An Adaptive Meeting Scheduling Agent, J William Murdock and Ashok K Goel. Proceedings of the First Asia-Pacific Conference on Intelligent Agent Technology (IAT'99), Hong Kong, December 15-17, 1999. ftp://ftp.cc.gatech.edu/pub/groups/ai/goel/murdock/iat99.ps

An intelligent agent, such as a meeting scheduling system, has a set of constraints which characterizes what that agent can do. However, a dynamic environment may require that a system alter its constraints. If situation-specific feedback is available, a system may be able to adapt by reflecting on its own reasoning processes. Such reflection may be guided not only by explicit representation of the system's constraints but also by explicit representation of the functional role that those constraints play in the reasoning process. We present an operational computer program, SIRRINE2 which uses Task-Method-Knowledge models of a system to reason about traits such as system constraints. We further describe an experiment with SIRRINE2 in the domain of meeting scheduling.

Adaptive Modeling. , Ashok Goel. 1996 International Workshop on Qualitative Reasoning, Monterrey, May 1996. mailto:goel@cc.gatech.edu

Analogical Design: A Model-Based Approach, Bhatta, S., Goel, A. and Prabhakar, S. . AID-94: In Proc. of the Third International Conference on AI in Design, Aug. 1994, Lausanne, Switzerland. ftp://ftp.cc.gatech.edu/pub/ai/students/bhatta/imba-aid94.ps

Analyzing Political Decision Making from an Information Processing Perspective: JESSE, Donald Sylvan, Ashok Goel, and B. Chandrasekaran. American Journal of Political Science, 34(1):74-123, 1990. mailto:goel@cc.gatech.edu

AskJef: Integrating Case-Based and Multimedia Technologies for Interface Design Advising, John Barber, Sambasiva Bhatta, Ashok Goel, Mark Jacobson, Michael Pearce, Louise Penberthy, Murali Shankar, Robert Simpson and Eleni Stroulia. Proc. Second International Conference on Artificial Intelligence in Design, Pittsburgh, June 1992, pp. 457-476. ftp://ftp.cc.gatech.edu/pub/ai/goel/eleni/aid-askjef-new.ps

AskJef is a prototype AI system that helps software engineers in designing human-machine interfaces. It provides a memory of interface design examples, primitive domain objects, and design principles, guidelines, errors and stories. The design examples are represented graphically and decomposed temporally. The different types of knowledge are cross-indexed to enable the designer to navigate through the system's memory. AskJef helps software engineers in (1) understanding interface design problems by illustrating and explaining solutions to similar examples, and (2) comprehending the domain of interface design by illustrating and explaining the use of design guidelines. It uses text, graphics, animation and voice to present relevant information to the designer.

Beyond Domain Knowledge: Towards A Computing Enviornment for the Learning of Design Skills and Strategies, Georgia Tech Cognitive Science Technical Report, 1995. mailto:goel@cc.gatech.edu

Case-Based Decision Support: A Case Study in Architectural Design., Michael Pearce, Ashok Goel, Janet Kolodner, Craig Zimring, Lucas Sentosa and Richard Billington. IEEE Expert, 7(5):14-20, October 1992. mailto:goel@cc.gatech.edu

Case-Based Design: A Task Analysis, Ashok Goel and B. Chandrasekaran. Artificial Intelligence Approaches to Engineering Design, Volume II: Innovative Design, C. Tong and D. Sriram (editors), pp. 165-184, San Diego: Academic Press, 1992. mailto:goel@cc.gatech.edu

A Case-Based Tool for Conceptual Design Problem Solving, Ashok Goel, Janet Kolodner, Michael Pearce, Richard Billington, and Craig Zimring. Proc. Third DARPA Workshop on Case-Based Reasoning, Washington D.C., May 1991, pp. 109-120, Los Altos, CA: Morgan Kaufmann. mailto:goel@cc.gatech.edu

Combining Navigational Planning and Reactive Control, Khaled Ali and Ashok Goel. Proc. AAAI-96 Workshop on Reasoning About Actions, Planning and Control: Bridging the Gap, Portland, August 1996. http://www.cc.gatech.edu/grads/a/Khaled.S.Ali/aaai96_workshop.ps.Z

Complexity in Classificatory Reasoning, Ashok Goel, N. Soundararajan, and B. Chandrasekaran. Proc. Sixth National Conference on Artificial Intelligence (AAAI-87), Seattle, Washington, July 1987,421-425, Los Altos, CA: Morgan Kaufmann. mailto:goel@cc.gatech.edu

Computational Feasibility of Structured Matching, Ashok Goel and Thomas Bylander. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(12):1312-1316, December 1989. mailto:goel@cc.gatech.edu

Computational Trade-Offs in Experience-Based Reasoning, Ashok Goel, Khaled Ali and Andres Gomez. Proc. AAAI-94 Workshop on Evaluating Case-Based Reasoning, Seattle, Washington, July 1994. mailto:goel@cc.gatech.edu

Concurrent Assembly of Composite Explanations, Ashok Goel, John Josephson, and P. Sadayappan. Abductive Inference: Computation, Philosophy, and Technology,J. Josephson and S. Josephson (editors), first part of Chapter 6,pp. 142-156, New York: Cambridge University Press, 1994. mailto:goel@cc.gatech.edu

