Visit to MIT

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Visit to MIT
Artem Katasonov, PhDUniversity of Jyväskylä,
Finland
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Finland

• Area of 338.145 square kilometers - 8th biggest
  country in Europe.
• Population is 5.3 million.
• Only 16 people per square kilometer - the most
  sparsely populated country in the European Union.
• As a countermeasure, Finland is among
  world-leaders in use of ICT technologies.
  Homeland of Nokia.
• Around 92 of people has mobile phone and 58
  has Internet connection at home.
• Paying bills, buying any kind of tickets, filing
  official papers, etc all can be done over
  Internet.

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University of Jyväskylä

• Started in 1863 as a Teacher training school.
  Established as university in 1966.
• Around 16.000 students and 2.500 staff.
• Student acceptance rate is 1/7 one of the most
  popular universities in Finland.

• Has 7 faculties Humanities, Education, Social
  Sciences, Sport and Health Sciences, Business and
  Economics, Mathematics and Science, and
  Information Technology.
• Known for the only in Finland univerisity-level
  sports education, the Finlands oldest and
  biggest IT faculty, physics research
  (nanoscience), psychology research.

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UBIWARE project

• Current project of Industrial Ontologies Group
  (IOG) lead by Vagan Terziyan (Professor in
  Distributed Systems at MIT department).
• IOG now Vagan 2 PhDs 2 PhD students 1 MSc
  student 1 BSc student.

• UBIWARE (2007 - 2010) Smart Semantic Middleware
  for Ubiquitous Computing. Previous project
  SmartResource (2004-2007).
• Run in Agora Center. Funded 80 by Tekes (Finnish
  National Agency for Technology and Innovation)
  and 20 by a set of companies
• Metso (paper industry), ABB and Fingrid (power
  networks), Nokia (telecommunications).
• In SmartResource participated and may still join
  TeliaSonera (telecommunications) and Tietoenator
  (software).

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UBIWARE goal and approach

• Goal Project aims at designing a new generation
  middleware platform (UBIWARE) which will allow
  creation of self-managed complex industrial
  systems consisting of distributed, heterogeneous,
  shared and reusable components of different
  nature.

• Approach utilization of agent and semantic
  technologies
• Every resource of interest is assigned a
  representative - software agent to configure
  and monitor it, and to enable it to participate
  in new business processes.
• Understanding among and coordination of these
  heterogeneous agents is to be achieved through
  semantic data and ontologies.

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Ontological coordination problem

• Two robots meet on Mars. If there is a need for
  cooperation or at least coordination
• Assume both follow FIPA ACL can exchange
  messages, understand INFORM, REQUEST, REJECT,
  etc.
• Assume both follow BDI architecture share
  meaning of I intend, My goal is
• Both designed for Mars hopefully have similar
  domain ontology concepts surface, rock but
  mapping between concept labels already requires
  use of standard semantic technologies (RDF, OWL).

• Assume US robot has a jet pack and thus can
  fly, while EU robot has no idea about
  possibility of moving in third dimension.
• How US robot can communicate its movement
  intentions, so that two can coordinate? A
  semantic approach is needed but standard OWL/RDF
  are not suited for describing active objects or
  even just dynamically changing information.

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Ontologies that need to be shared
To have effective coordination, agents have to
share

• ExtOnt(A) - properties of the external world
• MentOnt(A) - internal mental properties of the
  agent
• BodyOnt(A) - properties of the agent's (physical)
  body
• InOnt(A)
• - properties of the sensory input
• - properties of the communication input
• OutOnt(A)
• - properties of the action output
• - properties of the communication output

• Domain ontology
• E.g. BDI model
• Available sensors and actuators
• Sensing vocabulary
• E.g. FIPAs ACL
• Acting vocabulary
• E.g. FIPAs ACL

Are not treated
Impossible to standardize, cannot explicitly
model because of RDF limitations
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Semantic Agent Programming Language (S-APL)

• Need to be able to ontologically describe not
  only the domain, but the agents themselves.
• Our approach RDF-based (more precisely, Notation
  3) APL.
• In a nutshell
• Lets start treating agents programs as data
• If it is data, lets use the semantic approach
  (advantages are well-known)
• Use Notation 3 (extension of RDF by Tim
  Berners-Lee) to overcome limitations of basic
  RDF.

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S-APL

• It is a hybrid of semantic reasoners like CWM and
  agent programming languages like AgentSpeak,
  AFAPL.
• From APLs point of view S-APL is a language that
  provides all the features (and more) as normal
  APLs, while being RDF based and thus providing
  advantages of semantic data model and reasoning.
• From semantic reasoning point of view S-APL is
  CWM extended with common APL features such as
• Beliefs-Desires-Intentions (BDI) architecture,
  i.e. ability to describe goals and commitments -
  data items presence of which leads to some
  executable behavior, and
• Ability to link to sensors and actuators
  implemented in a procedural language, namely
  Java.

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S-APL Agent Architecture
Behavior Engine
Beliefs storage
Data
Rules
RAB
RAB
RAB
RAB
Blackboard
RAB Reusable Atomic Behavior
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Options for S-APL use

• The developers of a specific application can use
  S-APL in different ways
• Semantic Reasoning. S-APL rules operating on
  S-APL semantic data.
• Semantic Data. RABs (i.e. Java components)
  operating on S-APL semantic data.
• Workflow management. RABs operating on Java
  blackboard objects, with S-APL used only as
  workflow management tool, specifying what RABs
  are engaged and when.
• Any combination of the three options above.

