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Molecules of Knowledge: Self-Organisation in Knowledge-Intensive Environments

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Molecules of Knowledge: Self-Organisation in Knowledge-Intensive Environments

  1. 1. Molecules Of Knowledge:Self-Organisation in Knowledge-Intensive EnvironmentsStefano Mariani, Andrea Omicini{s.mariani, andrea.omicini}@unibo.itDipartimento di Informatica: Scienza e Ingegneria (DISI)Alma Mater Studiorum—Universit`a di BolognaLaboratory of Systems and Applications 2012/2013Master Degree in Computer Science EngineeringMariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 1 / 46
  2. 2. Outline1 MoK Genesis2 The Molecules Of Knowledge ModelInformal MoKFormal MoK3 A MoK InfrastructureThe TuCSoN MiddlewareMapping MoK over TuCSoN4 A Case Study: MoK-News5 Conclusion & Future WorksMariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 2 / 46
  3. 3. MoK GenesisOutline1 MoK Genesis2 The Molecules Of Knowledge ModelInformal MoKFormal MoK3 A MoK InfrastructureThe TuCSoN MiddlewareMapping MoK over TuCSoN4 A Case Study: MoK-News5 Conclusion & Future WorksMariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 3 / 46
  4. 4. MoK GenesisWhere It All Started IFrom my own Master Degree Thesis introduction (roughly):“Information specialists are facing new and critical challenges in theirknowledge production process: the increasing amount of information tomine, the pace at which it’s made available and the heterogeneity of itsstructure are just a few to mention.[. . . ]By developing models, technologies, and tools to explore the newlandscape of information, computer scientists can help people todiscover, manage, and publish information at lower costs.”What can we learn?Mariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 4 / 46
  5. 5. MoK GenesisWhere It All Started IIA lot of software systems, nowadays, are in some way socio-technicalknowledge-intensive environments, that is systems in which:a huge amount of information has to be handled(human) users’ behaviour deeply affects the system own behaviourThe challengeAs software engineers, we should build systems capable of dealing withsuch issues, which could mean:able to self-organise information such as the most useful survives and“grows” whereas the useless one disappearsable to self-organise information such as it spontaneously flows towardmore interested users whereas running away from less interested onesMariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 5 / 46
  6. 6. MoK GenesisWhat I Observed IAgain, quotes from myself:“People reach the knowledge they need from different sources ofinformation. Such sources could be either external or internal to theirworking sw system.[. . . ]Whichever is the nature of a source of knowledge, hopefully either (i) itis already structured or (ii) there exists a proper sw entity able to doso.”Ok. . . so?Mariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 6 / 46
  7. 7. MoK GenesisWhat I Observed IIUnfortunately, knowledge people needs to achieve their goals is oftenmessed up with information they don’t care much about. Worse,information can be represented according to different formats—e.g. XML,JSON, OWL, HTML, . . .How to solve?We really need many many things. . .efficient natural language processing techniques to structure otherwiseunstructured informationtext mining to summarize and classify informationreasoning engines to correlate knowledge for which some semanticalrelationship existsuser profiling methods to distinguish useful information from useless onelast but not least, something to put all this stuff together!Mariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 7 / 46
  8. 8. MoK GenesisWhat I Envisioned I“These sources could be reified within the sw system as “seeds”,continuously and autonomously producing “atoms of knowledge”,which have to be seen as autonomous “living” pieces of knowledge.[. . . ]Such injection should not be a “one-shot” operation, rather it shouldbe continuous in time, according to an injection rate which should bedynamic, thus changing according to the system’s state and users’expected behaviour.[. . . ]Furthermore, every single injection should not produce a single atom,but a (dynamically varying) number of identical copies of an atom,let’s say, its “concentration”—as in chemistry.”For God’s sake, why??Mariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 8 / 46