Concurrent Synthesis of Composite Explanatory Hypotheses. , Ashok Goel, P. Sadayappan, and John Josephson. . Proc. Seventeenth International Conference on Parallel Processing, St. Charles, Illinois, August 1988, Vol. III, pp. 156-160. mailto:goel@cc.gatech.edu

Connectionism and Information Processing Abstractions: The Message Still Counts More Than the Medium, B. Chandrasekaran, Ashok Goel, and Dean Allemang. AI Magazine, 9(4):24-34, Winter 1988. mailto:goel@cc.gatech.edu

A Control Architecture for Model-Based Redesign Problem Solving, Ashok Goel and Sattiraju Prabhakar . Proc. IJCAI-1991 Workshop on AI in Design Sydney, Australia, August 1991, pp. 121-136. mailto:goel@cc.gatech.edu

A Control Architecture for Redesign and Design Verification, Ashok Goel and Sattiraju Prabhakar. Proc. 1994 Australian-New Zealand Intelligent Information Systems Conference, Brisbane,Queensland, Australia, Nov. 29 - Dec 2, 1994, pp. 377 - 381. mailto:goel@cc.gatech.edu

A Control Architecture for Run-Time Method Selection, Ashok Goel and Todd Callantine. Proc. AAAI 1991 Workshop on Cooperation Among Heterogeneous Intelligent Systems Anaheim, California, July 1991. mailto:goel@cc.gatech.edu

A Cross-Domain Experiment in Case-Based Design Support: ArchieTutor, Ashok Goel, Michael Pearce, Ali Malkawi, and Kim Liu. Proc. AAAI-93 Workshop on Case-Based Reasoning, July 1993, pp. 111-117. mailto:goel@cc.gatech.edu

Design, Analogy, and Creativity, Ashok K. Goel. To appear in IEEE Expert Special Issue on AI in Design. ftp://ftp.cc.gatech.edu/pub/groups/ai/goel/ieeexpert97.ps

Analogical reasoning appears to play a key role in creative design. This article provides a brief overview of recent AI research on analogy-based creative design. It begins with an examination of characterizations of creative design. Then it analyzes theories of analogical design in terms of four questions: why, what, how, and when. Next it briefly describes three recent AI theories of analogy-based creative design: SYN [Borner et al 1996], DSSUA [Qian and Gero 1992], and IDEAL [Bhatta 1995]. Finally it enumerates a set of research issues in analogy-based creative design.

Discovery of Physical Principles from Design Experiences, S. Bhatta and A. Goel. In a special issue on ``Machine Learning in Design'' of the International Journal of AI EDAM (AI for Engineering Design, Analysis, and Manufacturing), 8(2), Spring. ftp:://ftp.cc.gatech.edu/pub/ai/students/bhatta/dp-aiedam94.ps

Efficiency of Essentials-First Strategy for Assembling Composite Explanations, Ashok Goel, Jack Smith and John Svirbely, & Olivier Fischer. Abductive Inference: Computation, Philosophy, and Technology,J. Josephson and S. Josephson (editors), second part of Chapter 6, pp. 142-156, New York: Cambridge University Press, 1994. mailto:goel@cc.gatech.edu

An Experience-Based Approach to Navigational Path Planning, Proc. IEEE/RSJ International Conference on Robotics and Systems,RayleAigh, North Carolina, July 1992, Volume II, pp. 705-710,IEEE Press. mailto:goel@cc.gatech.edu

Explanatory Interface in Interactive Design Environments, Ashok K. Goel, Andres Gomez de Silva Garza, Nathalie Grue, J. William Murdock, Margaret M. Recker, and T. Govindaraj. Fourth International Conference on AI in Design, Stanford University, June 1996. ftp://ftp.cc.gatech.edu/pub/groups/ai/goel/murdock/aid96.ps

Explanation is an important issue in building computer-based interactive design environments in which a human designer and a knowledge system may cooperatively solve a design problem. We consider the two related problems of explaining the system's reasoning and the design generated by the system. In particular, we analyze the content of explanations of design reasoning and design solutions in the domain of physical devices. We describe two complementary languages: task-method-knowledge models for explaining design reasoning, and structure-behavior-function models for explaining device designs. Interactive Kritik is a computer program that uses these representations to visually illustrate the system's reasoning and the result of a design episode. The explanation of design reasoning in Interactive Kritik is in the context of the evolving design solution, and, similarly, the explanation of the design solution is in the context of the design reasoning.

From Data to Knowledge: Method Specific Transformations, M. Jeff Donahoo, J. William Murdock, Ashok K. Goel, Sham Navathe, and Edward Omiecinski. Proccedings of the 1997 International Symposium Symposium of Methodologies for Intelligent Systems.. ftp://ftp.cc.gatech.edu/pub/groups/ai/goel/murdock/ismis97.ps

Generality and scale are important but difficult issues in knowledge engineering. At the root of the difficulty lie two hard questions: how to accumulate huge volumes of knowledge, and how to support heterogeneous knowledge and processing? One answer to the first question is to reuse legacy knowledge systems, integrate knowledge systems with legacy databases, and enable sharing of the databases by multiple knowledge systems. We present an architecture called HIPED for realizing this answer. HIPED converts the second question above into a new form: how to convert data accessed from a legacy database into a form appropriate to the processing method used in a legacy knowledge system? One answer to this reformed question is to use method-specific transformation of data into knowledge. We describe an experiment in which a legacy knowledge system called Interactive Kritik is integrated with an ORACLE database using IDI as the communication tool. The experiment indicates the computational feasibility of method-specific data-to-knowledge transformations.

