the action by applying com-
lated to planning, verification, optimization, etc. By
puted variable substitution to them. This means that
relating a user query to a meta-reasoning expertise1
all the variables in the action predicates are replaced
stored in the knowledge base the Inference Engine
withthecorrespondingvaluesfromthevariablesub-
draws a conclusion about an appropriate method or
stitution. The second operation copies all the pred-
a problem solver, and relevant prior knowledge for
icates from the source state to the destination state,
solving a given problem. For example, if the user
and for every predicate in the action we perform the
query is to reach a certain goal, the inference en-
followingproceduresonthedestinationstate:
gine will look up the knowledge base and deduce
• iftheinstantiatedpredicatesymbolisadd,thenits that a Planner should be used to generate a strat-
argumentistreatedasapredicate,anditisadded egy to reach that goal. Additionally, given that most
tothedestinationstate;
1Meta-reasoningisreasoningaboutreasoning,i.e.,itis
• iftheinstantiatedpredicatesymbolisdelete,then
comprised of computational processes concerned with the
itsargumentistreatedasapredicate,anditisre- operation and regulation of other computational processes
movedfromthedestinationstate. withinthesameentity(WilsonandKeil,2001).
of the planners accept planning problems in Plan- throughout the ontology. This is important because
ning Domain Definition Language (PDDL) (Mcder- ontologies often reuse and extend other ontologies.
mottetal., 1998)astheirinput, theinferenceengine Updatinganontologywithoutpropercarecanpoten-
looks up corresponding model transformation rules tially corrupt the others depending on it and conse-
that should be applied to formulate the problem in a quentlyallthesystemsthatuseit.
format understandable by the selected planner. The
Interpretertakesthegeneratedstrategyandmapsitto 3.1 UpperOntology
statechangesthatitgivestotheActuationEngine,so
thatitcanperformactuationintherealworld.
Upperontologiesshouldbedesignedtodescribegen-
Sincethephysicalworldisnotentirelypredictable
eral concepts that can be used across all domains.
KMARF needs to take into consideration that there
They have a central role in facilitating interoperabil-
might be changes in the information stored in the
ityamongdomainspecificontologies,whicharebuilt
knowledge base. The Perception Engine is respon-
hierarchically underneath the upper and generic lay-
sible,whenneeded,topushnewknowledgefromthe
ers,andthereforecanbeseenasspecializationofthe
environment(i.e.,predicates)intheknowledgebase.
moreabstractconcepts.
Additionally, when executing the strategy the Inter-
Figure 4 presents a subset of the proposed up-
preterworkstightlywiththeReasoner. Incasethere
per ontology. Its development was prompted by
areanychangesintheexpectedstateofthesystemthe
our use cases in management and control of com-
ReasonersendsareplanningrequesttothePlanner.
plex systems-of-systems, and was inspired by other
ontologies such as SUMO (Suggested Upper Merg-
erd Ontology) (Niles and Pease, 2001), and W3C
3 TAXONOMY OF ITS DOMAIN
SSN(SemanticSensorNetworkOntology)(Compton
CONCEPTS etal.,2012).
Someimportantconceptsdefinedontheproposed
This section describes the objects or the concepts of generalontologyincludeSystem,Cyber-PhysicalSys-
ITS domain and their relation to more generic CPS tem,AgentandCPSAgent. ASystemisasetofcon-
domain concepts. The concepts are organized in an nected parts forming a complex whole that can also
ontology that has multiple layers of abstraction. We be used as a resource by other systems. A Cyber-
design our ontology by combining high level con- Physical System is a system with both physical and
cepts and cross-domain relationships borrowed from computational components. They deeply integrate
three areas: CPS; Agent-Based Model (ABM); and computation, communication and control into phys-
Systems-of-Systems (SoS). The proposed ontology icalsystems. AnAgentisasystemthatcanactonits
consists of an Upper Ontology, which contains the own, sense the environment and produce changes in
CPS, ABM, and SoS concepts and relations and a theworld. Whenanagentisembeddedintoacyber-
generalITSDomainOntology. ThegeneralITSDo- physical system it is called a CPS Agent, or cyber-
mainOntologycanbefurtherreferencedfromontolo- physicalagent.