  9. 9. MoK GenesisWhat I Envisioned IIAdaptive and self-organising systems seem the only possible answer whenthe scale of the problem is too big—too much data availableunpredictability too high—“what the hell the user is doing??”global control unrealistic—anyone has its own smartphonedeterministic solutions simply won’t work [Omicini and Viroli, 2011]—lookat natural systems. . .MoK purposeThe goal of the Molecules Of Knowledge model is that of building aself-* software system able to deal with all the above issues by drawinginspiration from the many natural systems studied so far—e.g. physicalsystems, social systems, biochemical systems, . . .Mariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 9 / 46
  10. 10. The Molecules Of Knowledge ModelOutline1 MoK Genesis2 The Molecules Of Knowledge ModelInformal MoKFormal MoK3 A MoK InfrastructureThe TuCSoN MiddlewareMapping MoK over TuCSoN4 A Case Study: MoK-News5 Conclusion & Future WorksMariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 10 / 46
  11. 11. The Molecules Of Knowledge Model Informal MoKOutline1 MoK Genesis2 The Molecules Of Knowledge ModelInformal MoKFormal MoK3 A MoK InfrastructureThe TuCSoN MiddlewareMapping MoK over TuCSoN4 A Case Study: MoK-News5 Conclusion & Future WorksMariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 11 / 46
  12. 12. The Molecules Of Knowledge Model Informal MoKWhat’s MoK?Molecules Of Knowledge (MoK)MoK is a biochemically-inspired coordination model promotingself-organisation of knowledge toward the idea of self-organisingworkspaces [Omicini, 2011]:knowledge sources produce atoms of knowledge in biochemicalcompartments, which then may diffuse and/or aggregate in molecules bymeans of biochemical reactions, acting locally within and between suchspacesknowledge prosumers workspaces are mapped into such compartments,which reify information-oriented user actions to drive atoms and moleculesaggregation and diffusionMariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 12 / 46
  13. 13. The Molecules Of Knowledge Model Informal MoKWait...what??Coordination — Our goal is to autonomously compose information in theattempt to obtain meaningful knowledge→ we need a coordination model to govern interactions betweenindependent data chunksSources — No way to know a priori which information is useful and whichnot, at any time, for any user→ we need techniques to keep sources within the system, even if theyare temporarily unusedReactions — As much work as possible should be done by the system,transparently to users→ we need a proactive computational model able to spontaneouslystart computationsReification — Being our target a socio-techinal scenario, users will have acentral role to play in the system→ hence the system must be able to properly model users and theiractions in order to promptly react to their stimuliMariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 13 / 46
  14. 14. The Molecules Of Knowledge Model Informal MoKEnvisioning MoK SystemsMariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 14 / 46
  15. 15. The Molecules Of Knowledge Model Informal MoKMoK Abstractions Iatoms the smallest unit of knowledge in MoK, contain informationfrom a source and belong to a compartment—thus beingsubject to its “laws of nature”molecules the MoK units for knowledge aggregation, bond together“somehow-related” atomsenzymes emitted by MoK catalysts, represent prosumer’s actions andparticipate MoK reactions to affect the way in which atomsand molecules evolvereactions working at a given rate, they drive the evolution of eachMoK compartment, by ruling the way in which moleculesaggregate, are reinforced, diffuse, and decayMariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 15 / 46
