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.


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.

Proceedings of the 24th International Conference on World Wide Web, 88-96 (2015)

Is Sniping A Problem For Online Auction Markets?

A common complaint about online auctions for consumer goods is the presence of ``snipers,'' who place bids in the final seconds of sequential ascending auctions with predetermined ending times. The literature conjectures that snipers are best-responding to the existence of ``incremental" bidders that bid up to their valuation only as they are outbid. Snipers aim to catch these incremental bidders at a price below their reserve, with no time to respond. As a consequence, these incremental bidders may experience regret when they are outbid at the last moment at a price below their reservation value. We measure the effect of this experience on a new buyer's propensity to participate in future auctions. We show the effect to be causal using a carefully selected subset of auctions from and instrumental variables estimation strategy. Bidders respond to sniping quite strongly and are between 4 and 18 percent less likely to return to the platform.

Studies in Computational Intelligence: Successful Case-Based Reasoning Applications – 1, Volume 305/2 010. P 53-82. Springer

Development of Industrial Knowledge Management Applications with Case-Based Reasoning

Mehmet H.Goker, Catherine Baudin, Michel Manago, Mehmet H.Goker, Catherine Baudin, Michel Manago

The successful development, deployment and utilization of Case-Based Reasoning Systems in commercial environments require the development team to focus on aspects that go beyond the core CBR engine itself. Characteristics of the Users, the Organization and the Domain have considerable impact on the design decisions during implementation and on the success of the project after deployment.

If the system is not technically and organizationally integrated with the operating environment, it will eventually fail. In this chapter, we describe our experiences and the steps we found useful while implementing CBR applications for commercial use. We learned these lessons the hard way. Our goal is to document our experience and help practitioners develop their own approach and avoid making the same mistakes.

Journal of Law, Economics and Organization

Auctions versus Negotiations in Procurement: An Empirical Analysis

Patrick Bajari, Robert McMillan

Should the buyer of a customized good use competitive bidding or negotiation to select a contractor? To shed light on this question, we consider several possible determinants that may influence the choice of auctions versus negotiations. We then examine a comprehensive data set of private sector building contracts awarded in Northern California during the years 1995-2000.

The analysis suggests a number of possible limitations to the use of auctions. Auctions may perform poorly when projects are complex, contractual design is incomplete and there are few available bidders.

Furthermore, auctions may stifle communication between buyers and sellers, preventing the buyer from utilizing the contractor's expertise when designing the project. Some implications of these results for procurement in the public sector are discussed. JEL classifications: D23, D82, H57, L14, L22, L74.