gies that instantiate transport-domain specific transi- Importantformathematicaldesciptionsofinterre-
tionsandstates,asdescribedinSection4. lations between systems are the elements Arc, Node
The objective of breaking the ontology into mul- and Graph. Where an Arc is any element of a graph
tiple levels is twofold. First, this approach allows that connects two Nodes, while a Graph is a set of
to capture and isolate different levels of properties, NodesconnectedbyArcs.
attributes and relationships. Higher layers provide TheconceptofSystemcanbefurtherexpandedby
broaderdefinitionsandmoreabstractconcepts,while anumberofattributes,suchasCapacity,RoleandCa-
lower layers are less abstract and can support spe- pabilitythatcanalsohaverelationshipsamongthem.
cific domains and applications with concepts and re- The System itself is represented within the Declara-
lations which might not be present in the upper lev- tive Knowledge as an Object. Affordance is a prop-
els. Second,ontologiesareexpectedtochange,grow erty the defines the tasks that can be done on a spe-
and evolve as new domains and techniques are con- cificSystem,whileCapabilitydefinesthesetoftasks
templatedinthem(Daviesetal., 2006). Leavingthe thesystemcanperform. SystemscanalsohaveCon-
moreabstractandgeneralconceptsinanupperlayer, straints,whichinturnarerelatedtoKPIsthatareused
andthemorespecificonesinlowerlayers,reinforces tomeasurewhethersuchconstraintsaresatisfied.
theideathatalteringthemostgeneralconceptsshould The higher level of the proposed ontology also
beavoided,makingthemlesslikelytosufferconstant provides definitions and relationships between the
modificationsthatcouldleadtounnecessarychanges mainKnowlegdeBaseconcepts, theDeclarativeand
Figure6:TheITSDomainOntology.
Figure4:Anupperontologyfordomainintegration.
centralconceptswithintheITSdomainistheTrans-
portAgent, thatextendsAgent fromtheupperontol-
ogy. The Transport Agent encompasses agents that
arecapableoftransportingsomeentity,rangingfrom
physical goods to virtual data. Some important con-
ceptsfromtheupperlayersthatapplytotheTransport
AgentincludeDynamicsandCapacity,amongothers.
Transport Agents in turn are strongly related to the
Abstract concept of Transportation Mode which de-
fines the type of transportion scenario (e.g., Roads,
Figure5:KnowledgeBaserepresentation. Rail,Telco).
Another important concept is the Transportation
Procedural Knowledge. In our knowledge model, a
Infrastructure which encompasses all elements re-
Transition is a Procedural Knowledge concept that
quired by a Transportation Mode, such as Routes,
determines how to achieve a certain state (Action)
TracksandTransportationNetworks. Mostelements
given that an agent observes a particular state (Pre-
within the Transportation Infrastructure are exten-
condition) as being true in the world and there is an
sionsofGraph,ArcandNode,abstractconceptsfrom
ordered list of effect free function calls in that state
the Upper ontology. Therefore, by using high level
(Computation). Meanwhile, both Precondition and
graph definitions it is possible to define most of the
ActionhaveaPredicateSet thatisdirectlyrelatedto
transportation infrastructure in an ITS Domain. A
theconceptofStatefromtheDeclarativeKnowledge.
node inside the transportation infrastructure is re-
TheGoalState, whichisanspecificationofState, is
ferredtoasaPOI(PointofInterest)anditcanbeany
relatedtotheconceptsofTaskandWorkflowfromthe
desired location within the Transportation Network
ProceduralKnowledge. WhereaWorkflowisdefined
(e.g., a crossing, a specific point in the route, coor-
as sequence of Tasks, which in turn is defined by a
dinate,awarehouse,abusstop).ATrafficSemaphore
sequence of Goal States assigned to a single Agent.
ismodeledasagenericActuator thatisusedtocon-
Figure5presentsthemainelementsoftheknowledge
trol and regulate traffic and it can be applied in any
basemodeling.
transportationscenario.
3.2 ITSDomainOntology ATransportableEntityencompassesanyelement
thatcanbetransportedbyaTransportAgent,suchas
With the support of the presented upper ontology regularCargoornetworkData. AtypicalPassenger
model,inthissectionweproposeanITSdomainspe- is also a Transportable Entity and extends the upper
cific ontology, as depicted in Figure 6. One of the ontologyconceptofHuman.
