1. Agent-Driven Self-Management “Device” “Expert” “Service” Resource Agent

2. IBM: Autonomic Computing (1) §The computing domain is now a vast and diverse matrix of complex software, hardware and services. By 2020 we expect billions of devices and trillions of software processes, with a lot of data. And it's not just a matter of numbers. It's the complexity of these systems and the way they work together that is creating a shortage of skilled IT workers to manage all of the systems. It's a problem that's not going away, but will grow exponentially, just as our dependence on technology has. §Autonomic Computing is about how to enable computing systems to operate in a fully autonomous manner. No administration, just simple high-level policy statements. §Autonomic Computing is an approach to self-managed computing systems with a minimum of human interference. The term derives from the body's autonomic nervous system, which controls key functions without conscious awareness or involvement.

3. IBM: Autonomic Computing (2)

4. “Self-(re)configuration” example Professor in Software Engineering; Head of Artificial Intelligence Dep.; Head of “MetaIntelligence” Research Lab. … in Kharkov National University of Radioelectronics (Ukraine) Professor in Distributed Systems; Head of Industrial Ontologies Group … … in the Department of Mathematical Information Technology, University of Jyvaskyla (Finland) “Self-reconfigurable“ means that the system is capable of utilizing its own system of control to change its overall structural shape.

5. Self-Configurable Systems: Invented by Hollywood?

6. Why self (re)configuration? Versatility: Self-reconfigurable systems are potentially more adaptive than conventional systems. The ability to reconfigure allows a system to disassemble and reassemble components to form new morphologies that are better suited for new tasks, Robustness: Since system components are interchangeable (within a system and between different systems), self-reconfigurable systems can also replace faulty parts autonomously, leading to self-repair. Low Cost: Self-reconfigurable systems can potentially lower overall cost by making many copies of one (or relatively few) type of modules so economies of scale and mass production come into play. Also, a range of a system can be made from one set of modules, saving costs through reuse and generality of the system. One source of inspiration for the development of these systems comes from biological systems that self-construct out of a relatively small repertoire of lower- level building blocks (cells or amino acids, depending on the scale of interest). This architecture underlies the ability of biological systems to physically adapt, grow, heal, and even self-replicate - capabilities that would be desirable in many engineered systems.

7. Dynamic reconfigurability vs. self-configurability Dynamic reconfigurability is reconfigurability at run- time, but not necessarily based on system self-awareness and intentions and not necessarily supported by any special algorithms. Self-configurability is going one step beyond – the system is expected to autonomously and deliberately perform the reconfiguration.

8. Composition (content: components of data and capabilities), Structure (partonomy, business logic and interaction applied to content) and Parameters (features of the content and structure) What is configuration?

9. What is configuration of a self-configurable system? What is configuration of a self-configurable system? Everything is configurable !

10. What is an Intelligent Agent? Self-Configurability ! ENVIRONMENT Behavior Events Self-Configuration Self-Awareness

11. “Read Only” world “Write Only” world “Read and Write” world ωiωi φiφi Agent i alone may “play” a function: φ i = f i (ω i ), Where f i is individual behavior Agent i fifi “Behavior” function Agent and the World (W = Internal + External Environments)

12. Indirect Collaboration (via communication) Communication Agent i and Agent k together may “play” 6 types of functions: φ i = f i (ω i ); φ k = f k (ω k ); φ i = f ki (ω k ); φ k = f ik (ω i ); F 1 ()F 2 () φ i = F 1 (f i (ω i ), f ki (ω k )); φ k = F 2 (f k (ω k ), f ik (ω i )), F - where F - collaborative behavior ωiωi φiφi ωkωk φkφk fifi fkfk Need ontological mapping between Ont(Agent i ) and Ont(Agent k )

13. Indirect Control (via environment) Agent i may “play” 2 types of functions knowing rules of the environment: R φ i = f ( ω i ); φ j = R (f (ω i )), R where R – function of environmental rules ωiωi φiφi ωjωj φjφj Environmental Rules

14. Summary: Why Agents ? Growing complexity of computer systems and networks Distributed nature of systems (data, software, users, etc.) Ubiquitous computing, “Internet of Things” scalability challenges Need for self-manageability of a complex system Need for new software development paradigms in designing distributed systems Agent-based approach meets the above challenges … And finally: Agents are excellent tool for self-configuration !!!

