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AND SET OF EXPERIENCE KNOWLEDGE STRUCTURE

Decisional DNA. AND SET OF EXPERIENCE KNOWLEDGE STRUCTURE. REFERENCES.

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AND SET OF EXPERIENCE KNOWLEDGE STRUCTURE

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  1. Decisional DNA AND SET OF EXPERIENCE KNOWLEDGE STRUCTURE

  2. REFERENCES Decisional DNA and the Smart Knowledge Mmanagement System: A process of transforming information into knowledge, in Tools and Techniques for the Design and Implementation of Enterprise Information, Idea: London (2008) An OWL Ontology of Set of Experience Knowledge Structure, Journal of Universal Computer Science, Vol. 13, pp. 209-223, 2007. Towards the Construction of Decisional DNA: A Set of Experience Knowledge Structure Java Class within an Ontology System, Cybernetics and Systems: An International Journal, Vol. 38, 2007, pp. 859-878. British Library Direct: Order from the British Library: GENETIC ALGORITHMS FOR DECISIONAL DNA: SOLVING SETS OF EXPERIENCE KNOWLEDGE STRUCTURE.direct.bl.uk/research/0B/36/RN209832973.html

  3. Content • Introduction • Knowledge & Knowledge Technology • Set of Experience Knowledge Structure • Decisional DNA • KSCS • Negotiation and Unification of SOEKS • Conclusions

  4. Introduction • Knowledge is considered a valuable possession of incalculable worth. • Knowledge seems to be the only true source of a nation’s economic and military strength, as well as, the key source of competitive advantage of a company (Drucker, 1995). • The means and the ability of acquisition of knowledge, through efficient transformation of information, can make the difference between the success and failure of a company in the competitive environment of global economy and knowledge society.

  5. Knowledge • Knowledge is “the fact or condition of knowing something with familiarity gained through experience or association” (Merriam-Webster Dict. 2004). • Lin et al. (2002) describe the concept of knowledge as an organized mixture of data, integrated with rules, operations, and procedures, and it can be only learnt through experience and practice.

  6. ? KNOWLEDGE INFORMATION DATA

  7. Knowledge Technology • Many technologies, such as Knowledge Management (KM), Data Mining (DM) and Knowledge-base (Kb) are currently working with different types of knowledge, however, they do NOT keep structured knowledge of the formal decision events they participate on. • Knowledge Supply Chain System (KSCS) is a platform that keeps explicit knowledge of the formal decision events it performs, understanding that a formal decision event is a decision occurrence that was made following strict procedures that make it structured and formal.

  8. Proposed Platform… • Reduces Information Restrictions. • Develops Knowledge and Applies it. • A System which is Knower and Decider. • Understands that Knowledge is NOW an Administrative and Technological Matter. • Technology able to capture and store formal decision events as explicit knowledge.

  9. MULTI- DISCIPLINARITY ATOMICITY AUTOPOIESIS ENACTION MODEL Five Fundamentals… Transforming Information Into Knowledge

  10. Model Intelligent behavior presupposes a capacity for representing the world in certain way (models). • Simplified version of something complex. • Knowledge can be defined by computations based on models

  11. …Model • A system can understand the world as long as it represents it, and creates its own models. • Reformulating models permits a dynamic perception of the world.

  12. Enaction(Varela et al. 1991) • – acting. • Knowledge depends upon the experiences of a system with some capacities that perceive and act. • These capacities, when perceiving and acting, build the models.

  13. …Enaction • Enaction supposes that the world can be divided in regions of elements and separate tasks. • Knowledge can be reached taking into account the acceptance of the elements, properties, and relationships. • The system has to construct its own world, perceptions, rules, and relationships according to the experiences it is having and the defined regions.

  14. Autopoiesis (Maturana et al. 1980) Autopoiesis means self-production. A system produces its own organization and maintains and constitutes itself in a defined space. The components of the system constitutes a distinct unity and they continuously regenerate through interactions and transformation.

