1. SmartResource Project: 3-rd year (2006)
Status Report (16 May 2006)
“Expert”
Resource Agent
“Device”
Resource Agent
Resource Agent
“Service” PI
University of Jyväskylä
Industrial Ontologies Group

2. Project Team: Industrial Ontologies Group
University of Jyväskylä
Kharkov National University of Radioelectronics
Researchers
Vagan Terziyan
Oleksiy Khriyenko
Olena Kaykova
Oleksiy Loboda
Contact Person:
Timo Tiihonen s:
phone:
Sergiy Nikitin
Yaroslav Tsaruk
Artem Katasonov
URL:

3. Contents Status of the project deliverable D3
Deliverable 3.1: General Networking Framework (GNF)
Publications
Doctoral Studies

4. Deliverables D3.1: D3.2: D3.3: ------: Checkpoint_1 - (16 May 2006)
General Networking Framework (GNF)
( completed)
Checkpoint_2 - (17 October 2006)
D3.2:
SmartResource Platform in Distributed Power Networks Maintenance (Design of the SmartResource platform for the domain of distributed energy networks maintenance. Pilot ontology of the distribute power network and agent-driven platform to manage the distributed communications)
D3.3:
SmartResource Platform for Web Service Interactions’ Semantic Log (Design of SmartResource Platform for the scenario of interaction among SOAP-based Web Services. Pilot ontology, basic business intelligence tools )
(in progress )
Checkpoint_3 - (20 December 2006)
------:
Summarizing Research, Road-mapping, Business Analysis, Reporting
(in progress )

5. Schedule GNF research Doctoral Studies
1 checkpoint
2 checkpoint
3 checkpoint
GNF research
SmartResource Platform in Distributed Power Networks Maintenance
SmartResource Platform for Web Service Interactions’ Semantic Log
Summarizing Research, Road-mapping, Business Analysis, Reporting
Doctoral Studies
1 Feb 2006
Mar 2006
Apr 2006
16 May 2006
Jun 2006
July 2006
Aug 2006
Sep
17 Oct 2006
Nov
20 Dec 2006
Working seminars at companies: ≈ 4-8 September

6. General Networking Framework (GNF)
SmartResource 2006
Deliverable 3.1
General Networking Framework (GNF)

7. Going beyond WWW
GUN Concept

8. Semantic Web: which resources to annotate ?
This is just a small part of Semantic Web concern !!!
Technological and business processes
External world resources
Web resources / services / DBs / etc.
Semantic annotation
Shared ontology
Multimedia resources
Web users (profiles, preferences)
Smart machines, devices, homes, etc.
Web agents / applications / software components
Web access devices

9. GUN Concept
GUN – Global Understanding eNvironment
GUN
Customer

10. GENI Initiative by NSF: Future Internet

11. General Networking Framework
Proactive World Concept

12. Industrial World Resources
Industrial World consists on a variety of resources: simple and complex products, machines, tools, devices and their components, Web-services, human workers and customers, processes, software and information systems, standards, markets, domain ontologies, etc. Thus the Industrial World contains all type of entities (physical, biological, digital, etc.)
Controls
Tools
Devices/Machines
/Products ...
Humans
Industrial World
Customer
Markets
Standards
Designs
Data/Documents
Domain Ontologies
Software
Processes
Organizations
Services

13. GUN Resources Histories Agents Rules Standards Tools&Platforms
World of GUN consists on a variety of resources: agents for managing IW or GUN resources, resource histories semantically enriched with metadata, GUN ontologies, adapters for connecting with IW resources, tools, platforms, standards, executable software components, engines and rules employed by agents, multi-agent commitments and conventions, internal and global standards.
Histories
Agents
Rules
Standards
Resource history data
Rules
Global Understanding Environment
Rules
Resource history data
Behaviour
Resource history metadata
Tools&Platforms
GAF PI
GUN Ontologies
GUN Engines
Commitments
and conventions
Adapters
Executable
software components

14. GUN – Intelligent Control
GUN is meant for intelligent control over the Industrial World.
Monitoring World
Industrial World (IW)
Global Understanding Environment (GUN)
Controlling World
Reasoning about World

15. Proactive Industrial World Resources
To each entity (resource) of the Industrial World (IW), the Global Understanding Environment (GUN) provides a Resource Agent, which is assumed to “take care” of the resource and to implement proactivity of the resource behavior. Each of IW can be GUN-supported if there is an opportunity to connect resource to GUN.
Resource Agent
Industrial Resource
Industrial World (IW)
Global Understanding Environment (GUN)
This is just a relation. Direct contact will be performed via sensors, effectors and adapters

