Agile development of diagnostic knowledge systems

The success of diagnostic knowledge systems has been proved over the last decades. Nowadays, intelligent systems are embedded in machines within various domains or are used in interaction with a user for solving problems. However, although such systems have been applied very successfully the development of a knowledge system is still a critical issue. Similarly to projects dealing with customized software at a highly innovative level a precise specification often cannot be given in advance. Moreover, necessary requirements of the knowledge system can be defined not until the project has been started or are changing during the development phase. Many success factors depend on the feedback given by users, which can be provided if preliminary demonstrations of the system can be delivered as soon as possible, e.g., for interactive systems validation the duration of the system dialog. This thesis motivates that classical, document-centered approaches cannot be applied in such a setting. We cope with this problem by introducing an agile process model for developing diagnostic knowledge systems, mainly inspired by the ideas of the eXtreme Programming methodology known in software engineering. The main aim of the presented work is to simplify the engineering process for domain specialists formalizing the knowledge themselves. The engineering process is supported at a primary level by the introduction of knowledge containers, that define an organized view of knowledge contained in the system. Consequently, we provide structured procedures as a recommendation for filling these containers. The actual knowledge is acquired and formalized right from start, and the integration to runnable knowledge systems is done continuously in order to allow for an early and concrete feedback. In contrast to related prototyping approaches the validity and maintainability of the collected knowledge is ensured by appropriate test methods and restructuring techniques, respectively. Additionally, we propose learning methods to support the knowledge acquisition process sufficiently. The practical significance of the process model strongly depends on the available tools supporting the application of the process model. We present the system family d3web and especially the system d3web.KnowME as a highly integrated development environment for diagnostic knowledge systems. The process model and its activities, respectively, are evaluated in two real life applications: in a medical and in an environmental project the benefits of the agile development are clearly demonstrated.

[1]  Hans-Peter Eich,et al.  Internet-Based Decision-Support Server for Acute Abdominal Pain , 1999, AIMDM.

[2]  Hanna Wasyluk,et al.  Extension of the HEPAR II Model to Multiple-Disorder Diagnosis , 2000, Intelligent Information Systems.

[3]  Rainer Knauf,et al.  Validating rule-based systems: a complete methodology , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[4]  Frank Puppe,et al.  An Agile Process Model for Developing Diagnostic Knowledge Systems , 2004, Künstliche Intell..

[5]  Frederick Hayes-Roth,et al.  Building expert systems , 1983, Advanced book program.

[6]  Tony R. Martinez,et al.  Improved Heterogeneous Distance Functions , 1996, J. Artif. Intell. Res..

[7]  Stephen Cranefield,et al.  UML for ontology development , 2002, The Knowledge Engineering Review.

[8]  Alun Preece,et al.  Verifying expert systems: A logical framework and a practical tool , 1992 .

[9]  Joachim Baumeister,et al.  An expert system to estimate the pesticide contamination of small streams using benthic macroinvertebrates as bioindicators II. The knowledge base of LIMPACT , 2003 .

[10]  J. Reggia,et al.  Abductive Inference Models for Diagnostic Problem-Solving , 1990, Symbolic Computation.

[11]  Thomas R. Gruber,et al.  A translation approach to portable ontology specifications , 1993 .

[12]  Alun D. Preece,et al.  Foundation and application of knowledge base verification , 1994, Int. J. Intell. Syst..

[13]  Janet L. Kolodner,et al.  Case-Based Reasoning , 1989, IJCAI 1989.

[14]  Stefan K. Bamberger,et al.  Cooperating Diagnostic Expert Systems to Solve Complex Diagnosis Tasks , 1997, KI.

[15]  David W. Aha,et al.  Weighting Features , 1995, ICCBR.

[16]  Ian Witten,et al.  Data Mining , 2000 .

[17]  Kristian G. Olesen,et al.  HUGIN - A Shell for Building Bayesian Belief Universes for Expert Systems , 1989, IJCAI.

[18]  Steffen Staab,et al.  Ontology Learning Part One - On Discoverying Taxonomic Relations from the Web , 2002 .

[19]  Agnar Aamodt,et al.  Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches , 1994, AI Commun..

[20]  Sandra Marcus,et al.  Automating Knowledge Acquisition for Expert Systems , 1988 .

[21]  John F. Sowa,et al.  Knowledge representation: logical, philosophical, and computational foundations , 2000 .

[22]  Vice President,et al.  An Introduction to Expert Systems , 1989 .

[23]  Barry Smyth,et al.  Remembering To Forget: A Competence-Preserving Case Deletion Policy for Case-Based Reasoning Systems , 1995, IJCAI.