  16. 16. The Molecules Of Knowledge Model Informal MoKMoK Abstractions IIcompartments the spatial abstraction of MoK, compartments representthe conceptual loci for all MoK entities as well as for MoKbiochemical processes, also providing MoK with the notionsof locality and neighbourhoodsources each one associated to a compartment, MoK sources arethe origins of knowledge, which is continuously injected at acertain rate in the form of MoK atomscatalysts the abstraction for knowledge prosumers, catalysts emitenzymes in order to attract to him/her relevant knowledgeitemsMariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 16 / 46
  17. 17. The Molecules Of Knowledge Model Informal MoKWrap-upSourcesMoK sources, atoms and molecules, represent the information within thesystem at a certain point in time and space.ReactionsMoK biochemical reactions and compartments are the proactive part of aMoK system, meant to support, ease, even replace users work.ReificationMoK enzymes, catalysts and compartments together bring users actionsas well as their effects inside a MoK system, to be exploited withinreactions in order to drive MoK behaviour.Mariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 17 / 46
  18. 18. The Molecules Of Knowledge Model Formal MoKOutline1 MoK Genesis2 The Molecules Of Knowledge ModelInformal MoKFormal MoK3 A MoK InfrastructureThe TuCSoN MiddlewareMapping MoK over TuCSoN4 A Case Study: MoK-News5 Conclusion & Future WorksMariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 18 / 46
  19. 19. The Molecules Of Knowledge Model Formal MoKThe MoK Model IAtomsBelonging to a source, atoms carry a piece of data, possibly some metadata andkeep track of their concentration in the local compartmentatom(src, val, attr)cMoleculesMolecules are unordered collections of somehow related atoms, again equippedwith a concentration valuemolecule(Atoms)cEnzymesEnzymes are strictly coupled to the atom/molecule being accessed—and, asusual, have a concentration attachedenzyme(Atoms)cMariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 19 / 46
  20. 20. The Molecules Of Knowledge Model Formal MoKThe MoK Model IIAggregationIn the aggregation reaction two somehow related atoms/moleculesa arejoined to spring a new moleculemolecule(Atoms1) + molecule(Atoms2)−→raggmolecule(Atoms1 Atoms2) + Residual(Atoms1 Atoms2)aFrom now on we’ll se atoms as “singleton” molecules.ReinforcementReinforcement reaction increases the concentration of molecules byconsuming compliant enzymes from the local compartmentenzyme(Molecule1) + Moleculec1 −→rreinfMoleculec+11Mariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 20 / 46
  21. 21. The Molecules Of Knowledge Model Formal MoKThe MoK Model IIIDecayEnforcing time situatedness, molecules should fade away as time passesMoleculec−→rdecayMoleculec−1DiffusionAnalogously, space situatedness is based upon diffusion, being inspired bybiology{Molecule1 Molecules1}σi+ {Molecules2}σii−→rdiffusion{Molecules1}σi+ {Molecules2 Molecule1}σiiMariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 21 / 46
  22. 22. The Molecules Of Knowledge Model Formal MoKImagine. . .. . . as users are busy doing their work, e.g. searching fornews,. . .. . . they release enzymes within their compartment, whichare then used to increment the concentration of themolecules being accessed. . .. . . meanwhile, non-accessed molecules decay. . .. . . and any molecule may migrate to neighbourscompartment, giving them a chance to be reinforced byother usersMariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 22 / 46
  23. 23. The Molecules Of Knowledge Model Formal MoKMoK Model IVWhat is “somehow related/compliant”?MoK reactions should check reactants correlation to apply, so, in order todefine a MoK system, one should first of all define a FMoK functionFMoK: molecule × molecule → D, which takes two molecules anddetermines if (and how much) they are related.The FMoK functionThe exact definition of FMoK – that is the mathematical description ofdomain D – depends on the application at hand, however will likelydepend on the fields val and attr inside MoK atoms.N.B. The FMoK function could range from the simple Linda syntacticalmatching – hence D = {true, false} – to more complex semantical fuzzymatching mechanisms—for which typically D ∈ [0, 1].Mariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 23 / 46