16. Proactive Industrial World Resources (2)
Heterogeneous Industrial World resources due to being represented by agents become homogeneous in GUN Environment. IW
Device
GUN
Domain Ontology
Service
Human

17. Proactive GUN Resources (1)
Also to each entity (resource) of the Global Understanding Environment (GUN), except GUN agents, GUN provides a Resource Agent, which is assumed to “take care” of the resource and to implement proactivity of the resource behavior.
Resource Agent
GUN Resource
Global Understanding Environment (GUN)
API connection

18. Proactive GUN Resources (2)
Analogously, heterogeneous GUN resources, due to being represented by agents, become homogeneous in GUN environment and naturally interoperable with IW resources.
GUN
Executable
software component
Adapter
GUN Ontology
Resource history
History

19. Contacts of an [Industrial] Resource Agent
Each GUN agent (responsible for an industrial resource) or “Resource Agent” communicates only with agents (either with other resource agents or with “GUN resource agents”) and has no direct contact with any other software or other entities
GUN
Resource Agent
Agent
GUN Resource

20. Contacts of a GUN Resource Agent
Each GUN agent (responsible for a GUN resource) or “GUN Resource Agent” communicates not only with agents (either with other resource agents or with “GUN resource agents”) but also with appropriate GUN resource directly
GUN
GUN Resource Agent
GUN Resource

21. Architecture of a GUN platform
Resource Agent
Agents
GUN Resource
Agent Layer
Component Layer
Resource history
Commit-ments PI
Behavior
Data Layer
GUN Ontology

22. Reusable atomic behaviours Pool of Atomic Behaviours
Flexibility of configuration
SmartResource Agent
.class
Assign a role
Script-Role
Live
activity
.class
activity
Activity
Activity
Activity
Activity
Beliefs storage
Reusable atomic behaviours
RequestSender.class
RequestReceiver.class
DataSender.class
DataReceiver.class
Alerter.class
ExternalAppStarter.class
OntologyLookup.class
The present prototype is implemented by using these 7 atomic behaviours only
Pool of Atomic Behaviours
Ontology of the Roles

23. Connection between an industrial resource and its GUN agent
Actual contact between an industrial resource and its GUN agent is performed via special category of GUN agents (adapters), which are connected to the resource through sensors (to get information) and through effectors (to send control command). Adapters needed to manage the heterogeneity of resources
Resource Agent
Industrial Resource
Sensor Adapter
GUN
Agent
Sensor
Sensor Adapter IW
Effector Adapter
Effector /
Actuator
Agent
Effector Adapter

24. Domain Ontology as GUN-supported Industrial Resource
Domain Ontology as part of external to GUN Industrial World is also considered as a resource and thus has a “Domain Ontology Resource Agent” from GUN. Adapters for that resource are connected also to GUN Ontology via appropriate GUN Ontology Agent.
Domain Ontology
Resource Agent
GUN IW
Sensor
Sensor Adapter
Domain Ontology
GUN Ontology
GUN Ontology
Agent
Effector Adapter
Effector

25. P2P Smart Ontologies Network

26. Resource Maintenance Lifecycle
Condition Monitoring
States
Symptoms
Predictive Measurement
Predictive Monitoring
Measurement
Data Warehousing
56°C
Conditions Warehousing
History
Resource
(device, expert, service,…)
Diagnostics
Diagnoses Warehousing
Predictive Diagnostics
Plan Warehousing
Predictive Maintenance
Maintenance
Maintenance Planning
Maintenance Plan
Diagnoses

27. GUN Platform in a Nutshell
Each agent can keep needed adapters, histories, behavior sets, software components and other GUN resources on the own GUN agent-platform. On such platform resource agent can communicate with other GUN resources agents locally.
GUN
Resource Agent
Industrial Resource
Resource Adapter IW
Resource history metadata
Behavior
GUN Platform
Ontology

28. Distributed Agent-Supported History Storages
Shared ontology guarantees interoperability and understanding among resource agents. Industrial world will be represented in GUN environment with distributed history database, which can be queried by agents and is the subject of agent communication.
Resource Agent
Resource Adapter
History data
GUN Platform
GUN
Resource Agent
Resource Adapter
History data
GUN Platform
Domain Ontology
Resource Agent
Resource Adapter
History data
GUN Platform

29. Part_of product hierarchy in ontology…
… results to hierarchical MAS
isPartOf

30. The “Main Boss” among GUN agents
GUN’s Top Agent is the one, which resource, to be taken care of, is the Industrial World as whole. Such agent will be on the top oh the hierarchy of resource agents
Monitoring World
Industrial World (IW)
Global Understanding Environment (GUN)
Controlling World
Reasoning about World

