Taking up the situated cognition challenge with ripple down rules

Situated cognition poses a challenge that requires a paradigm shift in the way we build symbolic knowledge-based systems. Current approaches require complex analysis and modelling and the intervention of a knowledge engineer. They rely on building knowledge-level models which often result in static models that suffer from the frame of reference problem. This approach has also resulted in an emphasis on knowledge elicitation rather than user requirements elicitation. The situated nature of knowledge necessitates a review of how we build, maintain and validate knowledge-based systems. We need systems that are flexible, intuitive and that interact directly with the end-user. We need systems that are designed with maintenance in mind, allowing incremental change and on-line validation. This will require a technique that captures knowledge in context and assists the user to distinguish between contexts. We take up this challenge with a knowledge acquisition and representation method known as Ripple-down Rules. Context in Ripple-down Rules is handled by its exception structure and the storing of the case that prompted a rule to be added. A rule is added as a refinement to an incorrect rule by assigning the correct conclusion and picking the salient features in the case that differentiate the current case from the case associated with the wrong conclusion. Thus, knowledge acquisition and maintenance are simple tasks, designed to be performed incrementally while the system is in use. Knowledge acquisition, maintenance and inferencing are offered in modes that can be performed reflexively without a knowledge engineer. We further describe the addition of modelling tools to assist the user to reflect on their knowledge for such purposes as critiquing, explanation, “what-if” analysis and tutoring. Our aim is to provide a system that lets the user choose the mode of interaction and view of the knowledge according to the situation in which they find themselves and their own personal preferences.

[1]  C. Geertz Local Knowledge: Further Essays In Interpretive Anthropology , 1983 .

[2]  Donald A. Norman,et al.  Psychology of everyday things , 1990 .

[3]  Ramanathan V. Guha,et al.  CYC: A Midterm Report , 1990, AI Mag..

[4]  Rudi Studer,et al.  KARO: An Integrated Environment for Reusing Ontologies , 1994, EKAW.

[5]  Donald A. Norman,et al.  Cognition in the Head and in the World: An Introduction to the Special Issue on Situated Action , 1993, Cogn. Sci..

[6]  John McCarthy,et al.  Notes on Formalizing Context , 1993, IJCAI.

[7]  P. Compton,et al.  A philosophical basis for knowledge acquisition , 1990 .

[8]  John McDermott,et al.  Preliminary steps toward a taxonomy of problem-solving methods , 1993 .

[9]  L. Suchman Plans and situated actions , 1987 .

[10]  Peter F. Patel-Schneider,et al.  The DARPA Knowledge Sharing Effort: A Progress Report , 1997, KR.

[11]  David Chapman,et al.  Penguins Can Make Cake , 1989, AI Mag..

[12]  William J. Clancey Methodology for building an intelligent tutoring system , 1987 .

[13]  Allen Newell,et al.  The Knowledge Level , 1989, Artif. Intell..

[14]  Alan L. Rector Helping with a Humanly Impossible Task: Integrating Knowledge Based Systems into Clinical Care , 1989, SCAI.

[15]  Ashwin Srinivasan,et al.  Ripple down rules: Turning knowledge acquisition into knowledge maintenance , 1992, Artif. Intell. Medicine.

[16]  Rudolf Wille,et al.  Conceptual Structures of Multicontexts , 1996, ICCS.

[17]  A.S. Philippakis,et al.  Structured what if analysis in DSS models , 1988, [1988] Proceedings of the Twenty-First Annual Hawaii International Conference on System Sciences. Volume III: Decision Support and Knowledge Based Systems Track.

[18]  Franz Schmalhofer,et al.  Beyond the Knowledge Level: Descriptions of Rational Behavior for Sharing and Reuse , 1994, EKAW.

[19]  Brian R. Gaines An Ounce of Knowledge is Worth a Ton of Data: Quantitative studies of the Trade-Off between Expertise and Data Based On Statistically Well-Founded Empirical Induction , 1989, ML.

[20]  Johanna D. Moore,et al.  Explanation in second generation expert systems , 1993 .

[21]  Eleni Stroulia,et al.  Reflective, Self-Adaptive Problem Solvers , 1994, EKAW.

[22]  G. Kelly The Psychology of Personal Constructs , 2020 .