  24. 24. The Molecules Of Knowledge Model Formal MoKOn the TableSemantics — Matching between actual molecules wandering in acompartment and molecules templates within reactions mustbe semantic to have a meaningful aggregation of knowledgeStigmergy — Enzymes are crucial in the process of driving acompartment behaviour toward its user needs, ’cause theyare the traces of its own behaviourChemistry — MoK reactions execution is driven by rates, which inturn are influenced by molecules concentrations, thus thecomputational model is that of a chemical reactionSituatedness — Information relevance may change depending on when itis available and where (also to whom, of course), hencespatio-temporal situatedness is a mandatory featureMariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 24 / 46
  25. 25. A MoK InfrastructureOutline1 MoK Genesis2 The Molecules Of Knowledge ModelInformal MoKFormal MoK3 A MoK InfrastructureThe TuCSoN MiddlewareMapping MoK over TuCSoN4 A Case Study: MoK-News5 Conclusion & Future WorksMariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 25 / 46
  26. 26. A MoK InfrastructureTuple-based Coordination Models ICoordination modelsTuple-based coordination models and languages have already shown theireffectiveness in the engineering of complex software systems, likeknowledge-intensive, pervasive and self-organising ones[Omicini and Viroli, 2011].Mariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 26 / 46
  27. 27. A MoK InfrastructureTuple-based Coordination Models IIBiochemical tuple spacesThe biochemical tuple space abstraction brings self-organisation intotuple-based coordination, by exploiting the (bio)chemical metaphorenhanced with topology aspects [Viroli and Casadei, 2009].→ tuples are seen as chemical reactants, thus equipped with anactivity/pertinency value—resembling chemical concentration1→ chemical reactions evolve tuples and possibly diffuse them toneighboring chemical compartments→ tuple spaces act as chemical solutions simulators, that is updateconcentrations following the Gillespie algorithm [Gillespie, 1977], hostand execute chemical reactions and manage the topology-relatedfeatures1Their relative quantity w.r.t. the others.Mariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 27 / 46
  28. 28. A MoK Infrastructure The TuCSoN MiddlewareOutline1 MoK Genesis2 The Molecules Of Knowledge ModelInformal MoKFormal MoK3 A MoK InfrastructureThe TuCSoN MiddlewareMapping MoK over TuCSoN4 A Case Study: MoK-News5 Conclusion & Future WorksMariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 28 / 46
  29. 29. A MoK Infrastructure The TuCSoN MiddlewareTuCSoN & ReSpecT ITuCSoN [Omicini and Zambonelli, 1999]TuCSoN is a Linda-inspired coordination model & infrastructureproviding developers with a distributed, tuple-based middleware exploitingprogrammable tuple spaces called tuple centres.ReSpecT [Omicini, 2006]The behaviour of such tuple centres can be programmed through theReSpecT logic language so to encapsulate any coordination laws directlyinto the coordination abstraction.Mariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 29 / 46
  30. 30. A MoK Infrastructure The TuCSoN MiddlewareTuCSoN & ReSpecT IITuCSoN provides the basic ingredients to enable biochemical coordination:topology multiple tuple centres can be deployed in different nodes of anetwork and/or can coexist in a single node, promoting thenotions of locality and neighbourhoodprogrammability chemically-inspired evolution of tuples is enabled byimplementing the Gillespie algorithm [Gillespie, 1977] as aReSpecT programmatching general purpose MoK reactions can be applied to actualreactants by using ReSpecT logic tuples and templates torepresent atoms, molecules and enzymesMariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 30 / 46
  31. 31. A MoK Infrastructure Mapping MoK over TuCSoNOutline1 MoK Genesis2 The Molecules Of Knowledge ModelInformal MoKFormal MoK3 A MoK InfrastructureThe TuCSoN MiddlewareMapping MoK over TuCSoN4 A Case Study: MoK-News5 Conclusion & Future WorksMariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 31 / 46