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S-APL Rules

• Conditional commitment
• John loves ?x gt ...
• gt is the shorthand for saplimplies. If the left
  side of gt is truth (beliefs are connected with
  AND), the right side is copied into G. The
  commitment is removed after that (as fulfilled).
  Otherwise, it stays in beliefs and checked again
  and again until becomes true.
• Persistent behavior rule (not removed upon
  execution)
• ... gt ... saplis saplRule
• Conditional action
• ... -gt ... saplelse
• -gt is the shorthand for saplimpliesNow. Such a
  condition will be checked only once and will be
  removed even if the condition was false. Adding
  gbelse block defines what should be added to
  beliefs if the condition was false.

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S-APL Rules (2)

• Exclusive condition
• John Loves ?x .
• saplI sapldoNotBelieve ?x .. ..
• gt ...
• If the object of sapldoNotBelieve matches G, the
  left side of gt is false. If there are several
  sapldoNotBelieve, they are connected with OR
• Commitment with a guard condition
• ... gt ... saplis sapltrue
• saplexistsWhile ...
• If the object of saplexistsWhile is false, its
  subject is removed in this case, the commitment
  is dropped.

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S-APL Action commitments

• Unconditional commitment to an action
• saplI sapldo javaubiware.shared.MessageSe
  nderBehavior
• saplconfiguredAs
• preceiver saplis John .
• pcontent saplis bla bla .
• saplSuccess sapladd John was
  notified
• Special statements to add or remove beliefs the
  subject can be saplStart, saplEnd,
  saplSuccess, and saplFail. The predicate is
  either sapladd or saplremove.
• Sequential plan
• saplI sapldo ... saplconfiguredAs
• ... saplSuccess sapladd saplI sapldo
  ...

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S-APL Querying constructs

• ?man loves ?girl. ?girl age ?age saplAll
  ?girl.
• ?person age ?age. ?age lt 25 saplAll
  ?person.
• ?person age ?age. ?person loves ?x saplis
  saplOptional saplAll ?person.
• John loves ?girl saplor Bill loves
  ?girl saplAll ?girl.
• ?person age ?age saplOrderBy ?age.
• saplOrderByDescending, saplLimit and
  saplOffset are also available. As in SPARQL

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S-APL statements and containers

• After query ?x accordingTo Bill, can do
• ... ?x saplis sapltrue accordingTo John
  gt ...
• ?x saplis sapltrue
• saplI sapladd ?x
• saplI saplremove ?x (used as pattern for
  removal)
• saplI saplerase ?x (removed itself)
• ?x saplhasMember room1 hasTemperature 25
  (new member added)
• Can get ID of a statement with (then, can do most
  of above)
• hasTemperature 25 saplID ?x
  accordingTo Bill
• ?x rdfpredicate hasTemperature accordingTo
  Bill
• ?x saplis sapltrue accordingTo Bill
• ?c accordingTo Bill. ?c saplhasMember ?x
• Can do query like ?x saplis sapltrue
  according to Bill. ?x saplis sapltrue
  accordingTo John gt ...

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UBIWARE topics (work packages)

• Core distributed agent-based platform
• S-APL
• Managing distributed resource data
• Including mechanisms of inter-agent information
  exchange
• Policies in UBIWARE
• E.g. access control security policies
• Configurability and Self-Management
• Including configurability of access to legacy
  (non-semantic) data
• Context-aware filtering and presentation of data
  to humans
• Middleware for peer-to-peer discovery in UBIWARE
• Industrial cases and prototypes

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S-APL as communication content language

• We use S-APL also as inter-agent communication
  content language
• One agent sends a query as saplI saplwant
  saplYou saplanswer ..query.. , the other
  answers with the matching part of its beliefs
• One agent requests another agent to perform an
  action plan by sending the plan with saplI
  saplwant saplYou sapldo ..plan..
• Such interaction is easily programmed with S-APL
  itself. The platform includes a set of standard
  models Listener, Believer, Informer and Follower.

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Non-semantic data transformation

• First, the original data from the resource is
  transformed into an S-APL representation based on
  the ontology of the data model of the original
  data (e.g. table-row-column ontology for
  tables). This transformation is performed by a
  Reusable Atomic Behavior (RAB).
• Second, the data from the data-model-ontology
  S-APL is transformed into the final
  domain-ontology S-APL using S-APL own means, i.e.
  rules.
• The platform includes a set of standard RABs such
  as TextTableReader, SQLReader, XMLReader,
  XMLWriter, etc.

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Example application architecture
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Policies in S-APL (example)

• ActionCommitment saplis
• saplI sapldo ?behavior saplconfiguredAs
  ?parameters
• .
• Action rdfssubClassOf ActionCommitment
• owlRestriction saplI owlsameAs ?subject.
  saplI owlsameAs ?object.
• Print rdfssubClassOf Action
• owlRestriction ?behavior Print.
  ?parameters saplhasMember pprint saplis
  ?print.
• Swear rdfssubClassOf Print
• owlRestriction BadWord saplis ?word.
  sapltrue "contains(?print,?word)".
• Then, can specify a policy as e.g.
• fgEmployee Swear fgEmployee saplis
  sbacProhibition
• The platform includes the standard S-APL model
  SBACReasoner that implements such access control
  policies using the S-APL MetaRule mechanism

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Possible MIT JYU cooperation

• Further development of S-APL or alike,
  co-authoring a paper
• Formal semantics of S-APL
• Implementation of policies for Semantic Web using
  S-APL
• ?
• A visiting researcher from JYU to MIT
• UBIWARE has plans and the budget for few 3-month
  stays in US universities
• Anytime until the end of UBIWARE, i.e. April 2010