From Design Cases to Generic Mechanisms, Sambasiva Bhatta and Ashok Goel. To appear in Artificial Intelligence in Engineering Design,Analysis and Manufacturing, Special Issue on Machine Learning, Vol. 10, in press.. mailto:goel@cc.gatech.edu

From Design Experiences to Generic Mechanisms: Model-Based Learning in Analogical Design, S. Bhatta and A. Goel. In Proceedings of the AID-94 workshop on Machine Learning in Design, Aug. 1994, Lausanne, Switzerland. ftp://ftp.cc.gatech.edu/pub/ai/students/bhatta/detogm-mld94.ps

From Models to Cases: where do cases come from and what happens when a case is not available? , Ashok Goel, Andres Gomez, Todd Callantine, Michael Donnellan, and Juan Santamaria. Proc. Fifteenth Annual Conference of the Cognitive Science Society, Boulder, Colorado, July 1993, pp. 474-480, Hillsdale, NJ: Lawrence Erlbaum. mailto:goel@cc.gatech.edu

From Numbers to Symbols to Knowledge Structures: Artificial Intelligence Perspectives on the Classification Task, B. Chandrasekaran and Ashok Goel. IEEE Transactions on Systems, Man, and Cybernetics, 18(3):415-424, May/June, 1988. mailto:goel@cc.gatech.edu

A Functional Approach to Program Understanding, Eleni Stroulia and Ashok Goel. Proc. AAAI-92 workshop on AI and Automated Program Understanding, San Jose, July 1992, pp. 120-124. mailto:goel@cc.gatech.edu

Functional Explanations in Design, Andres Gomez de Silva Garza, Nathalie Grue, J. William Murdock, Margaret M. Recker. To appear in IJCAI-97 Workshop on Modeling and Reasoning. about Function ftp://ftp.cc.gatech.edu/pub/ai/goel/murdock/ijcai97fr.ps

A key step in explaining how something works is explaining what that thing was intended to do. This is equally true of physical devices and of abstract devices such as knowledge systems. In this paper, we consider the problem of providing functionally oriented explanations of a knowledge-based design system. In particular, we analyze the content of explanations of reasoning in the context of the design of physical devices. We describe a language for expressing explanations: task-method-knowledge models. Additionally, we describe the Interactive Kritik system, a computer program that makes use of these representations to visually illustrate the system's reasoning.

Functional Models and Model-Based Diagnosis in Adaptive Design, Ashok Goel and Eleni Stroulia. To appear in Artificial Intelligence for Engineering Design,Analysis and Manufacturing, Special Issue on Functional Representation and Reasoning, 1996. mailto:goel@cc.gatech.edu

Functional Reasoning about Devices with Fields and Cycles, Ashok Goel, Eleni Stroulia and Kai Yeung Luk. Proc. AAAI-94 Workshop on Representation and Reasoning about Function, Seattle, Washington, July 1994. mailto:goel@cc.gatech.edu

Functional Reasoning for Design and Diagnosis. , Jon Sticklen, Ashok Goel, B. Chandrasekaran, and William Bond. Proc. Second International Workshop on Model-Based Diagnosis, Paris, France, July 1989, Los Altos, CA: Morgan Kaufmann. mailto:goel@cc.gatech.edu

Functional Representation and Reasoning in Reflective Systems., E. Stroulia and A. Goel. Applied Artificial Intelligence: An International Journal, Special Issue on Functional Reasoning, Vol. 9, No. 1, pp. 101-124. mailto:goel@cc.gatech.edu

Functional models have been extensively investigated in the context of several problem-solving tasks such as device diagnosis and design. In this paper, we view problem solvers themselves as devices, and use structure-behavior-function models to represent how they work. The model representing the functioning of a problem solver explicitly specifies how the knowledge and reasoning of the problem solver result in the achievement of its goals. Then, we employ these models for performance-driven reflective learning. We view performance-driven learning as the task of redesigning the knowledge and reasoning of the problem solver to improve its performance. We use the model of the problem solver to monitor its reasoning, assign blame when it fails, and appropriately redesign its knowledge and reasoning. This paper focuses on the model-based redesign of a path planner's task structure. It illustrates the model-based reflection using examples from an operational system called Autognostic system.