31. Axioms of General Networking Framework1
Axiom 1: Each resource in dynamic Industrial World is a process and each process in this world is a resource.
1.1
1.2
1.1.1
1.1.2
1.1.3
1.2.1
1.2.2
1.2.3
Axiom 2: Hierarchy of subordination among resource agents in GUN corresponds to the “part-of” hierarchy of the Industrial World resources. 1
1.1
1.2
1.1.1
1.1.2
1.1.3
1.2.1
1.2.2
1.2.3

32. Multiple Commitments and Cloning
Each industrial resource can theoretically be involved to several processes, appropriate commitments and activities, which can be either supplementary or contradictory. This means that the resource is part of several more complex resources and its role within each of the resource might be different. Modeling such resources with GUN can be provided by appropriate resource agent, which can make clones of itself and distribute all necessary roles among them.
Team Member
Concursant
GUN IW
Clone
Resource Agent
Industrial Resource
Wife
Manager

33. Locally Valid Rules Team Resource Individual Resource
Each industrial resource, which joins some commitment, will behave according to restrictions the rules of that commitment require. The more commitments individual resource takes, the more restriction will be put on its behavior.
Rule 4
Rule 8
Rule 5
Team Resource
Rule 6
contradiction
Rule 1
Individual Resource
Rule 7
Rule 2
Rule 3

34. General Networking Framework
Resource Process Integration Description Framework
(RP/IDF)

35. Nature of RSCDF – RGBDF – RPIDF
Industrial Resource
Resource as a subject of observation and monitoring
Resource State/Condition Description Framework SC
Resource as a proactive component in business processes II
Industrial Resource
"Bosses" - business processes …
Resource Goal/Behavior Description Framework
"Instructions" GB
Rules
Resource Agent
Resource Agent
Resource as a business process “manager”
III
Industrial Resource
Metarules PI
Resource Process/Integration Description Framework
"Instructions" …
Resource components

36. Pi Rule Statement (1) true_if false_if object subject predicate
RDF container
RDF container
false_in_context
true_in_context Pi
true_if
false_if
RDF container
RDF container
object
subject
RGBDF rule statement:
Ra: IF(…) then Pi
predicate
RGBDF rule statement:
Rb: IF(…) then

37. P2 Rule Statement (2) P1 P4 P3 P5 false_if true_if X Y R1 R2
RDF container
RDF container P1 P4 P3 P5
false_if
true_if X Y
false_in_context
true_in_context R1 P2 R2

38. Two separate sub-processes
Coordination Needed !
Coordination Needed !
Initial Environment State:
Agent1
Agent2
Goal (Environment State): 3 1 2 6 4 7 5

39. R2 Meta-Rule Statement R1 P4 P3 P5 false_if true_if X Y R’1 R’2
RDF container
RDF container R1 P4 P3 P5
false_if
true_if X Y
false_in_context
true_in_context
R’1 R2
R’2

40. Process coordination with metarules
Upper-process Agent
Upper process (i.e. agent-driven Smart Resource) 6 1 4 6
Inherited individual goals
Inherited individual goals 4 7 1 1 3 3 5 2 5 2
Goal (Environment State):
1.2
1.1
Group goal
Initial Environment State:
Agent1
Agent2
Goal (Environment State): 6 1 4 7 3 5 2
Corrected individual behavior with RPIDF constraints (metarules)

41. Illustrating Process Coordination
g1, g2, g3
Model 1
Model 2
General Model
G and g – goals,
Pg – priority of the goal g
- behavior plan
- behavior planer G
G,( g1, g2 )
Pg1 , Pg2 , Pg3

42. Auction as complex resource (1)
Doctor 1
Behavior
Outsource service with role ”Doctor”
Doctor 2
Doctor 3
Doctors
Agent Registry
Resource Agent

43. Auction as complex resource (2)
Doctor 1
QoS1
Auction Agent
Doctor 2
QoS2
Doctor 3
Resource Agent
QoS3

44. Auction as complex resource (3)
Behavior
Outsource service ”Doctor 3”
Doctor 3
QoS3
e.g. minimal price or maximal accuracy or fastest response time
Resource Agent

45. Resource Integration scenario as a complex resource (1)
Doctor 1
Behavior
Outsource service with role ”Doctor”
Doctor 2
Doctor 3
Doctors
Agent Registry
Resource Agent