  15. Atomicity andMultidisciplinarity ATOMICITY Divide and Conquer. Division of the problem into sub-problems. MULTIDISCIPLINARITY Use several specialized skills for solving problems.

  16. Knowledge-base Layer Integration Layer DIAGNOSIS Internal Analyzer Layer External Analyzer Layer Risk Analyzer Layer PROGNOSIS SOLUTION Solution Layer Experience Creator Ruler Creator Intuition Creator KNOWLEDGE Macro-processes, layers and creators. Knowledge Supply Chain System (KSCS) The KSCS works through four macro-processes which are supported by six layers and three creators.

  17. Platform: KSCS KNOWLEDGE SUPPLY CHAIN SYSTEM (KSCS) KSCS is supported by many applications that supply information. These applications working together confront a problem and perform particular formal decision events. Subsequently, the KSCS constructs Sets of Experience based on this information and guides them through a process of transformation into knowledge.

  18. Platform: KSCS • Multi-Source Knowledge-Experience Management System. • Integrated tool of rule-based systems, experts systems, numerical models, and self-learning technology to help in the decision-making process. • Interrelated net of similar systems supplying knowledge and sharing experiences and perceptions of their own worlds.

  19. Internal Analyzer layer Experience Creator Se1=Le1 Se2=Le2 BS1(r,f) PRIORITIES Si1=Li1 . . . . . . Si2=Li2 Ser=Ler BSk(r,f) . . . Li1 (r,f) Sim=Lim M1 Li2 (r,f) . . . M2 External Analyzer layer Intuition Creator . . . Lim (r,f) BS (r,f) I1(BSi)(l) Mn Le1 (r,f) . . . Le2 (r,f) Is(BSi)(l) . . . Ler (r,f) Ruler Creator R1(BSi,Vj) . . . Rq(Bsi,Vj) Knowledge-base layer Integration layer Risk Analyzer layer DIAGNOSIS SOLUTION KNOWLEDGE PROGNOSIS Platform Process

  20. Additional Considerations The platform should be trained. Users must feed the platform – It is an organizational challenge. Rules and sets of experience should be re-evaluated or adjusted by users. It is the day-to-day operation that makes the platform more accurate.

  21. Knowledge and Set of Experience

  22. Representing Knowledge • One of the most complicated issues about knowledge is its representation. Representing knowledge determines how knowledge is acquired and transformed from tacit knowledge to explicit knowledge. • It is necessary to create a structure able to take knowledge, to store proper characteristics of the experience acquired, to keep this experience as explicit knowledge, and to allow it for multiple technologies to be used, analysed and categorized. • Knowledge Representation (KR) for formal decision events.

  23. Knowledge Representation : FIVE Fundamentals 1. Surrogate, a substitute for the thing itself that is used to determine consequences by thinking rather than acting

  24. Knowledge Representation : FIVE Fundamentals 2. Set of ontological commitments, that is an answer to the question, in what terms should I think about the world.

  25. Knowledge Representation : FIVE Fundamentals 3. Fragmentary theory of intelligent reasoning.

  26. Knowledge Representation : FIVE Fundamentals 4. A medium for pragmatically efficient computation, environment in which thinking is accomplished.

  27. Knowledge Representation : FIVE Fundamentals 5. Medium of human expression, a language in which we say things about the world.

  28. Knowledge Representation (KR) • The most generalized techniques of KR use logic, rules, or frames. • LOGIC implicates understanding the world in terms of individual entities and associations between them. • RULE-BASED systems view the world in terms of attribute-object-value and the rules that connect them. • FRAMES comprisethinking about the world in terms of prototypical concepts.

  29. ** OBSERVED STATISTICS REPORT for scenario TVANIM ** Label Mean Standard Number of Minimum Maximum Value Deviation Observations Value Value TIME IN SYSTEM 26.956 34.643 83 8.202 170.770 ** FILE STATISTICS REPORT for scenario TVANIM ** File Label or Average Standard Maximum Current Average Number Input Location Length Deviation Length Length Wait Time 1 QUEUE INSP 0.863 0.873 4 1 4.060 2 QUEUE ADJT 1.610 1.307 4 1 51.526 ** ACTIVITY STATISTICS REPORT for scenario TVANIM ** What to represent?… All applications in the knowledge-based layer confront problems and perform formal decision events. A formal decision event is a decision occurrence, which was performed using procedures that make it structure and formal.