Functional Representation as a Basis for Design Rationale., B. Chandrasekaran, Ashok Goel and Yumi Iwasaki. . IEEE Computer, 26(1):48-56, January 1993. mailto:goel@cc.gatech.edu

Functional Representation of Designs and Redesign Problem Solving, Ashok Goel and B. Chandrasekaran. Proc. Eleventh International Joint Conference on Artificial Intelligence (IJCAI-89),Detroit, Michigan, August, 1989, pp. 1388-1394, Los Altos,California: Morgan Kaufmann Publishers. mailto:goel@cc.gatech.edu

Generic Teleological Mechanisms and their Use in Case Adaptation, E. Stroulia and A. Goel. Proceedings of CogSci'92. ftp://ftp.cc.gatech.edu/pub/ai/goel/eleni/cogsci92.ps

In experience-based (or case-based) reasoning, new problems are solved by retrieving and adapting the solutions to similar problems encountered in the past. An important issue in experience-based reasoning is to identify different types of knowledge and reasoning useful for different classes of case-adaptation tasks. In this paper, we examine a class of non-routine case-adaptation tasks that involve patterned insertions of new elements in old solutions. We describe a model-based method for solving this task in the context of the design of physical devices. The method uses knowledge of generic teleological mechanisms (GTMs) such as cascading. Old designs are adapted to meet new functional specifications by accessing and instantiating the appropriate GTM. The Kritik2 system evaluates the computational feasibility and sufficiency of this method for design adaptation.

Grounding Case Adaptation in Causal Models. , Ashok Goel. Methodologies for Intelligent Systems --- 5, Z. Ras, M. Zemenkova,and M. Emrich (editors), Amsterdam, Netherlands: North Holland, 1990,pp. 260-267. mailto:goel@cc.gatech.edu

Innovation in Analogical Design: A Model-Based Approach, Sambasiva Bhatta, Ashok Goel and Sattiraju Prabhakar. In Proc. Third International Conference on Artificial Intelligence in Design, Lausanne, Switzerland, August 1994, pp. 57-74. mailto:goel@cc.gatech.edu

An Integrated Experience-Based Approach to Navigational Path Planning for Automonous Mobile Robotics, Ashok Goel, Michael Donnellan, Nancy Vasquez, and Todd Callantine. . Proc. IEEE International Conference on Robotics and Automation, Atlanta, Georgia, May 1993,pp. 818-825, IEEE Press. mailto:goel@cc.gatech.edu

Integrating Artificial Intelligence and Multimedia Technologies for Interface Design Advising, John Barber, Mark Jacobson, Louise Penberthy, Robert Simpson, Sambasiva Bhatta, Ashok Goel, Michael Pearce, Murali Shankar, and Eleni Stroulia. NCR Journal of Research and Development, 6(1):75-85, October 1992. mailto:goel@cc.gatech.edu

Integrating Case-Based and Model-Based Reasoning: A Computational Model of Design Problem Solving, Ashok Goel. AI Magazine, 13(2):50-54, Summer 1992. mailto:goel@cc.gatech.edu

Integrating Case-Based and Model-Based Reasoning for Creative Design:Constraint Discovery, Model Revision and Case Composition, Sattiraju Prabhakar and Ashok Goel. Proc. Second International Conference on Computational Models of Creative Design, Heron Island,Australia, December 1992, pp. 101-127. mailto:goel@cc.gatech.edu

JESSE: An Information Processing Model of Policy Decision Making, Ashok Goel, B. Chandrasekaran and Donald Sylvan. Proc. IEEE Third Annual AI Systems in Government Conference Washington, D. C.,October 1987, pp. 178-87, IEEE Computer Society Press. mailto:goel@cc.gatech.edu

KA: Integrating natural language understanding with design problem solving, Kavi Mahesh, Justin Peterson, Ashok Goel, Kurt P. Eiselt. In Working Notes from the AAAI Spring Symposium on Active NLP: Natural Language Understanding in Integrated Systems. ftp://ftp.cc.gatech.edu/pub/ai/eiselt/er-km-94-01.ps.Z

In this article, we present our research on the integration of natural language understanding and problem solving capabilities in the context of the design of physical devices. We describe an experimental integrated system called KA [Goel and Eiselt, 1991; Pittges et al, 1993] that illustrates some of the benefits of building an integrated theory of multiple cognitive tasks focusing on language u nderstanding and its interaction with design problem solving. We show for example how our work on KA imposed constraints on the target representation of natural language understanding and how the integrated approach redefined classical problems in language processing such as ambiguity and underspecification in terms of the overall goals of the KA system. Language understanding imposed constraints, in return, on the task structure of the design problem solver.

KA: Situating Natural Language Processing in Design Problem Solving, Justin Peterson, Kavi Mahesh, Ashok Goel, and Kurt Eiselt. Proc. Sixteenth Annual Conference of the Cognitive Science Society,August 1994, Atlanta, Georgia, pp. 711-716, Hillsdale, NJ:Lawrence Erlbaum. mailto:goel@cc.gatech.edu

A Knowledge-based Selection Mechanism for Strategic Control with Application in Design, Diagnosis and Planning, William Punch, Ashok Goel and David Brown.. International Journal of Artificial Intelligence Tools, Vol. 4 (3), pp 323-348, 1996. mailto:goel@cc.gatech.edu

Knowledge Compilation: A Symposium, Ashok Goel, Tom Bylander, B. Chandrasekaran, Thomas Dietterich, Richard Keller, and Chris Tong. IEEE Expert, 6(2):71-93, April 1991. mailto:goel@cc.gatech.edu

Kritik: An Early Case-Based Design System, A. Goel, S. Bhatta, E. Stroulia. Mary Lou Maher, Pearl Pu (eds.) Issues and Applications of Case-Based Reasoning to Design. Lawrence Erlbaum associates, 1997.. ftp://ftp.cc.gatech.edu/pub/ai/goel/kritik.ps

Learning About Novel Operating Environments: Designing by Adaptive Modelling, Sattiraju Prabhakar and Ashok Goel. To appear in Artificial Intelligence in Engineering Design,Analysis and Manufacturing, Special Issue on Machine Learning, Vol. 10, in press.