[24]  Robert Rowen Diagnostic systems for manufacturing , 1990 .

[25]  Francisco Javier Díez,et al.  Parameter adjustment in Bayes networks. The generalized noisy OR-gate , 1993, UAI.

[26]  Frank Puppe,et al.  XPS-99: Knowledge-Based Systems. Survey and Future Directions , 1999, Lecture Notes in Computer Science.

[27]  D. Dudgeon,et al.  The impact of agricultural runoff on stream benthos in Hong Kong, China. , 2002, Water research.

[28]  Robert Milne,et al.  Tiger: Continuous Diagnosis of Gas Turbines , 2000, ECAI.

[29]  Peter J. F. Lucas,et al.  An intelligent system for pacemaker reprogramming , 1999, Artif. Intell. Medicine.

[30]  Dieter Fensel,et al.  Knowledge Engineering: Principles and Methods , 1998, Data Knowl. Eng..

[31]  R. Budde,et al.  Approaches to Prototyping , 1984, Springer Berlin Heidelberg.

[32]  Frank Puppe,et al.  A Diagnostic Expert System for Structured Reports, Quality Assessment, and Training of Residents in Sonography , 2004, Medizinische Klinik.

[33]  David Heckerman,et al.  Probabilistic Interpretation for MYCIN's Certainty Factors , 1990, UAI.

[34]  Frank Puppe,et al.  Using Automated Tests and Restructuring Methods for an Agile Development of Diagnostic Knowledge Systems , 2004, FLAIRS Conference.

[35]  Peter Struss,et al.  A Prototype for Model-based On-board Diagnosis of Automotive Systems , 2000, AI Commun..

[36]  Thomas Reinartz,et al.  Relations between Customer Requirements, Performance Measures, and General Case Properties for Case Base Maintenance , 2002, ECCBR.

[37]  Kent Beck,et al.  Test-infected: programmers love writing tests , 2000 .

[38]  Frank Puppe,et al.  Incremental Development of Diagnostic Set-Covering Models with Therapy Effects , 2003, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[39]  Michael M. Richter,et al.  Fallbasiertes Schliessen: Vergangenheit, Gegenwart, Zukunft , 2003, Inform. Spektrum.

[40]  Trevor J. M. Bench-Capon,et al.  Maintenance of Knowledge-based Systems , 1993 .

[41]  Thomas Roth-Berghofer,et al.  Review and Restore for Case‐Base Maintenance , 2001, Comput. Intell..

[42]  V. R. Benjamins,et al.  Overview of Knowledge Sharing and Reuse Components: Ontologies and Problem-Solving Methods , 1999, IJCAI 1999.

[43]  M. Richter Classification and Learning of Similarity Measures , 1993 .

[44]  Dietmar Janetzko,et al.  Case Retrieval Nets as a Model for Building Flexible Information Systems , 2001, Künstliche Intell..

[45]  Frank Puppe,et al.  Inductive Learning for Case-Based Diagnosis with Multiple Faults , 2002, ECCBR.

[46]  Joachim Baumeister,et al.  A rule-based vs . a model-based implementation of the knowledge system LIMPACT and its significance for maintenance and discovery of ecological knowledge , 2002 .

[47]  Edward H. Shortliffe,et al.  Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence) , 1984 .

[48]  Frank Puppe,et al.  HepatoConsult: a knowledge-based second opinion and documentation system , 2002, Artif. Intell. Medicine.

[49]  Joachim Baumeister,et al.  Erratum to “ An expert system to estimate the pesticide contamination of small streams using benthic macroinvertebrates as bioindicators Part 2 : The knowledge base of LIMPACT ” [ Ecological Indicators 2 ( 2002 ) 239 – 249 ] , , 2003 .

[50]  Frank Puppe,et al.  Overview on MED1: A Heuristic Diagnostics System with an Efficient Control-Structure , 1983, GWAI.

[51]  Frank van Harmelen,et al.  A quantitative analysis of the robustness of knowledge-based systems through degradation studies , 2003, Knowledge and Information Systems.

[52]  Frank van Harmelen,et al.  Torture Tests: A Quantitative Analysis for the Robustness of Knowledge-Based Systems , 2000, EKAW.

[53]  Guus Schreiber,et al.  KADS : a principled approach to knowledge-based system development , 1993 .

[54]  Frank Puppe,et al.  Requirements for a classification expert system shell and their realization in Med 2 , 1987, Appl. Artif. Intell..

[55]  Alan L. Rector,et al.  MEDICAL INFORMATICS , 1990, The Lancet.

[56]  Michael M. Richter,et al.  The Knowledge Contained in Similarity Measures , 1995 .

