We strongly believe in open source and giving to our community. We work directly with researchers in academia and seek out new perspectives with our intern and fellowship programs. We generalize our solutions and release them to the world as open source projects. We host discussions and publish our results.


Knowledge Acquisition Journal Volume 6. 179-176. 1994

Increasing Levels of Assistance in Refinement of Knowledge-based Retrieval Systems

Catherine Baudin, Smadar Kedar, Barney Pell, Catherine Baudin, Smadar Kedar, Barney Pell

This paper is concerned with the task of incrementally acquiring and refining the knowledge and algorithms of a knowledge-based system in order to improve its performance over time. In particular, we present the design of DE-KART, a tool whose goal is to provide increasing levels of assistance in acquiring and refining indexing and retrieval knowledge for a knowledge-based retrieval system.

DE-KART starts with knowledge that has been entered manually, and increase its level of assistance in acquiring and refining that knowledge, both in terms of the increased level of automation in interacting with users, and in terms of the increased generality of the knowledge.

DE-KART is at the intersection of machine learning and knowledge acquisition: it is a first step towards a system which moves along a continuum from interactive knowledge acquisition to increasingly automated machine learning as it acquires more knowledge and experience.

Electronic J. Diff. Equ., 1993 no. 08, pp. 1-7 (1993)

One-sided Mullins-Sekerka flow does not preserve convexity

The Mullins-Sekerka model is a nonlocal evolution model for hypersurfaces, which arises as a singular limit for the Cahn-Hilliard equation. Assuming the existence of sufficiently smooth solutions we will show that the one-sided Mullins-Sekerka flow does not preserve convexity. The main tool is the strong maximum principle for elliptic second order differential equations.

Proceedings of INTERCHI 93. 1993

Ask How it Works: An Interactive Intelligent Manual for Devices

Smadar Kedar, Catherine Baudin, Lawrence Birnbaum, Richard Osgood, Ray Bareiss, Smadar Kedar, Catherine Baudin, Lawrence Birnbaum, Richard Osgood, Ray Bareiss

We describe Ask How It Works, a prototype interactive intelligent manual for devices, based on novel intelligent training systems called ASK Systems.

Proceedings of the IJCAI 93 International Joint Conference in Artificial Intelligence. 1993

Using Device Models to Facilitate the Retrieval of Multimedia information

Catherine Baudin, Jody Gevins, Vinod Baya, Catherine Baudin, Jody Gevins, Vinod Baya

No Information

Proceedings of the AAAI 93 Conference. Washington DC,1993

Question-based Acquisition of Conceptual Indices for Multimedia Design Documentation

Catherine Baudin, Jody Gevins, Vinod Baya

No information

COGSCI 92, Proceedings of the 14th conference of the Cognitive Science Society. 1992

Dedal: Using Domain Concepts to Index Engineering Design Information

Catherine Baudin, Jody Gevins, V.Baya , Ade Mabogunje

The goal of Dedal is to facilitate the reuse of engineering design experience by providing an intelligent guide for browsing multimedia design documents. Based on protocol analysis of design activities, we defined a language to describe the content and the form of technical documents for mechanical design.

We use this language to index pages of an Electronic Design Notebook which contains text and graphics material, meeting reports and transcripts of conversations among designers. Index and query language with concepts from a model of the designed artifact.

The information retrieval mechanism uses heuristic knowledge from artifact model to help engineers formulate questions, guide the search for relevant information and refine the existing set of indices. Dedal is a compromise between domain-independent argumentation-based systems and pure model-based systems which assume a complete formalization of all design documents.

Proceedings of the Ninth National Conference on Artificial Intelligence (AAAI-91), Los Angeles, CA, July 1991

CATMS: an ATMS which avoids label explosions

J. Collins, Dennis DeCoste

Assumption-based truth maintenance systems have developed into powerful and popular means for considering multiple contexts simultaneously during problem solving. Unfortunately, increasing problem complexity can lead to explosive growth of node labels.

In this paper, we present a new ATMS algorithm (CATMS) which avoids the problem of label explosions, while preserving most of the query time efficiencies resulting from label compilations. CATMS generalizes the standard ATMS subsumption relation, allowing it to compress an entire label into a single assumption.

This compression of labels is balanced by an expansion of environments to include any implied assumptions. The result is a new dimension of flexibility, allowing CATMS to trade-off the query-time efficiency of uncompressed labels against the costs of computing them. To demonstrate the significant computational gains of CATMS over de Kleer’s ATMS,we compare the performance of the ATMS-based QPE [9] problem-solver using each.

Chapter. Knowledge-based Aided Design. Academic Press, 1992

Compiling diagnostic rules and redesign plans from a structure/behavior device model

Richard Keller, Catherine Baudin, Yimi Ywasaki, P. Nayak, Kazuo Tanaka

The current generation of expert systems is fueled by special-purpose, task-specific associational rules developed with the aid of domain experts. In many cases, the expert has distilled or compiled these so-called 'shallow rules from 'deeper' models of the application domain in order to optimize task performance.

With the traditional knowledge engineering approach, only the shallow, special-purpose rules are elicited from the expert - not the underlying domain models upon which they are based. This results in two significant problems.

First, expert systems cannot share knowledge bases because they contain only special-purpose rules and lack the underlying general domain knowledge that applies across tasks. Second, because the underlying models are missing, shallow rules are unsupported and brittle.

This chapter describes a proposed second generation expert system architecture that addresses these problems by linking special-purpose rules to underlying domain models using a process called rule compilation. Rule compilation starts with a detailed domain model, and gradually incorporates various simplifying assumptions and approximations into the model, thereby producing a series of successively less general - but more task-efficient - models of the domain.

The end product of the rule compilation process is an associational rule model specialized for the task at hand.The process of rule compilation is illustrated with two simple implemented examples.

In the first, a structure/behavior model of a simple engineered device is compiled into a set of plans for redesign. In the second, the same underlying device model is compiled into a set of fault localization rules for troubleshooting.

Proceedings of the 4th International Conference on Design Theory and Methodology. 1992

An Experimental Study of Design Information Reuse

Vinod Baya, Jody Gevins, Catherine Baudin, Ade Magogunje, Larry Leifer

We are reporting results and experiences from an experimental study conducted to study the nature of design information reuse during redesign. Starting with a detailed study of the questioning behavior of two designers, we have developed a framework for understanding the character and the basic constitution of information that should be recorded during design for the reuse process to be useful and productive.

This study lays the ground work for future work in recording, characterizing and indexing design information as it is generated during the design process.

1 Introduction Design in any engineering domain is a very complex activity. We as researchers look at this activity from various perspectives such as technical, methodological, social 1 Jody Gevins is a contractor at Sterling software systems and Catherine Baudin at RECOM inc.

Proceedings of the Machine Learning Workshop. 1991

Design Rationale Capture as Knowledge Acquisition Tradeoffs in the Design of Interactive Tools

Thomas Gruber, Catherine Baudin, John Boose, Jay Weber

This paper introduces a panel to be held at the Knowledge Acquisition Track of the Machine Learning Workshop (ML91). This panel will focus on the problem of acquiring design rationale knowledge from humans for later reuse.

The design of tools for design rationale capture reveals several fundamental issues for knowledge acquisition, such as the relationships among formality and expressiveness of representations, and kinds of automated support for elicitation and analysis of knowledge.

This paper sets the background for discussion by identifying dimensions of a design space for design rationale tools, and then includes position statements from each panelist arguing for various positions in this space.