Knowledge Base
Static Data
Reusable Data
Upper.ttl Reference
ITS_Domain.ttl transition_library.ttl Library Entities
state_library.ttl Reference
User Data
TransportPlanner
TR.ttl busTransitions.ttl Knowledge Engineer API:
busStates.ttl Create New Transition and State File
with potential reference to reusable
- Transport States and
transitions and states
Fleet Planner Transition Files for
[transition file, state file]
- Model Bus Transport domain
Transformation Fleet Manager API:
Problem and
Rules (TR) File Trigger Execution of new Plan
Domain File
[transition file, state file, TR file]
Generator
domain_file.ttl
Trigger
problem_file.ttl
Replanning
Generated
Plan
PDDL Executor PDDL Planner, PDDL
(third party) Intepreter (third party)
Monitor, Actuate
Environment: Connected
Vehicles, Roadside Sensors
and Actuators, …
Figure7: Overviewoftheimplementedsystem. The“.ttl”extensiondenotesmodelfilesinTurtleformat. Componentsin
dottedrectanglesarethird-party.
4 IMPLEMENTATION particular transport planning problem. The mod-
els are described using semantic web technologies
and are based on the W3C Web Ontology Language
Thissectiondescribescurrentprogresstowardsapro-
(OWL)(Hitzleretal.,2012)andstoredinTurtlefor-
totypeimplementationoftheKMARFarchitectureil-
mat (Beckett et al., 2014). The other component
lustrated in Figure 3. The implementation targets a
is a PDDL Generator, which, given the transforma-
large problem area in ITS known as “transportation
tion rules, states & transition files as input, gener-
planning”, which we define as the schedules gener-
ates a problem and domain file in PDDL language.
ated for a set of vehicles to pickup and alight peo-
PDDLGeneratorisimplementedinJava(Jamesetal.,
pleorcargoalongoneormoreroutes,withinagiven
2015) and uses Apache Jena (Apache Foundation,
amount of time (see also Section 1). The transport
2016)forparsingtheTurtle-formattedinputfromthe
planning problem includes a set of connected vehi-
knowledgebase. Additionally,EclipseJetty(Eclipse
cles, for example buses or trucks, and a central co-
Foundation, 2016) provides a Representational State
ordinating function that computes the schedule and
transmits it to these vehicles2. In this implementa- Transfer(REST)APIfortriggeringPDDLfilegener-
ation,anddefiningcustomstatesandtransitionmod-
tionweassumethattheInferenceEnginecomponent
els. Morespecifically:
has already deduced that a Planner should be used
to solve the transportation planning problem, using
• The API allows human experts (for example
meta-reasoning expertise and user query data sup-
knowledgeengineers)tospecifyatransportlogis-
pliedfromtheKnowledgeBaseandtheuserinterface
ticsproblem,byaddinganewinitialandgoalstate
componentsrespectively.
intheknowledgebase,intheformofastatefile,
Figure 7 shows the components of the imple-
and a set of transitions with precondition, com-
mentedsystem. Oneofthecomponentsimplemented
putation and action parts in the form of a transi-
is the Knowledge Base, which contains model trans-
tion file. These two files are jointly used by the
formationrulesforPDDLlanguageaswellasstates
PDDLGeneratorsoftwarecomponentinorderto
& transition models that are based on the structure
generate a new schedule. The state file defines
defined in Section 3 and contain information for the
theagents,vehiclesandroutes,containsinforma-
2Correctinterpretationoftheschedulerestsontheve- tionabouttheinitialstateofthesystem(e.g.,the
hicles,whichcanbepartiallyorcompletelyautonomousor location of agents in the route, the route and its
theymayalsohavehumandrivers. waypoints,thetimerequiredforvehiclestotravel
Table1:Knowledgefortransportplangeneration.
aroute,etc.) anddefinesgoalconditions(e.g.,all
agentsareserviced). Thetransitionsfiledescribes Title Description
intermediatetransitionsthatareusedbytheplan-
Route Spec- Specification of the route(s)
ner to reach the specified goal state from the ini-
ification graph(s), which includes vertices
tialstate. Anexampleofsuchplancanbefound
(coordinates), edges (roads) and
at(KMARFauthors,2016).
edge-traversal costs, metrics (e.g.