[57]  Frank Puppe,et al.  Systematic Introduction to Expert Systems , 1993, Springer Berlin Heidelberg.

[58]  Stuart K. Card Information visualization and information foraging , 1996, AVI '96.

[59]  Guus Schreiber,et al.  Knowledge Engineering and Management: The CommonKADS Methodology , 1999 .

[60]  Susanne Ziegler,et al.  Wissensbasierte Diagnosesysteme im Service-Support , 2001 .

[61]  Heinz Züllighoven,et al.  Evolutionary System Development , 1992 .

[62]  Bob J. Wielinga,et al.  CommonKADS: a comprehensive methodology for KBS development , 1994, IEEE Expert.

[63]  Thomas Roth-Berghofer,et al.  Six Steps in Case–Based Reasoning: Towards a maintenance methodology for case–based reasoning systems , 2001 .

[64]  Alun D. Preece,et al.  Principles and practice in verifying rule-based systems , 1992, Knowl. Eng. Rev..

[65]  James A. Reggia,et al.  Computer-Assisted Medical Decision Making , 1985, Computers and Medicine.

[66]  Tim Menzies,et al.  Knowledge maintenance: the state of the art , 1999, The Knowledge Engineering Review.

[67]  A. Merry,et al.  Practical Perioperative Transoesophageal Echocardiography , 2018 .

[68]  William Mark,et al.  Explanation-Based Indexing of Cases , 1988, AAAI.

[69]  Dieter Landes DesignKARL - A language for the design of knowledge-based systems , 1994, SEKE.

[70]  Rainer Knauf,et al.  System Refinement in Practice - Using a Formal Method to Modify Real-Life Knowledge , 2002, FLAIRS.

[71]  Martin Gogolla,et al.  On Formalizing the UML Object Constraint Language OCL , 1998, ER.

[72]  Joseba Quevedo,et al.  TIGER: Knowledge Based Gas Turbine Condition Monitoring , 1996, AI Commun..

[73]  R. Schulz,et al.  Linking insecticide contamination and population response in an agricultural stream , 1999 .

[74]  Homer R. Warner,et al.  Computer--assisted medical decision-making , 1979 .

[75]  Kent L. Beck,et al.  Extreme programming explained - embrace change , 1990 .

[76]  Kent Beck,et al.  Chrysler goes to extremes , 1998 .

[77]  W. Ertel,et al.  Reasoning with Probabilities and Maximum Entropy: The System PIT and its Application in LEXMED , 2000 .

[78]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[79]  H. E. Pople,et al.  Internist-I, an Experimental Computer-Based Diagnostic Consultant for General Internal Medicine , 1982 .

[80]  Susan Craw,et al.  Organising Knowledge Refinement Operators , 1999, EUROVAV.

[81]  Stefan Bamberger,et al.  Building Web-based knowledge clusters , 1998 .

[82]  Stefan Wess,et al.  Using k-d Trees to Improve the Retrieval Step in Case-Based Reasoning , 1993, EWCBR.

[83]  Fausto Giunchiglia,et al.  A Theory of Abstraction , 1992, Artif. Intell..

[84]  Joachim Baumeister,et al.  Diagnostic Reasoning with Multilevel Set-Covering Models , 2002 .

[85]  Raymond J. Mooney,et al.  Inductive Learning For Abductive Diagnosis , 1994, AAAI.

[86]  Ivar Jacobson,et al.  The unified modeling language reference manual , 2010 .

[87]  Mark Stefik,et al.  Introduction to knowledge systems , 1995 .

[88]  Frank Puppe,et al.  Quality Measures for Semi-Automatic Learning of Simple Diagnostic Rule Bases , 2004 .

[89]  Aldo Gangemi,et al.  An Overview of the ONIONS Project: Applying Ontologies to the Integration of Medical Terminologies , 1999, Data Knowl. Eng..

[90]  Martin K. Purvis,et al.  UML as an Ontology Modelling Language , 1999, Intelligent Information Integration.

[91]  Marc Ayel,et al.  Validation, verification and test of knowledge-based systems , 1991 .

[92]  Frank Puppe,et al.  Inductive Learning of Simple Diagnostic Scores , 2003, ISMDA.

[93]  David Heckerman,et al.  Probabilistic similarity networks , 1991, Networks.

[94]  Giulio Trillò Annual Meeting of the European Society for Computing and Technology in Anaesthesia and Intensive Care (ESCTAIC) , 2002 .

[95]  Anna Hart,et al.  Knowledge acquisition for expert systems (2nd ed.) , 1992 .

[96]  M. Fowler Improving the Design of Existing Code , 2000 .