  32. 32. A MoK Infrastructure Mapping MoK over TuCSoNTuCSoN for MoK IMoK {atoms, molecules, enzymes} → ReSpecT logic tuplesBeing generated, accessed, moved and consumed both by users and by theMoK system itself – through reactions –, atoms, molecules and enzymescould all be implemented as ReSpecT tuples so to be effectively managedby the TuCSoN middleware.MoK compartments → TuCSoN tuple centresBy definition, compartments are the locality abstraction in MoK, thus themapping with TuCSoN tuple centres is straightforward since they hostboth:the ordinary tuples, that is data chunks—thus atoms, molecules andenzymesthe specification tuples, that is ReSpecT programs statements—henceMoK reactionsMariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 32 / 46
  33. 33. A MoK Infrastructure Mapping MoK over TuCSoNTuCSoN for MoK IIMoK reactions −→∗ ReSpecT programsMoK reactions are simply declarative statements specifying how existingknowledge should combine, fade away, replicate or move, hence they needto be interpreted and executed. Furthermore, being chemically-inspired,this should be done according to Gillespie’s chemical simulation algorithm.MoK reactions → ReSpecT logic tuples → ReSpecT programsSo, mapping to TuCSoN could be “two-layered”:MoK reactions are encapsulated into ReSpecT tuples of the kindlaw([Inputs], Rate, [Outputs]);such tuples basically constitute the raw data consumed by theReSpecT implementation of Gillespie algorithm—which continuously,in a chemical-like fashion, schedules and executes them.Mariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 33 / 46
  34. 34. A Case Study: MoK-NewsOutline1 MoK Genesis2 The Molecules Of Knowledge ModelInformal MoKFormal MoK3 A MoK InfrastructureThe TuCSoN MiddlewareMapping MoK over TuCSoN4 A Case Study: MoK-News5 Conclusion & Future WorksMariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 34 / 46
  35. 35. A Case Study: MoK-NewsWhy NewsNews management systems are a prominent example ofknowledge-intensive, socio-technical systems, due to:heterogeneity News sources can be virtually anything, from handwrittennotes to printed official documents through web publishedarticlesubiquity Netbooks, tablets and smartphones pushed informationproduction, sharing and consumption to be pervasive asnever beforeunpredictability News producers are no longer graduated journalists solely,they include bloggers and whoever has access to the webthoughMariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 35 / 46
  36. 36. A Case Study: MoK-NewsMoK-News ModelFormallyA generic MoK atom of the form atom(src, val, attr)c becomes aspecialised MoK-News [Mariani and Omicini, 2012] atom of the formatom(src, val, sem(tag, catalog))cwheresrc ::= news source urival ::= news contentattr ::= sem(tag, catalog)tag ::= NewsML tag | NITF tagcatalog ::= NewsCode uri | ontology uriMariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 36 / 46
  37. 37. A Case Study: MoK-NewsEnvisioning MoK-News Systems IA MoK-News system should hence be seen as a self-organising newsrepository in which! news pieces – “tag-content” pairs – are injected either automatically(e.g. using XML parsers) or manually (by journalists) in the form ofMoK-News atoms! enzymes are released by catalysts (journalists) as manifestations oftheir actions over knowledge! biochemical reactionsaggregate together semantically related atoms — based upon cataloginformationdiffuse atoms/molecules in neighborhood compartmentsreinforce them by using enzymesdecay non-relevant informationMariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 37 / 46
  38. 38. A Case Study: MoK-NewsEnvisioning MoK-News Systems II“Smart” diffusionIt is achieved as a self-organising process caused by the cooperation amongdiffusion, reinforcement – of relevant knowledge, that is more frequentlyaccessed – and decay—of useless information, ignored by catalysts.E.g., a journalist interested in sports news is more likely to search, read,annotate – generally, access – sport-related atoms. In the process, shereleases enzymes which reinforce accessed molecules concentration. In thevery end, her compartment will mainly store sports-related knowledge.Mariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 38 / 46