Learning Generic Mechanisms from Experiences for Analogical Reasoning, Bhatta, S. and Goel, A. . In Proceedings of the Fifteenth Annual Conference of the Cognitive Science Society. ftp://ftp.cc.gatech.edu/pub/ai/students/bhatta/lgm-cogsci93.ps

Learning in Parallel Distributed Processing: Computational Complexity and Information Content, John Kolen and Ashok Goel. IEEE Transactions on Man, Systems, and Cybernetics, 21(2):359-367,March/April 1991. mailto:goel@cc.gatech.edu

Learning Problem-Solving Concepts by Reflecting on Problem Solving, E. Stroulia and A. Goel. Proc. 1994 European Conference on Machine Learning, Catania, Italy, April 1994, pp. 287-306, Available as Lecture Notes in Artificial Intelligence 784 - Machine Learning, F. Bergadano and L. De Raedt (editors), Berlin: Springer-Verlag, 1994. ftp://ftp.cc.gatech.edu/pub/ai/goel/eleni/ecml.ps

Learning and problem solving are intimately related: problem solving determines the knowledge requirements of the reasoner which learning must fulfill, and learning enables improved problem-solving performance. Different models of problem solving, however, recognize different knowledge needs, and, as a result, set up different learning tasks. Some recent models analyze problem solving in terms of generic tasks, methods, and subtasks. These models require the learning of problem-solving concepts such as new tasks and new task decompositions. We view reflection as a core process for learning these problem-solving concepts. In this paper, we identify the learning issues raised by the task-structure framework of problem solving. We view the problem solver as an abstract device, and represent how it works in terms of a structure-behavior-function model which specifies how the knowledge and reasoning of the problem solver results in the accomplishment of its tasks. We describe how this model enables reflection, and how model-based reflection enables the reasoner to adapt its task structure to produce solutions of better quality. The Autognostic system illustrates this reflection process.

Manufacturing Diagnosis and Control: A Task-Specific AI Approach, William Punch, Ashok Goel, and Jon Sticklen. Intelligent Modeling, Diagnosis and Control of Manufacturing Processes, B. Chu and S. Chen (editors), Singapore: World Scientific Press, 1992, Chapter 1, pp. 1-32. mailto:goel@cc.gatech.edu

Mental Models, Natural Language, and Knowledge Acquisition, Ashok Goel and Kurt Eiselt. ACM SIGART, Special Issue on Integrated Cognitive Architectures, 2(4): 75:78, August 1991. mailto:goel@cc.gatech.edu

Meta-Cases: Explaining Case-Based Reasoning, Ashok Goel and J. William Murdock. Proc. 3rd European Workshop on Case-Based Reasoning, November 1996. ftp://ftp.cc.gatech.edu/pub/ai/goel/murdock/ewcbr96.ps

AI research on case-based reasoning has led to the development of many laboratory case-based systems. As we move towards introducing these systems into work environments, explaining the processes of case-based reasoning is becoming an increasingly important issue. In this paper we describe the notion of a meta-case for illustrating, explaining and justifying case-based reasoning. A meta-case contains a trace of the processing in a problem-solving episode, and provides an explanation of the problem-solving decisions and a (partial) justification for the solution. The language for representing the problem-solving trace depends on the model of problem solving. We describe a task-method-knowledge (TMK) model of problem-solving and describe the representation of meta-cases in the TMK language. We illustrate this explanatory scheme with examples from Interactive Kritik, a computer-based design and learning environment presently under development.

Method Specific Knowledge Compilation: Towards Practical Design Support Systems, J. William Murdock, Ashok K. Goel, M. Jeff Donahoo, and Sham Navathe. Proceedings of the Fifth International Conference on Artificial Intelligence and Design (AID'98), Lisbon, Portugal, July 20-23, 1998. ftp://ftp.cc.gatech.edu/pub/ai/goel/murdock/aid98.ps

Modern knowledge systems for design typically employ multiple problem-solving methods which in turn use different kinds of knowledge. The construction of a heterogeneous knowledge system that can support practical design thus raises two fundamental questions: how to accumulate huge volumes of design information, and how to support heterogeneous design processing? Fortunately, partial answers to both questions exist separately. Legacy databases already contain huge amounts of general-purpose design information. In addition, modern knowledge systems typically characterize the kinds of knowledge needed by specific problem-solving methods quite precisely. This leads us to hypothesize method-specific data-to-knowledge compilation as a potential mechanism for integrating heterogeneous knowledge systems and legacy databases for design. In this paper, first we outline a general computational architecture called HIPED for this integration. Then, we focus on the specific issue of how to convert data accessed from a legacy database into a form appropriate to the problem-solving method used in a heterogeneous knowledge system. We describe an experiment in which a legacy knowledge system called {\ik} is integrated with an ORACLE database using IDI as the communication tool. The limited experiment indicates the computational feasibility of method-specific data-to-knowledge compilation, but also raises additional research issues.