[23]  G Edwards,et al.  Peirs: A pathologist‐maintained expert system for the interpretation of chemical pathology reports , 1993, Pathology.

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

[25]  Edward H. Shortliffe,et al.  An Approach to Verifying Completeness and Consistency in a Rule-Based Expert System , 1982, AI Mag..

[26]  R. Wille Concept lattices and conceptual knowledge systems , 1992 .

[27]  Madgie M. Hunt The Invention of Memory: a New View of the Brain , 1989 .

[28]  Elliot Soloway,et al.  Assessing the Maintainability of XCON-in-RIME: Coping with the Problems of a VERY Large Rule-Base , 1987, AAAI.

[29]  Alun D. Preece,et al.  Exploring the Structure of Rule Based Systems , 1993, AAAI.

[30]  David Chapman,et al.  Pengi: An Implementation of a Theory of Activity , 1987, AAAI.

[31]  Paul Compton,et al.  The (Extensive) Implications of Evaluation on the Development of Knowledge-Based Systems , 1995 .

[32]  Edward H. Shortliffe,et al.  Adapting a Consultation System to Critique User Plans , 1983, Int. J. Man Mach. Stud..

[33]  Herbert A. Simon,et al.  Situated Action: A Symbolic Interpretation , 1993, Cogn. Sci..

[34]  William J. Clancey,et al.  Model Construction Operators , 1992, Artif. Intell..

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

[36]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.

[37]  D. Schoen Educating the reflective practitioner , 1987 .

[38]  Brian R. Gaines,et al.  Comparing the Conceptual Systems of Experts , 1989, IJCAI.

[39]  Joost Breuker,et al.  Components of Problem Solving and Types of Problems , 1994, EKAW.

[40]  Rudolf Wille,et al.  Lattices in Data Analysis: How to Draw Them with a Computer , 1989 .

[41]  M. Weintraub An explanation-based approach to assigning credit , 1991 .

[42]  Byeong Ho Kang,et al.  Verification and validation with ripple-down rules , 1996, Int. J. Hum. Comput. Stud..

[43]  Allen Newell,et al.  SOAR: An Architecture for General Intelligence , 1987, Artif. Intell..

[44]  Terry Winograd,et al.  Understanding computers and cognition - a new foundation for design , 1987 .

[45]  William J. Clancey,et al.  From Guidon to Neomycin and Heracles in Twenty Short Lessons: ORN Final Report 1979-1985 , 1986, AI Mag..

[46]  J. Lave Cognition in Practice: Outdoors: a social anthropology of cognition in practice , 1988 .

[47]  Michael A. Szczepkowski,et al.  Expertise in dynamic, physical task domains , 1997 .

[48]  Z. Pawlak Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .

[49]  Samson W. Tu,et al.  A multiple-method knowledge-acquisition shell for the automatic generation of knowledge-acquisition tools , 1992 .

[50]  Harold J. Steudel,et al.  A Decision-Table-Based Processor for Checking Completeness and Consistency in Rule-Based Expert Systems , 1987, Int. J. Man Mach. Stud..

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

[52]  James G. Greeno,et al.  Situativity and Symbols: Response to Vera and Simon , 1993, Cogn. Sci..

[53]  Ronald J. Brachman,et al.  ON THE EPISTEMOLOGICAL STATUS OF SEMANTIC NETWORKS , 1979 .

[54]  Philip E. Agre,et al.  The Symbolic Worldview: Reply to Vera and Simon , 1993, Cogn. Sci..

[55]  S. Tyler The said and the unsaid : mind, meaning, and culture , 1983 .

[56]  Daniel G. Bobrow,et al.  DARN: Toward a community memory for diagnosis and repair tasks , 1987 .

[57]  William J. Clancey,et al.  Heuristic Classification , 1986, Artif. Intell..

[58]  Randall Davis,et al.  Interactive Transfer of Expertise: Acquisition of New Inference Rules , 1993, IJCAI.

[59]  Bernhard Nebel,et al.  Terminological Cycles: Semantics and Computational Properties , 1991, Principles of Semantic Networks.

[60]  William J. Clancey,et al.  Situated Action: A Neuropsychologiwl Interpretation Response to Vera and Simon , 2005 .

[61]  R. A. Brooks,et al.  Intelligence without Representation , 1991, Artif. Intell..