  39. 39. A Case Study: MoK-NewsEnvisioning MoK-News Systems IIIMariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 39 / 46
  40. 40. A Case Study: MoK-NewsEnvisioning MoK-News Systems IVA stochastic equilibrium between diffusion, reinforcement and decay laws, makesa “smart migration” pattern appear by emergence.Mariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 40 / 46
  41. 41. Conclusion & Future WorksOutline1 MoK Genesis2 The Molecules Of Knowledge ModelInformal MoKFormal MoK3 A MoK InfrastructureThe TuCSoN MiddlewareMapping MoK over TuCSoN4 A Case Study: MoK-News5 Conclusion & Future WorksMariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 41 / 46
  42. 42. Conclusion & Future WorksFinal RemarkMolecules Of KnowledgeThe MoK model→ provides knowledge workers in general with a novel approach both inthinking and managing knowledge→ supports their work through self-organising shared workspaces able toautonomously cluster and spread informationMariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 42 / 46
  43. 43. Conclusion & Future WorksFurther DevelopmentsHow to. . .? push the MoK model toward the idea of self-organising workspace[Omicini, 2011], fully supporting adaptiveness of compartments ratherthan information solely?? effectively implement efficient semantic matching mechanisms[Nardini et al., 2013] to lift Linda purely syntactical one currentlyexploited in TuCSoN?Mariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 43 / 46
  44. 44. BibliographyBibliography IGillespie, D. T. (1977).Exact stochastic simulation of coupled chemical reactions.The Journal of Physical Chemistry, 81(25):2340–2361.Mariani, S. and Omicini, A. (2012).Self-organising news management: The Molecules of Knowledge approach.In Fernandez-Marquez, J. L., Montagna, S., Omicini, A., and Zambonelli, F., editors, 1stInternational Workshop on Adaptive Service Ecosystems: Natural and Socially InspiredSolutions (ASENSIS 2012), pages 11–16, SASO 2012, Lyon, France.Pre-proceedings.Nardini, E., Omicini, A., and Viroli, M. (2013).Semantic tuple centres.Science of Computer Programming, 78(5):569–582.Special section: Self-Organizing Coordination.Omicini, A. (2006).Formal ReSpecT in the A&A perspective.In Canal, C. and Viroli, M., editors, 5th International Workshop on Foundations ofCoordination Languages and Software Architectures (FOCLASA’06), pages 93–115,CONCUR 2006, Bonn, Germany. University of M´alaga, Spain.Proceedings.Mariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 44 / 46
  45. 45. BibliographyBibliography IIOmicini, A. (2011).Self-organising knowledge-intensive workspaces.In Ferscha, A., editor, Pervasive Adaptation. The Next Generation Pervasive ComputingResearch Agenda, chapter VII: Human-Centric Adaptation, pages 71–72. Institute forPervasive Computing, Johannes Kepler University Linz, Austria.Omicini, A. and Viroli, M. (2011).Coordination models and languages: From parallel computing to self-organisation.The Knowledge Engineering Review, 26(1).Special Issue for the 25th Years of the Knowledge Engineering Review.Omicini, A. and Zambonelli, F. (1999).Coordination for Internet application development.Autonomous Agents and Multi-Agent Systems, 2(3):251–269.Special Issue: Coordination Mechanisms for Web Agents.Viroli, M. and Casadei, M. (2009).Biochemical tuple spaces for self-organising coordination.In Field, J. and Vasconcelos, V. T., editors, Coordination Languages and Models, volume5521 of LNCS, pages 143–162. Springer, Lisbon, Portugal.Mariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 45 / 46
  46. 46. Molecules Of Knowledge:Self-Organisation in Knowledge-Intensive EnvironmentsStefano Mariani, Andrea Omicini{s.mariani, andrea.omicini}@unibo.itDipartimento di Informatica: Scienza e Ingegneria (DISI)Alma Mater Studiorum—Universit`a di BolognaLaboratory of Systems and Applications 2012/2013Master Degree in Computer Science EngineeringMariani, Omicini (Universit`a di Bologna) Molecules Of Knowledge LSA-LM, 17/5/2013 46 / 46

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