• The API also provides means for triggering gen- timetotravel,fuelspent,etc.)
eration of a new PDDL problem and domain file
Transport Number of vehicles, Vehicle IDs
given the above input on request of a human or
Agents (e.g.VINcodes)andvehiclecapac-
other system. Typically this request is created
ity
from a customer (e.g., human operator, or an au-
tomated fleet management system). Once gen- Transportable NumberandIDoftransportableen-
erated, the files are assigned Universal Resource Entities tities
Identifiers (URIs). An external system can sub- Starting Where are the vehicles located,
sequently perform Hypertext Transfer Protocol Conditions wherearethepassengerslocated
(HTTP) GET requests using the URI references
Transition What transitions the transport
toretrievethefiles. Anexampleofsuchasystem
definitions vehicles perform (which include
canbeaPDDLsolver3. Forthisimplementation,
preconditions, optionally compu-
weuseathird-partysolvernamed“OPTIC”,orig-
tations and actions) . There are
inally developed by Benton et al (Benton et al.,
currently three actions available
2012).
in the library: pickup-agent, drop-
The authors have released the current implemen- agent,move-to-next-coordinate
tation of the Knowledge Base and PDDL Genera-
Goal condi- Final state: what criteria needs to
tor as open-source, available for the community to
tions besatisfiedinorderforthetransport
use (KMARF authors, 2016). Currently, there is no
servicetoconcludeonthespecified
componenttosupportinteractionwiththerealworld,
route (usually this means that all
both for triggering the planning process, but also for
agentsarepickedup,off-boardedin
actuatingreal-worldconnecteddevices(e.g. busesor
specificpartsoftheroute).
sensors) upon execution of the generated plan, how-
ever this is planned work. In its current state the
implementation can be used for rapid prototyping of
transportation planning functions. In addition to the WedefineCODastheeffortrequiredforformalizing
software itself, the “Upper”, “ITS”, “PDDL model theknowledgerequiredinorderforthesystemtostart
transformation rules” and a set of common reusable theautomatedtransportschedulegenerationprocess.
transitionsandstateontologiesareprovidedinTurtle Naturally, there exists a direct relationship between
format. theamountofknowledgetobeformalizedandtheef-
fort required, meaning that the more the amount of
non-formalizedtransportplan-relatedknowledge,the
5 EVALUATION moretherequiredeffort(e.g. intermsoftime,human
resource allocation, money etc.). Table 1 shows the
Inthissection,weevaluatetheimplementedsystemin differentaspectsofknowledgerequiredforthetrans-
termsofreusability. Giventhepracticallimitationof portationplan.AsdescribedinSection3,weviewthe
nothavingarealorsimulatedtestbedofconnectedve- transport network as a graph, with Points of Interest
hiclesasdescribedintheprevioussection,itisnotyet (POIs) as vertices on the graph and POI-connecting
possibletoevaluatesomeaspectsofthesystem(e.g. roadsasedges. Notethatthetablereferences“trans-
efficiency, performance, etc.) in realistic conditions. portable entities”. These entities can be passengers
Whatwedescribeinsteadisanevaluationoftheben- or cargo, depending on the use case. An interesting
efits this system brings on “cost-of-design” (COD). observationcanbemaderegardingtransitions,which
are similar regardless of the route, number and type
3In its current form, the API does not support adding
of transport agents (e.g., buses or trucks) and trans-
ofnewmodeltransformationrules,whichmeansthatonly
portable entities. Therefore, reusing these actions
PDDL language is supported. In the future however, we
acrossdifferenttransportplanningusecases,andstor-
plan to expand the functionality by adding support for
“pluggable”problemsolvingexpertisefiles,bothforPDDL ingthemaspartofthe“transitionlibrary”(seeFigure
andProlog. 7),issomethingthatcanpotentiallyreduceCOD.