A Model-Based Approach to Analogical Reasoning and Learning in Design, S. Bhatta. College of Computing Technical Report, Georgia Institute of Technology (PhD thesis proposal). ftp://ftp.cc.gatech.edu/pub/tech_reports/1992/GIT-CC-92-60.ps.Z

A Model-Based Approach to Blame Assignment: Revising the Reasoning Steps of Problem Solvers, Eleni Stroulia and Ashok Goel. To appear in Proc. National Conference on Artificial Intelligence - AAAI96,Portland, Oregon, August 1996. mailto:goel@cc.gatech.edu

A Model-Based Approach to Blame Assignment in Design, E. Stroulia, M. Shankar, A. Goel, and L. Penberthy. Proceedings of AID'92. ftp://ftp.cc.gatech.edu/pub/ai/goel/eleni/aid-ba-new.ps

We analyze the blame-assignment task in the context of experience-based design and redesign of physical devices. We identify three types of blame-assignment tasks that differ in the types of information they take as input: the design does not achieve a desired behavior of the device, the design results in an undesirable behavior, a specific structural element in the design misbehaves. We then describe a model-based approach for solving the blame-assignment task. This approach uses structure-behavior-function models that capture a designer's comprehension of the way a device works in terms of causal explanations of how its structure results in its behaviors. We also address the issue of indexing the models in memory. We discuss how the three types of blame-assignment tasks require different types of indices for accessing the models. Finally we describe the KRITIK2 system that implements and evaluates this model-based approach to blame assignment.

A Model-Based Approach to Case Adaptation, Proc. Thirteenth Annual Conference of the Cognitive Science Society, Chicago, August 1991, pp. 143-148, Hillsdale, NJ: Lawrence Erlbaum. mailto:goel@cc.gatech.edu

A Model-Based Approach to Redesign, Ashok Goel, Andres Gomez, Jeffrey Pittges, Murali Shankar and Eleni Stroulia. Proc. Thirteenth SPIE Knowledge Based Systems Conference,Orlando, April 1994, pp. 164-171, SPIE Press. mailto:goel@cc.gatech.edu

A Model-Based Approach to Redesign, Ashok Goel, Andres Gomez, Jeffrey Pittges, Murali Shankar and Eleni Stroulia. Proc. Thirteenth SPIE Knowledge Based Systems Conference, Orlando, April 1994,pp. 164-171, SPIE Press. mailto:goel@cc.gatech.edu

Model-Based Discovery of Physical Principles from Design Experiences, Sambasiva Bhatta and Ashok Goel. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, Special Issue on Machine Learning in Design, 8(2):113-123, May 1994. mailto:goel@cc.gatech.edu

Model-Based Indexing and Index Learning in Analogical Design, Sambasiva Bhatta and Ashok Goel. Proc. 1995 Seventeenth Annual Conference of the Cognitive Science Society, Pittsburgh, July 22-25, 1995, NJ, Hillsdale: Erlbaum. mailto:goel@cc.gatech.edu

Model-Based Indexing and Index Learning in Case-Based Design, Sambasiva Bhatta and Ashok Goel. To appear in International Journal of Engineering Applications of Artificial Intelligence,special issue on Machine Learning in Engineering. mailto:goel@cc.gatech.edu

Model-Based Learning of Structural Indices to Design Cases, S. Bhatta and A. Goel. In Proc. of IJCAI-93 workshop on ``Reuse of Designs: An Interdisciplinary Cognitive Approach''. ftp://ftp.cc.gatech.edu/pub/ai/students/bhatta/mblsi-ijcai93-ws.ps

A Model-Based Theory of Adaptive Design for New Operating Environments, Sattiraju Prabhakar, Ashok Goel and Sambasiva Bhatta. the Proc. Third International Conference on Computational Models. of Creative Design, Heron Island, Australia, December 1995, pages 267-301 mailto:goel@cc.gatech.edu

Modeling Foreign Policy Decision Making as Knowledge-Based Reasoning, Donald Sylvan, Ashok Goel, and B. Chandrasekaran. Artificial Intelligence and International Politics, pp.245-273, V. Hudson (editor), Boulder, Colorado: Westview Press, 1991. mailto:goel@cc.gatech.edu

Model Revision: A Theory of Incremental Model Learning, Ashok Goel. Proc. Eighth International Conference on Machine Learning, Chicago, June 1991, pp. 605-609, Los Altos, CA: Morgan Kaufmann. mailto:goel@cc.gatech.edu

Multistrategy Adaptive Navigational Path Planning, Ashok Goel, Khaled Ali, Michael Donnellan, Andres Gomez and Todd Callantine. IEEE Expert, 9(6):57-65, December 1994. mailto:goel@cc.gatech.edu

Multistrategy Language Understanding for Device Comrehension, Ashok Goel, Kavi Mahesh, Justin Peterson and Kurt Eiselt. Proc. 1996 Cognitive Science Conference, San Diego, July 1996.. mailto:goel@cc.gatech.edu

A Neural Architecture for a Class of Abduction Problems, Ashok Goel and J. Ramanujam. To appear in IEEE Transactions on Systems, Man and Cybernetics. mailto:goel@cc.gatech.edu