15. Web of Configurations … is the Web of “partonomy” (a classification based on part-of relation; not the same as taxonomy, which is a classification based on similarities). Configuration of an object (parts and their relationships) together with all policies applied to these parts fully describes the object from inside. Facilitates Configuration- to-Configuration interaction isPartOf

16. Proactive Configuration Part_of product hierarchy in the ontology results to hierarchical MAS

17. Configuration of objects vs processes Axiom 1: Each resource in dynamic Industrial World is a process and each process in this world is a resource. 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 1.1.2 1.1.1 1.1.31.2.1 1.2.2 1.2.3 1 1.1 1.2 1.1.2 1.1.1 1.1.3 1.2.2 1.2.1 1.2.3

18. Resource Configuration Example hasColor (ID3, “Muticolor”) hasBehind (ID3, ID4) Locomotive (ID3) hasPart (ID1,ID3) hasPart (ID1, ID4) Train (ID1) hasPart (ID1, ID5) hasDestinationTo (ID1, “Paris”) hasDestinationFrom (ID1, “Amsterdam”) hasConfiguration (ID3,ID6) hasConfiguration (ID4, ID7) hasConfiguratioin (ID5, ID8) hasConfiguration (ID1,ID2) hasColor (ID4, “Beige”) hasBehind (ID4, ID5) Car (ID4) hasColor (ID5, “Red”) hasAhead (ID5, ID4) Car (ID5) hasAhead (ID4, ID3)

19. Configuration Components hasPart (ID1,ID3) hasPart (ID1, ID4) Train (ID1) hasPart (ID1, ID5) hasDestinationTo (ID1, “Paris”) hasDestinationFrom (ID1, “Amsterdam”) hasConfiguration (ID3,ID6) hasConfiguration (ID4, ID7) hasConfiguratioin (ID5, ID8) hasConfiguration (ID1,ID2) Object of configuration Content of configuration Class of the resource Structure of the resource Parameters’ values of the resource Configuration of structural components Content of configuration

20. ReconfigurationReconfiguration hasPart (ID1,ID3) hasPart (ID1, ID4) Train (ID1) hasPart (ID1, ID5) hasDestinationTo (ID1, “Paris”) hasDestinationFrom (ID1, “Amsterdam”) hasConfiguration (ID3,ID6) hasConfiguration (ID4, ID7) hasConfiguratioin (ID5, ID8) hasConfiguration (ID1,ID2) hasPart (ID1,ID3) hasPart (ID1, ID4) Train (ID1) hasPart (ID1, ID5) hasDestinationTo (ID1, “Paris”) hasDestinationFrom (ID1, “Amsterdam”) hasConfiguration (ID3,ID9) hasConfiguration (ID4, ID10) hasConfiguratioin (ID5, ID11) hasConfiguration (ID1,ID8)

21. Reconfiguration behavior (option 1: reordering) hasPart (ID1,ID3) hasPart (ID1, ID4) Train (ID1) hasPart (ID1, ID5) hasDestinationTo (ID1, “Paris”) hasDestinationFrom (ID1, “Amsterdam”) hasConfiguration (ID3,ID9) hasConfiguration (ID4, ID10) hasConfiguratioin (ID5, ID11) hasConfiguration (ID1,ID8) hasColor (ID3, “Muticolor”) hasBehind (ID3, ID5) Locomotive (ID3) hasColor (ID4, “Beige”) hasBehind (ID5, ID4) Car (ID4) hasColor (ID5, “Red”) hasAhead (ID5, ID3) Car (ID5) hasAhead (ID4, ID5)

