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

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 e-mails: tiihonen@it.jyu.fi vagan@it.jyu.fi phone: +358 14 260 2741 Sergiy Nikitin Yaroslav Tsaruk Artem Katasonov URL: http://www.cs.jyu.fi/ai/OntoGroup

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

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 )

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 2006 17 Oct 2006 Nov 2006 20 Dec 2006 Working seminars at companies: ≈ 4-8 September

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

Going beyond WWW GUN Concept

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

GUN Concept GUN – Global Understanding eNvironment GUN Customer

GENI Initiative by NSF: Future Internet http://www.nsf.gov/cise/geni/

General Networking Framework Proactive World Concept

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

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

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

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

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

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

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

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

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

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

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

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

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

P2P Smart Ontologies Network

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

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

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

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

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

Axioms of General Networking Framework 1 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

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

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

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

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

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

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

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

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

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)

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

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

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

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

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

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

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

SmartResource 2006 Scientific Impact

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

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

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

SmartResource 2006 Publications

SmartResource Publications (2006) Refereed Journal Papers : Ermolayev V., Terziyan V., Kaykova O., SW @ 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 1744-2621, 11 pp. (to appear).

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. 356-366. 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).

Status of SmartResource-Related Publications (2003-2006) Books or Book chapters : 4 Refereed Journal Papers : 12 Refereed Conference Papers : 17 Reports : 11 Ms. Theses: 8 -------------------------------------------------------------------------------- Total: ≈ 50

SmartResource 2006 Doctoral studies

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