Practical Abduction: Characterization, Decomposition and Distribution, Ashok Goel, John Josephson, Olivier Fischer and P. Sadayappan. Journal of Experimental and Theoretical Artificial Intelligence, 7(1995):429-450. mailto:goel@cc.gatech.edu

Recompositional Analogy: A Model-Based Approach to Design Reuse, Ashok Goel. Proc. AAAI Spring Symposium on Computational Support for Incremental Modification and Reuse, Palo Alto, March 1992, pp. 116-121. mailto:goel@cc.gatech.edu

Reflective Self-Adaptive Problem Solvers, Eleni Stroulia and Ashok Goel. Proc. 1994 European Conference on Knowledge Acquisition,Germany, September 1994; available in book form as "A Future for Knowledge Acquisition" Luc Steels, Guus Schreiber and Walter Van de Velde (editors), Berlin: Springer-Verlag, 1994. mailto:goel@cc.gatech.edu

Representation, Organization, and Use of Topographic Models of Physical Spaces for Route Planning, Ashok Goel, Todd Callantine, Murali Shankar, and B. Chandrasekaran. Proc. the Seventh IEEE Conference on Artificial Intelligence Applications, Miami Beach, Florida, February 1991, pp. 308-314, IEEE Computer Society Press. mailto:goel@cc.gatech.edu

Representation of Design Functions in Experience-Based Design, Ashok Goel. Intelligent Computer Aided Design, D. Brown, M. Waldron, and H. Yoshikawa (editors), pp. 283-308, Amsterdam, Netherlands: North-Holland, 1992. mailto:goel@cc.gatech.edu

The Role of Essential Explanations in Abduction, Olivier Fischer, Ashok Goel, John Svirbely, and Jack Smith. Artificial Intelligence in Medicine, 3(1991):181-191, 1991. mailto:goel@cc.gatech.edu

The Role of Generic Models in Conceptual Change, Todd W. Griffith, Nancy J. Nersessian, and Ashok Goel. In Proc. of the Eighteenth Annual Conference of the Cognitive Science Society. ftp://ftp.cc.gatech.edu/pub/ai/goel/griffith/cogsci96.ps

We hypothesize generic models to be central in conceptual change in science. This hypothesis has its origins in two theoretical sources. The first source, constructive modeling, derives from a philosophical theory that synthesizes analyses of historical conceptual changes in science with investigations of reasoning and representation in cognitive psychology. The theory of constructive modeling posits generic mental models as productive in conceptual change. The second source, adaptive modeling, derives from a computational theory of creative design. Both theories posit situation independent domain abstractions, i.e. generic models. Using a constructive modeling interpretation of the reasoning exhibited in protocols collected by John Clement (1989) of a problem solving session involving conceptual change, we employ the resources of the theory of adaptive modeling to develop a new computational model, ToRQUE. Here we describe a piece of our analysis of the protocol to illustrate how our synthesis of the two theories is being used to develop a system for articulating and testing ToRQUE. The results of our research show how generic modeling plays a central role in conceptual change. They also demonstrate how such an interdisciplinary synthesis can provide significant insights into scientific reasoning.

Situating Natural Language Understanding in Experience-Based Design, Justin Peterson, Kavi Mahesh and Ashok Goel. International Journal of Human-Computer Studies, 41: 881-913, 1994. mailto:goel@cc.gatech.edu

Some Experimental Results in Multistrategy Navigation Planning, Ashok K. Goel, Khaled S. Ali, and Eleni Stroulia. GIT-CC-95-51. ftp://ftp.cc.gatech.edu/pub/tech_reports/1995/GIT-CC-95-51.ps.Z

Spatial navigation is a classical problem in AI. In this paper, we examine three specific hypotheses regarding multistrategy navigation planning in visually engineered physical spaces containing discrete pathways: (1) For hybrid robots capable of both deliberative planning and situated action, qualitative representations of topological knowledge are sufficient for enabling effective spatial navigation; (2) For deliberative planning, the case-based strategy of plan reuse generates plans more efficiently than the model-based strategy of search without any loss in the quality of plans or problem-solving coverage; and (3) For the strategy of model-based search, the ``principle of locality'' provides a productive basis for partitioning and organizing topological knowledge. We describe the design of a multistrategy navigation planner called Router that provides an experimental testbed for evaluating the three hypotheses. We also describe the embodiment of Router on a mobile robot called Stimpy for testing the first hypothesis. Experiments with Stimpy indicate that this hypothesis apparently is valid for hybrid robots in visually engineered navigation spaces containing discrete pathways such as office buildings. In addition, two different kinds of simulation experiments with Router indicate that the second and the third hypotheses are only partially correct. Finally, we relate the evaluation methods and experimental designs with the research hypotheses.