22. Reconfiguration behavior (option 2: recolor) hasPart (ID1,ID3) hasPart (ID1, ID4) Train (ID1) hasPart (ID1, ID5) hasDestinationTo (ID1, “Paris”) hasDestinationFrom (ID1, “Amsterdam”) hasConfiguration (ID3,ID6) hasConfiguration (ID4, ID13) hasConfiguratioin (ID5, ID14) hasConfiguration (ID1,ID12) hasColor (ID3, “Muticolor”) hasBehind (ID3, ID4) Locomotive (ID3) hasColor (ID5, “Beige”) hasBehind (ID4, ID5) Car (ID5) hasColor (ID4, “Red”) hasAhead (ID4, ID3) Car (ID4) hasAhead (ID5, ID4)

23. UBIWARE Abstract Architecture

24. Current UBIWARE Agent Architecture S-APL S-APL – Semantic Agent Programming Language (RDF-based) S-APL: http://www.cs.jyu.fi/ai/OntoGroup/ubidoc/http://www.cs.jyu.fi/ai/OntoGroup/ubidoc/ S-APL S-APL – is a hybrid of semantics (metadata / ontologies/ rules) specification languages, semantic reasoners, and agent programming languages. It integrates the semantic description of domain resources with the semantic prescription of the agents' behaviors S-APL

25. UBIWARE 3.0 (2009-2010) platform (August 2010) UBIWARE 3.0 is a platform for creating and executing configurable distributed systems based on generalized and reusable business scenarios, which heterogeneous components (actors) are not predefined but can be selected, replaced and configured in runtime.

26. Key Components of UBIWARE Scientific Impact 3. Language 1. UBIWARE: Approach and Architecture 2. Engine 4. Ontonuts

27. Environment HardBody SoftBody SoftMind HardMind HardSoul SoftSoul UbiDubi: UBIWARE-driven-UBIWARE (i.e. UBIWARE architectural components are also agent-driven) RAB RAB – Reusable Atomic Behavior RBE RBE – Reusable Behavior Engine RAB RABRABRAB RBE RBERBERBE Beliefs (facts, rules, policies, plans, collaboration protocols) Shared Beliefs Shared RABs Shared RBEs Shared Meta-Beliefs Meta-Beliefs (contexts) “Life” Behavior Configuration (GENOME) Shared Hardware “Visible” to other agents through observation Ontobility Ontobility is self- contained, self-described, semantically marked-up proactive agent capability (agent-driven ontonut), which can be “seen”, discovered, exchanged, composed and “executed” (internally or remotely) across the agent platform in a task-driven way and which can perform social utility-based behavior Genome Genome is part of semantically marked-up agent configuration settings, which can serve as a tool for agent evolution: inheritance crossover and mutation May be an agent

28. Consequence of Self-Management: "Everything-as-a-User"

29. Brief Summary: My view to the future of doing internet-assisted business is based on the assumption that quite soon we (humans) will not be the only ones who will use (decide why and what, order and pay for) the services provided through the Web. The ongoing trend on bridging heterogeneous Webs (Web of Humans, Web of Services, Web of Things, Web of Knowledge, etc.) has resulted to the following well-known slogan: EaaS - "Everything-as-a-Service". However, why not to say wider: EaaS4E - "Everything-as-a-Service-for-Everything", which also implies: EaaU - "Everything-as-a-User"? I suggest to discuss this. It is really difficult to imagine, how many different and interesting challenges should be addressed to make this vision happen! I will try to point out some of them. To make the discussion more concrete, I would suggest to consider 3 groups of services (before - traditionally human-centric): 1. Education for Everything; 2. Wellness and Healthcare for Everything; 3. Financial Services for Everything. Lets imagine that actual service consumer is a device or machine, software component or software system, mathematical abstraction, knowledge or intelligence,... and try to discuss and figure out together, why and how all of these may proactively discover and utilize various services through the Web. And finally the most interesting one: how we (humans) will benefit out of it? "Everything-as-a-User""Everything-as-a-User"

30. Services as well as products can be consumed not only by humans

31. Educational Services for Everything?

32. Wellness and Healthcare Services for Everything ?

33. Financial Services for Everything ?

34. Urban Services for Everything ?

35. Have we realized already? We are not only users of the agents any more... We are “Service-Providers” as well Are we ready for that?