46. Resource Integration scenario as a complex resource (2)
Doctor 1
Trust1
Trust2
Trust3
Data
Integration
Scenario
Agent
Diagnosis1
Doctor 2
Data
Diagnosis2
Data
Doctor 3
Diagnosis3
Resource Agent

47. Resource Integration scenario as a complex resource (3)
Integrated Diagnosis = F( )
Trust1
Trust2
Trust3
Diagnosis1
Diagnosis2
Diagnosis3 =
Integration
Scenario
Agent
Resource Agent
Doctor 3
Doctor 1
Doctor 2

48. SmartResource 2006
Scientific Impact

49. Scientific Impact of the GUN and SmartResource
Semantic Web research
SmartResource research
Resources
Resources
Semantic …
Discovery
Selection
Composition
Orchestration
Integration
Invocation
Condition Monitoring
Coordination
Communication
Negotiation
Context Awareness
Diagnostics
Forecasting
Control
Maintenance
Learning
GUN and SmartResource vision generalized and expanded the Semantic Web vision and the roadmap in general and Semantic web services in particular
Web Services
Semantic …
Discovery
Selection
Composition
Orchestration
Integration
Invocation
Execution Monitoring
Coordination
Communication
Negotiation
Context Awareness
The very top level of Semantic Web community research today
Web Services

50. Semantic Web Killer Application
Integration?
Semantic Web Services?
Ontologies and P2P ?
RDF-based Search Engine ?
Organizational Knowledge Sharing ?
The Semantic Web itself ?
Not at all ?
Anything else?
Our vision: Global Understanding Environment (GUN) is a Semantic Web Killer Application !!!
Industrial Ontologies Group

51. RDF Evolution Proactivity Dynamics Coordination PI GUN platform 2 3 1
Resource description includes instructions for the resource agent (relevant to the resource current role in a business process in a given context), which will be the basis for its proactive behavior
Proactivity
Resource description includes different states at different time and different conditions at different context 3
Resource description includes coordination constraints that the resource agent requires from the agents, which are taking care of the resource parts 1
Dynamics
Coordination
History
GUN platform PI PI

52. SmartResource 2006
Publications

53. SmartResource Publications (2006)
Refereed Journal Papers :
Ermolayev V., Terziyan V., Kaykova O., UKRAINE, In: M. Lytras (Ed.), Semantic Web Factbook 2005, AIS SIGSEMIS, 2006, 9 pp. (Book chapter, submitted 14 March 2006).
Terziyan V., Challenges of the "Global Understanding Environment" based on Agent Mobility, In: V. Sugumaran (ed.), Advanced Topics in Intelligent Information Technologies, Idea Group, 30 pp. (Book chapter, submitted 15 February 2006).
Kaykova O., Khriyenko O., Kovtun D., Naumenko A., Terziyan V., Zharko A., Challenges of General Adaptation Framework for Industrial Semantic Web, In: Amit Sheth and Miltiadis Lytras (eds.), Advanced Topics in Semantic Web, Idea Group, Vol. 1, 33 pp. (to appear).
Khriyenko O., Terziyan V., A Framework for Context-Sensitive Metadata Description, In: International Journal of Metadata, Semantics and Ontologies, ISSN , 11 pp. (to appear).

54. SmartResource Publications (2006)
Refereed Conference Papers :
Terziyan V., Bayesian Metanetwork for Context-Sensitive Feature Relevance, In: G. Antoniou et al. (eds.), Proceedings of the 4-th Hellenic Conference on Artificial Intelligence (SETN 2006), Lecture Notes in Artificial Intelligence, Vol. 3955, 2006, pp
Khriyenko O., Proactivity Layer of the Smart Resource in Semantic Web, In: 17th International Conference on Database and Expert Systems Applications - DEXA '06 , September 4-8, 2006, Andrzej Frycz Modrzewski Cracow College, Krakow, Poland, Springer, LNCS, 10 pp. (submitted 7 March, 2006).

55. Status of SmartResource-Related Publications (2003-2006)
Books or Book chapters :
Refereed Journal Papers :
Refereed Conference Papers :
Reports :
Ms. Theses:
Total: ≈ 50

56. SmartResource 2006
Doctoral studies

57. Artem Katasonov – PhD with Honor.
Thesis title: “Dependability Aspects in the Development and Provision of Location-Based Services”
Oleksiy Khriyenko – studies completed, thesis expected at the end of 2006
Sergiy Nikitin – ≈ 70 %
Andriy Zharko – studies completed, currently works in company, thesis expected during 2007
Anton Naumenko – ≈ 80 %, currently work on a grant for PhD studies, thesis expected during 2007
Yaroslav Tsaruk – ≈ 30 %, PhD studies performed in Vaasa University