Structured Matching: A Task-Specific Technique for Making Decisions, Thomas Bylander, Todd Johnson, and Ashok Goel. Knowledge Acquisition, 3(1):1-20, 1991. mailto:goel@cc.gatech.edu

A Task Structure for Case-Based Design , Ashok Goel and B. Chandrasekaran. Proc. 1990 IEEE International Conference on Systems, Man, and Cybernetics, Los Angeles, California, November 1990, pp. 587-592, IEEE Systems, Man, and Cybernetics Society Press. mailto:goel@cc.gatech.edu

Task Structures: What to Learn? , Eleni Stroulia and Ashok Goel. Proc. AAAI-94 Spring Symposium on Goal-Directed Learning,Stanford University, March 1994. mailto:goel@cc.gatech.edu

Teaching Introductory Artificial Intelligence: A Design Stance, Ashok Goel. Proc. 1994 AAAI Fall Symposium on Improving Introductory Instruction of Artificial Intelligence, New Orleans,November 1994. mailto:goel@cc.gatech.edu

Towards a Neural Architecture for Abductive Reasoning, Ashok Goel, J. Ramanujam, and P. Sadayappan. Proc. Second IEEE International Conference on Neural Networks, San Diego, California, July 1988, Vol. II, pp. 681-688, IEEE Press. mailto:goel@cc.gatech.edu

Towards Adaptive Web Agents, J. William Murdock and Ashok K. Goel. Proceedings of the Fourteenth IEEE International Conference on Automated Software Engineering (ASE'99), Cocoa Beach, FL, October 12-15, 1999. ftp://ftp.cc.gatech.edu/pub/groups/ai/goel/murdock/ase99.ps

There is an increasingly large demand for software systems which are able to operate effectively in dynamic environments. In such environments, automated software engineering is extremely valuable since a system needs to evolve in order to respond to changing requirements. One way for software to evolve is for it to reflect upon a model of its own design. A key challenge in reflective evolution is credit assignment: given a model representing the design elements of a complex system, how might that system localize, identify and prioritize prospective candidates for potential modification. We describe a model-based credit assignment mechanism. We also report on an experiment on evolving the design of Mosaic 2.4, an early network browser.

Towards Design Learning Environments - I: Exploring How Devices Work , Ashok K. Goel, Andres Gomez de Silva Garza, Nathalie Grue, J. William Murdock, Margaret M. Recker, T. Govindaraj . Third International Conference on Intelligent Tutoring Systems, Universite de Montreal, June 1996. ftp://ftp.cc.gatech.edu/pub/groups/ai/goel/murdock/its96.ps

Knowledge-based support for learning about physical devices is a classical problem in research on intelligent tutoring systems (ITS). The large amount of knowledge engineering needed, however, presents a major difficulty in constructing ITS's for learning how devices work. Many knowledge-based design systems, on the other hand, already contain libraries of device designs and models. This provides an opportunity for reusing the legacy device libraries for supporting the learning of how devices work. We report on an experiment on the computational feasibility of this reuse of device libraries. In particular, we describe how the structure-behavior-function (SBF) device models in an autonomous knowledge-based design system called Kritik enable device explanation and exploration in an interactive design and learning environment called Interactive Kritik.

Tractable Abduction, John Josephson and Ashok Goel. Abductive Inference: Computation, Philosophy, and Technology,J.Josephson and S. Josephson (editors), Chapter 9, pp. 202-215, New York: Cambridge University Press, 1994. mailto:goel@cc.gatech.edu

Trade-Offs in Acquiring Problem-Decomposition Knowledge: Some Experiments with the Principle of Locality, Eleni Stroulia and Ashok Goel. Proc. Eighth Knowledge Acquisition Workshop Banff,Canada, January 1994, pp. 18(1)-18(20). mailto:goel@cc.gatech.edu

Unification of Language Understanding, Device Comrehension and Knowledge Acquisition, Ashok Goel, Kavi Mahesh, Justin Peterson and Kurt Eiselt. Proc. 1996 Cognitive Science Conference, San Diego, July 1996. mailto:goel@cc.gatech.edu

Use of Device Models in Adaptation of Design Cases, Ashok Goel and B. Chandrasekaran. Proc. Second DARPA Case-Based Reasoning Workshop, Pensacola, Florida, May 1989, pp. 100-109, Los Altos, CA:Morgan Kaufmann. mailto:goel@cc.gatech.edu

Use of Diagnostic Experiences in Experience-Based Innovative Design, Sattiraju Prabhakar and Ashok Goel. Proc. Tenth SPIE Conference on Applications of AI: Knowledge-Based Systems, Orlando,Florida, April 1992, pp. 420-434, SPIE Press. mailto:goel@cc.gatech.edu

Use of Mental Models for Constraining Index Learning in Experience-Based Design, S. Bhatta and A. Goel. In Proc. of AAAI-92 workshop on ``Constraining Learning with Prior Knowledge''. ftp://ftp.cc.gatech.edu/pub/ai/students/bhatta/umil-aaai92-ws.ps

Viewing Nation-States as Cognitive Agents, Ashok Goel, Donald Sylvan and B. Chandrasekaran. Journal of Experimental and Theoretical Artificial Intelligence. mailto:goel@cc.gatech.edu

Virtual Prototyping for Product Demanufacture and Service Using a Virtual Design Studio Approach, Proc. 1995 ASME Computers in Engineering Conference, Boston, pp. 951-958, 1995. mailto:goel@cc.gatech.edu

What is Abductive Reasoning? , Ashok Goel and Gerard Montgomery. Neural Network Review, 3(4):181-187, June 1990. mailto:goel@cc.gatech.edu