  30. U V If X>70 then K = good R If W<2 then Z = 2 Z = 0.78 If G=blue then B = high K = average X = 100 H = good RtÈRlÈRtl W = 1.5 G = blue Y = 210 B = high V = 8451.54 C 2X+3Y-V <= 3450 Vl Vt H>=Excellent G<>blue AND Y+70X<2500 F Max P=3X-2Y+RQ CtÈCl Max K=Excellent Min C=YQ AND B=high FtÈFlÈFtl Formal Decision Events Their four components are variables, functions, constraints, and rules, and constitute the elements to be represented.

  31. “The only source of knowledge is experience.”Albert Einstein (1879 - 1955)

  32. U V If X>70 then K = good R If W<2 then Z = 2 Z = 0.78 If G=blue then B = high K = average X = 100 H = good RtÈRlÈRtl W = 1.5 G = blue Y = 210 B = high V = 8451.54 C 2X+3Y-V <= 3450 Vl Vt H>=Excellent G<>blue AND Y+70X<2500 F Max P=3X-2Y+RQ CtÈCl Max K=Excellent Min C=YQ AND B=high FtÈFlÈFtl Set of Experience Knowledge Structure The four components are variables, functions, constraints, and rules, and constitute the knowledge structure. Set of Experience Ei = (Vij, Fi, Ci, Ri)

  33. Set of Experience Knowledge Structure Graphic idea: SOEKS Ei = (Vi, Fi, Ci, Ri)

  34. Variables Rules Functions Constraints SOEKS or SOE SOE comprises a series of mathematical concepts (a logical component), together with a set of rules (a ruled based component), and it is built upon a specific event of decision-making (a frame component). • Unique • Adaptable • Dynamic

  35. Competitor’s payment Level = $30 • Working Condition = GOOD • Firing = 10 • Competitor’s Firing = 14 • Promotional Chance = EQUAL • X1 = 2 • X2 = 9 • RESULTS • X1 = 5 • X2 = 7.5 • Payment Level = $30 • Status of Payment = COMPETITIVE • Status of Firing = VERY GOOD • Status of Promotion = VERY GOOD • Worker’s Morale = VERY GOOD Constraints 6X1+X2>=21 X1+2X2>=20 X1>=0 X2>=0 Functions Min Payment Level=3X1 + 2X2 Worker’s Morale >= GOOD X1 X2 Payment Level Competitor’s Payment level Status of Payment Working Condition Promotional Chance Worker’s Morale Firing Competitor’s Firing Status of Firing Status of Promotion Variables IF Payment Level>=Competitor’s Payment Level THEN Status of Payment=COMPETITIVE Rules IF Firing<= 1.2*Competitor’s Firing THEN Status of Firing=VERY GOOD IF Promotional Chance=EQUAL THEN Status of Promotion=VERY GOOD IF Working Condition>=GOOD &Status of Payment=COMPETITIVE & Status of Firing=VERY GOOD & Status of Promotion=VERY GOOD THEN Worker’s Morale=VERY GOOD Example of SOE

  36. Applicable and Usable SOE

  37. The Set of Experience works with formal decision events from multiple applications. All of them having different languages, formats and structures, and therefore, being an obstacle to the continuous flow of information and knowledge. A unique language facilitates the integration practice. A Shareable SOE

  38. Web technologies have developed several tools for integration of disparate systems and distributed applications, including standards or protocols. Languages with a defined vocabulary, structure, and constraints for expressing information and knowledge. Standards aim for a common language. A Shareable SOE