TomeasuregainsinCOD,wehavedefinedasim- codeknowledgethemselves,ratherthanusingknowl-
plemetricwenamed“reusabilityindex”. Thismetric edgeengineers. TheconceptualmodelofKEWIcon-
istheratioofreusedentities,versustheratiooftotal sists of three main parts: an ontology for describing
entities in the knowledge input to PDDL Generator entitiesthatoccurinthecaptureddomain,amodelof
to generate the plan. For the bus use case, this ratio primitive rules (called actions) that can be executed
was 0.364 and for the truck use case, the ratio was bythesystemandhigh-levelmethodsforaccomplish-
0.251. This means that out of the total number enti- ing complex tasks. KEWI is specifically developed
ties created for each of the bus and truck use cases, for planning problems, whereas our framework tar-
36.4% and 25.1% of them were already available in gets different classes of reasoning problems by shar-
thelibraryrespectively. Weobservethatbothindices ing the same underlying state representation, as for-
arerelativelysignificant,whilethedifferencebetween malizedinourknowledgemodel.
the two is mainly attributed to the difference of the
TheitSIMPLEtoolwasfirstproposedinVaquero
route specification entities, as agents in both routes
et al. (Vaquero et al., 2005) for supporting the cre-
followed different paths. One observation that we
ation of generic planning systems by integrating do-
have made a posteriori to our measurements, is that
mainandproceduralknowledge,whileautomatically
thereusabilityindexcanbelarger,iftheroutespecifi-
generatingPDDL(Mcdermottetal.,1998)files.They
cationispartofthePDDLGenerator,whichcanauto-
proposeusingUMLformodelingdomainknowledge
maticallygeneratethespecificationusingdatafroma
and Petri Nets for modeling the procedural knowl-
mappingserviceinconjunctionwitharoutinglibrary.
edge regarding feasible state transitions, while us-
The knowledge engineer could then only specify the
ing XML as an internal language. The tool has
desiredwaypoints(e.g.,thebusstops),andtheroutes
since evolved (Vaquero et al., 2012) and has been
and graphs would be generated automatically by the
demonstratedinseveralcross-domainusecases,such
PDDLGenerator. Wearecurrentlyintheprocessof
as petroleum plant operations planning (Sette et al.,
evaluatingdifferentmappingservicessuchasGoogle
2008).
MapsandOpenStreetMapandopen-sourceroutingli-
In fact, when developing a framework that in-
braries such as GraphHopper and DirectionsService
tendstocombineknowledgeengineeringwithmulti-
fortheirapplicabilityinourimplementation.
pledeclarativeandproceduralreasoningclasses(such
as semantic reasoning and planning) one important
choice to make is the language (or languages) used
6 RELATED WORK to model problems and the required information for
solvingthem. In(Anisetal.,2014)theauthorscom-
Asmallnumberofframeworksexistthatsupportthe parethreeclassicallanguagesformodelingproblems
formalization of declarative and procedural knowl- in the cyber-physical production systems, namely:
edge. In (Vaquero et al., 2011) the authors present Prolog, Timed Automata (Alur and Dill, 1994) and
an overview of tools and methods in KEPS (Knowl- PDDL.Eachlanguagehasitsprosandcons,andcan
edgeEngineeringforPlanningandScheduling)area. be used for solving different reasoning problems. In
They classify the process of knowledge engineering KMARF we provide a “glue” knowledge model that
intosixphases: RequirementsSpecification, Knowl- depending on the reasoning problem to be solved is
edgeModeling,ModelAnalysis,DeployingModelto intended to be translated to other languages, such as
Planner, Plan Synthesis and Plan Analysis and Post- Prolog,TimedAutomataorPDDL.
Design. Thentheyprovidealistoftoolsandmethods Anotherrelatedfield ofworkisthearea ofstudy
thatareusedintheliteratureateachofthosephases. dealingwithcognitivearchitectures,whichareframe-
When commenting on these tools, the authors men- works that specify the underlying infrastructure for
tion that to that moment none of them treated dif- an intelligent system and include aspects of a cog-
ferently the knowledge encapsulated in the planning nitive agent that are constant over time and across
problemandinthesurroundingdomain,whichmakes different application domains (Langley et al., 2009).
ithardertoreuseknowledgeinotherdomains.Incon- Assuch,theyoftenincludememorymechanismsfor
trast,thegoalofKMARFistoexactlysupportreuse storing and processing different types of knowledge,
of knowledge, by relying on a common knowledge e.g., procedural, semantic and episodic knowledge.