[97]  Joachim Baumeister,et al.  Declaratively Querying and Visualizing Knowledge Bases in Xml , 2004, INAP/WLP.

[98]  Steffen Staab,et al.  International Handbooks on Information Systems , 2013 .

[99]  William F. Opdyke,et al.  Refactoring object-oriented frameworks , 1992 .

[100]  Henrik Eriksson,et al.  Knowledge modeling at the millennium : The design and evolution of Protégé-2000 , 1999 .

[101]  William J. Clancey,et al.  The Epistemology of a Rule-Based Expert System - A Framework for Explanation , 1981, Artif. Intell..

[102]  Dieter Fensel,et al.  The Knowledge Acquisition and Representation Language, KARL , 1995, Springer US.

[103]  Qiang Yang,et al.  Remembering to Add: Competence-preserving Case-Addition Policies for Case Base Maintenance , 1999, IJCAI.

[104]  C. Ohmann,et al.  Clinical benefit of a diagnostic score for appendicitis: results of a prospective interventional study. German Study Group of Acute Abdominal Pain. , 1999, Archives of surgery.

[105]  Ashwin Ram,et al.  The Utility Problem in Case-Based Reasoning , 1993 .

[106]  M. Cahalan,et al.  The Adequacy of Basic Intraoperative Transesophageal Echocardiography Performed by Experienced Anesthesiologists , 2001, Anesthesia and analgesia.

[107]  Thomas Roth-Berghofer,et al.  On Quality Measures for Case Base Maintenance , 2000, EWCBR.

[108]  Bob J. Wielinga,et al.  Using explicit ontologies in KBS development , 1997, Int. J. Hum. Comput. Stud..

[109]  Alistair Cockburn,et al.  Agile Software Development , 2001 .

[110]  Bruce G. Buchanan,et al.  Dendral and Meta-Dendral: Their Applications Dimension , 1978, Artif. Intell..

[111]  Dennis Merritt,et al.  Building Expert Systems in Prolog , 1989, Springer Compass International.

[112]  Andreas Abecker,et al.  Toward a Technology for Organizational Memories , 1998, IEEE Intell. Syst..

[113]  Frank Puppe,et al.  Wissensbasierte Diagnose- und Informationssysteme - Mit Anwendungen des Expertensystem-Shell-Baukastens D3 , 1996, Wissensbasierte Diagnose- und Informationssysteme.

[114]  E. Burton Swanson,et al.  The dimensions of maintenance , 1976, ICSE '76.

[115]  Ralph Bergmann,et al.  Similarity Measures for Object-Oriented Case Representations , 1998, EWCBR.

[116]  Natalya F. Noy,et al.  Knowledge-Acquisition Interfaces for Domain Experts: An Empirical Evaluation of Protégé-2000 , 2000 .

[117]  Rudi Studer,et al.  Knowledge Engineering: Survey and Future Directions , 1999, XPS.

[118]  Guus Schreiber,et al.  A case study in ontology library construction , 1995, Artif. Intell. Medicine.

[119]  Karsten Poeck,et al.  Making Role Limiting Shells More Flexible , 1993, EKAW.

[120]  Frank Puppe,et al.  ILMAX: a system for managing experience knowledge in a long‐term study of stream ecosystem regeneration: An application of ecological informatics , 2004 .

[121]  H. E. Pople,et al.  Internist-1, an experimental computer-based diagnostic consultant for general internal medicine. , 1982, The New England journal of medicine.

[122]  Frank Puppe Knowledge reuse among diagnostic problem-solving methods in the Shell-Kit D3 , 1998, Int. J. Hum. Comput. Stud..

[123]  Dieter Fensel,et al.  Developing Knowledge-Based Systems with MIKE , 1998, Automated Software Engineering.

[124]  C. M. Cooper,et al.  Biological Effects of Agriculturally Derived Surface Water Pollutants on Aquatic Systems—A Review , 1993 .

[125]  David L. Waltz,et al.  Toward memory-based reasoning , 1986, CACM.

[126]  B. Chandrasekaran,et al.  Generic Tasks in Knowledge-Based Reasoning: High-Level Building Blocks for Expert System Design , 1986, IEEE Expert.

[127]  Rose F. Gamble,et al.  Methodologies for the development of knowledge-based systems, 1982–2002 , 2003, The Knowledge Engineering Review.

[128]  A. Preece Building the Right System Right Evaluating V & V Methods in Knowledge Engineering , 1998 .

[129]  Kenneth D. Forbus,et al.  Building Problem Solvers , 1993 .

[130]  Frank Puppe,et al.  Evaluation of two Strategies for Case-Based Diagnosis handling Multiple Faults , 2003, Wissensmanagement.