  39. 4ML ABML ACAP ACML ACS X12 ADML AECM AFML AGML AHML AIF AIML AL3 AML ANATML ANML ANNOTEABGML ANZLIC APML APPEL APPML AQL ARML ASML ASTM ATML AWML AXML BannerML BCXML BEEP BGML BHTML BIBLIOML BIOML BIPS BizCodes BLM XML BML BPML BRML BSML CaseXML CaXML Standards HTML • XBEL • xCBL • XCES • XCFF • Xchart • xCIL • xCML • Xdelta • XDF • XForms • XGF • XGL • XHTML • XIOP • XLF • XLIFF • XLink • XMI • XML • XML Court • XML EDI • XML F • XML Key • XML MP • XML News • XML P7C • XML Query • XML RPC • XML Schema • XML Sign • XML TP • XML XCI • XMLife • XMLVoc • XMSG • XMTP • xNAL • XNS • XOL • XSBEL • XSIL • XUL • CBML • CDA • CDF • CDISC • CELLML • CFML • ChessGML • ChordML • ChordQL • CIDS • CIDX • CIM • CIML • CLT • CML • CNRP • Coins • ComicsML • Covad xLink • CP eXchange • CPL • CSS • CVML • CWMI • CXML • CycML • DaliML • DAML • DaqXML • DAS • DASL • DCMI • DDI • DeltaV • DESSERT • DIG35 • DLML • DML • DMML • DMTF • DocBook • DocScope • DoD XML • DOI • DPRL • DRI • DSD • DSML • DTB • DXS • EAD • eBIS-XML • ebXML • ECML • eCo • EcoKnow • eCX • ECIX • edaXML • EML • EMSA • eosML • ESML • ETD-ML • FieldML • FINML • FITS • FIXML • FLBC • FLOWML • FPML • FSML • GAME • GBXML • GDML • GEDML • GEML • GEN • GeoLang • GIML • GML • GXD • GXL • GXML • HEML • HITIS • HRMML • HR-XML • HTML • HTTP-DRP • HTTPL • HumanML • Hy XM • HyTime • ICE • ICML • IDE • IDML • IDWG • IEEE DTD • IFX • IML • IMPP • IMS Global • InTML • IOTP • IRML • IXML • IXRetail • JabberXML • JDF • JDox • JECMM • JigXML • JLife • JScoreML • JSML • KBML • LACITO • LandXML • LEDES • LegalXML • Life Data • LitML • LMML • LogML • LTSC XML • MAML • MathML • MatML • MBAM • MCF • MDDL • MDSI-XML • Metarule • MFDX • MISML • MIX • ML • MML • MMLL • MoDL • MOS • MPML • MPXML • MRML • MSAML • MTML • MusicXML • NAA Ads • NAML • Navy DTD • NewsML • NFF • NISO DTB • NITF • NLMXML • NML • NuDOC • NVML • OAGIS • OAMS • OBI • OCF • OCS • ODF • ODRL • OeBPS • Office XML • OFX • OIL • OIM • OLifE • OML • ONIX DTD • OODT • OOPML • OpenMath • OPML • OPX • OSD • OTA • P3P • PARLML • PCIS • PDML • PDX • PEF XML • PetroML • PGML • PhysicsML • PICS • PML • PMML • PNG • PNML • PrintML • PrintTalk • ProductionML • PSI • PSL • QAML • QML • QuickData • RBAC • RDDl • RDF • RDL • RecipeML • RELAX • RELAX NG • REPML • ResumeXML • RETML • REXML • RFML • RightsLang • RIXML • RoadmOPS • RosettaNet PIP • RSS • RuleML • SABLE • SAE J2008 • SAML • SBML • Schemtron • SDML • SearchDM-XML • SGML • SHOE • SIF • SMBXML • SMDL • SMI • SMIL • SML • SMML • SOAP • SODL • SOX • SpeechML • SPML • SSML • STEP • STEPML • STML • SVG • SWAP • SWMS • SyncML • TalkML • TaxML • TDL • TDML • TEI • ThML • TIM • TML • TMML • TMX • TP • TPAML • TREX • TxLife • UBL • UCLP • UDDI • UDEF • UIML • ULF • UML • UMLS • UPnP • URI/URL • UXF • vCalendar • vCard • VCML • VHG • VIML • VISA XML • VML • VMML • VocML • VoiceXML • VRML • WAP • WDDX • WebDAV • WebML • WeldingXMLXGM • WellML • Wf-XML • WIDL • WITSML • WML • WorldOS • WSIA • WSML • XACML • XAML • XBL • XBN • XBRL RuleML OptML SGML AIML PMML SNOML XML RDF LPFML XRML RFML MathML