modelacrossdifferentdomains. Thisknowledgeisusedasthebasisforactionselec-
KEWI(Wickleretal.,2015)isaknowledgeengi- tion, which can be reactive, based on planning or a
neeringtoolthathasbeendesignedtohelptheformal- hybrid of these two. There are many cognitive ar-
ization of a procedural knowledge used for planning chitectures (Samsonovich, 2010), each with its own
problems. Theideaistoenabledomainexpertstoen- strategies and constraints when dealing with plan-
ning, acting, sensing and knowledge management. ation of such systems. We have illustrated the usage
Some of the most notorious ones are SOAR (Laird, ofpartsofKMARFonanimplementationofagener-
2012) and CLARION (Licato et al., 2014). In atingtransportplanner, whichcanbeusedtorapidly
SOAR, for instance, procedural knowledge is repre- prototypeschedulersofvehicletransportfleets.
sentedbyif-thenrulesinamannersomewhatsimilar Therearecurrentlymanydifferentabstractionsin
to the STRIPS (Stanford Research Institute Problem theliteratureforhandlingthedesignofcomplexreal-
Solver)/PDDLformalism,whilesemanticknowledge world systems, including Cyber-Physical Systems,
abouttheworldisrepresentedinagraphheldinwork- Agent-Based Model, and Systems-of-Systems engi-
ingmemory. CLARIONontheotherhandemploysa neering,eachofthemaccompaniedbytheirownfor-
hybrid approach by integrating a rule based system malisms and theory. Our vision is that we can pro-
with artificial neural networks. KMARF has a num- videbettersupportforlinkingcross-domainusecase
berofsimilaritieswhencomparedtocognitivearchi- applications by integrating the common elements of
tectures. For instance, KMARF intends to improve thoseformalismsviaourknowledgemodel.
re-usabilityacrossdifferentdomains, whichisalsoa IntheimplementationofKMARFwehavesofar
natural feature in biologically-based cognitive archi- progressed into the development of an ontology for
tectures.Anothersimilarityisinknowledgerepresen- theknowledgebaseusingOWLWebOntologyLan-
tationsincemostcognitivearchitecturesalsoorganize guage, and we have used the framework to automat-
knowledge into either declarative memory or proce- icallygeneratePDDLfiles,feedthemintoaplanner,
dural memory. Finally, intelligent agents based on andcreateplans. Since, KMARFhasamuchbigger
cognitive architectures are also known for perform- vision than solving planning problems, in the future
ing a range of reasoning tasks such as planning and weplanonstudyinghowourknowledgemodelcanbe
verification. correlatedtootherformalismse.g.,TimedAutomata.
InourefforttoprovideKMARFwiththecapabil- Asfuturework, wealsoplantostudyhowmeta-
itytoreasonaboutproblemsindifferentdomains,we reasoningcanhelpKMARFtodeterminewhichprior
have included in it a CPS based ontology which de- knowledge and algorithms are relevant when a new,
scribes the relationships among CPS concepts. This problemorunforeseeninstancearrives.Suchinstance
approach of using ontologies to increase generality corresponds to the current state of the world, along
is well known and improves KMARF’s capability to with all current available sensory information. Ini-
reuse knowledge in different domains. One exam- tially,thesystemonlyknowshowtosolveproblemsit
ple of related work can be seen in covering Multi- hasseenbeforeandhadpreviouslyfoundreductions
AgentSystem(MAS)andCPS,ashasbeenexplored that could be solved separately using known proce-
in (Lin et al., 2010), where a MAS-based semantic dures. Ifitcannotfindsuchareductionforanewin-
modeling approach to CPS in the water distribution stance,itmustrecurtospacestateexplorationforgen-
domainwasdescribed. Thegoalwastodynamically eratingasequenceofstatetransitionsthateitherlead
integrate information from sensor networks with se- tothespecifiedgoalstateortoabetterstateinwhich
manticservicestosupportreal-timedecision-making. eitherthesystemknowshowtoproceedwithfurther
Two challenges were effectively addressed when de- reductions or declares the problem as intractable un-
signingtheontology: modelaccuracyandintegration deritscurrentknowledge.
of physical and cyber components; and pinpointing
inter-dependenciesbetweenCPScomponents.
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