  40. A Shareable Set of Experience Among all of the languages, XML was chosen because: Simplicity, Transmits not just format, but also meaningful information, Easy to understand, read, and write, Allows to structure information and knowledge from a graph structure as labelled trees, Permits defining restrictions for the document (XSD), Supported by the W3C as a language for knowledge representation, and XML is the leader method for application integration.

  41. <?xml version="1.0" encoding="UTF-8" standalone="no" ?> <!-- Set of Experience Knowledge Structure --> <set_of_experience xmlns: xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:noNamespaceSchemaLocation="set of experience model.xsd"> <date>2004-11-11</date> <hour>14:10:00</hour> <creation> <application> Excel</application> <application> System</application> <filename> payroll.xls</filename> <filename> payroll.ces</filename> <comment> Example of set of experience</comment> <comment> Company Expert System </comment> </creation> <category> <!-- Category encloses this set of experience into a determined chromosome of the company --> <area>Human Resources</area> <subarea>Salary Office</subarea> <subject>Payment Level</subject> <subject>Worker's Morale</subject> </category> A Shareable Set of Experience Set of experience knowledge structure is able to be implemented in XML.

  42. Extending SOE as a KR

  43. SOE Additional Considerations • The knowledge structure should be re-evaluated or adjusted by users. • It is the day-to-day experience and generation of formal decision events that make the knowledge structure more accurate. • The knowledge structure should acts as a trained element of “life” - Genetic History.

  44. Extending SOE as a KR • Experience is acquired while decision-making is executed. Thus, new knowledge is produced while solving problems. • It can be compared to the process of construction of the psychological space of an organization. • Based upon Kelly’s theory of psychological space, we develop a knowledge structure to administer formal decision events, a structure that builds up this space with formal decision experiences. Then, this psychological space can be used for future decision-making processes based upon previous decision events.

  45. Image credit U.S. Department of Energy Human Genome Program (http://www.ornl.gov/hgmis). Variables Rules Functions Constraints Extending SOE as a KR • Each SOE provides a value • Categorized according to type of decision • Gene provides aPhenotype • Categorized  Chromosomes

  46. Variables Variables Variables Variables Variables Variables Variables Variables Variables Variables Variables Variables Variables Variables Variables Rules Rules Rules Rules Rules Rules Rules Rules Rules Rules Rules Rules Rules Rules Rules Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Constraints Constraints Constraints Constraints Constraints Constraints Constraints Constraints Constraints Constraints Constraints Constraints Constraints Constraints Constraints Decisional DNA • Groups by area of sets of experience = chromosomes of area. • Groups of chromosomes of area = DECISIONAL DNA of a Company AREA 1 (marketing) AREA 2 (finances) AREA 3 (design)

  47. Variables Rules Functions Constraints Decisional DNA • It keeps: • the decisional history of a company. • the decisional experience of a company. • information that could be read by future generations of decision makers. • Knowledge in an explicit way. • Knowledge that could be shared.

  48. Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Decisional Chromosome Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Decisional DNA Vi Vi Vi Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Decisional Gene or SOEKS Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Decisional DNA Decisional DNA • SOE are grouped according to their phenotype creating Decisional Chromosomes. • Groups of chromosomes construct the Decisional DNA.

  49. Conclusion SOEKS is a suitable representation for formal decision events. Unifying Sets of experience into a unique holistic SOE, and storing them as Decisional DNA can explore new ways to put explicit knowledge in the hands of employees, customers, suppliers, and partners. Additionally, when it is applied in the KSCS, it advances the notion of administering knowledge in the current decision making environment.

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