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.


Acta Psychologica. 99, 235-253, 1998

Implicit Learning of Invariants or Rejection of Least Similar Test Instances? Further Consideration of an Invariant Learning Paradigm

Geoff Ward, Elizabeth Churchill

No information

Journal of the International Computer Chess Association (ICCA), March 1998

The significance of Kasparov versus Deep Blue and the future of computer chess

Dennis DeCoste

In this paper we argue that the recent Garry Kasparov vs. Deep Blue matches are significant for the field of artificial intelligence in several ways, including providing an example of valuable baseline benchmarks for more complex alternatives to contrast and justify themselves.

We will also briefly summarize some of the latest developments on computer chess research and highlight how our own work on a program called Chester tries to build on those developments to provide such justifications.

AAAI Workshop on textual case-based reasoning. 1998

From Text to Cases: Machine Aided Text Categorization for Capturing Business Reengineering Cases

Catherine Baudin, Scott Waterman

Sharing business experience, such as client engagements, proposals or best practices, is an important part of the knowledge management task within large business organizations. While full text search is a first step at accessing textual material describing corporate experience, it does not highlight important concepts and similarities between business practices structured or operated differently.

Conceptual indexing languages, on the other hand, are high level indexing schemes based on taxonomies of domain concepts designed to provide a common language to describe, retrieve, and compare cases.

However, the effective use of these high level languages is limited by the fact that they require users to be able to *describe cases in terms an often large body of controlled vocabulary. The main challenge to using CBR and data mining technology for accessing and analyzing corporate knowledge is not in designing sophisticated inference mechanisms, but is in representing large bodies of qualitative information in textual form for reuse.

This knowledge representation task is the process of mapping textual information to predefined domain models designed by knowledgeable domain experts. We are experimenting with machine aided text categorization technology to support the creation of quality controlled repositories of corporate experience in the business domain.

ACM CSCW 1998 Conference. 1998

Using Filtering Agents to Improve Prediction Quality in the GroupLens Research Collaborative Filtering System

Badrul Sarwar, Joseph Konstan, Al Borchers, Jon Herlocker, Brad Miller, John Riedl

No information

AI Magazine 18(1): Spring 1997

Making an impact: Artificial Intelligence at the Jet Propulsion Laboratory

The National Aeronautics and Space Administration (NASA) is being challenged to perform more frequent and intensive space-exploration missions at greatly reduced cost. Nowhere is this challenge more acute than among robotic planetary exploration missions that the Jet Propulsion Laboratory (JPL) conducts for NASA.

This article describes recent and ongoing work on spacecraft autonomy and ground systems that builds on a legacy of existing success at JPL applying AI techniques to challenging computational problems in planning and scheduling, real-time monitoring and control, scientific data analysis, and design automation.

Proceedings of the Third Conference on Knowledge Discovery and Data Mining (KDD-97), Newport Beach, CA, August 1997

Mining multivariate time-series sensor data to discover behavior envelopes

This paper addresses large-scale regression tasks using a novel combination of greedy input selection and asymmetric cost. Our primary goal is learning envelope functions suitable for automated detection of anomalies in future sensor data.

We argue that this new approach can be more effective than traditional techniques, such as static red-line limits, variance-based error bars, and general probability density estimation.

Proceedings of IJCAI, the International Joint Conference in Artificial Intelligence. 1997

Notes Explorer: Toward Structured Retrieval in Semi-structured Information Spaces

A semi-structured information space consists of multiple collections of textual documents containing fielded or tagged sections. The space can be highly heterogeneous, because each collection has its own schema, and there are no enforced keys or formats for data items across collections.

Thus, structured methods like SQL cannot be easily employed, and users often must make do with only full-text search. In this paper, we describe an intermediate approach that provides structured querying for particular types of entities, such as companies, people, and skills.

Entity-based retrieval is enabled by normalizing entity references in a heuristic, type-dependent manner. To organize and filter search results, entities are categorized as playing particular roles (e.g., company as client, as vendor, etc.) in particular collection types (directories, client engagement records, etc.).

The approach can be used to retrieve documents and can also be used to construct entity profiles - summaries of commonly sought information about an entity based on the documents’ content. The approach requires only a modest amount of meta-information about the source collections, much of which is derived automatically. On a set of typical user queries in a large corporate information space, the approach produces a dramatic improvement in retrieval quality over knowledge-free methods like full-text search.

Proceedings of CIKIM. 1996

Entity-Based Retrieval in Shared Semi-Structured Information Spaces

Semi-structured information sharing systems are gaining in popularity because they allow users to easily create shared collections of textual documents, organized by a common set of fields. Unfortunately, in a large organization this freedom can result in an unwieldy space of shared information that is difficult to retrieve.

Standard tools like full-text search do not alleviate the problem, in part because they do not make any use of the structure within each document collection. In this paper, we describe an approach that goes beyond full-text search by taking advantage of both the structure of the document collections and a knowledge of what information types are important within the organization sharing the information.

We present an implemented indexing/browsing system called Notes Explorer that allows users to browse for entities (companies, people, etc.) across a large semi-structured information space. Notes Explorer incorporates three key components:

(1) automatic classification of document fields to recognize common entity and document collection types;

(2) entity-based browsing over multiple document collections, with type-dependent normalization;


(3) content-based filtering of browse results.

Machine Learning and Knowledge Acquisition. G. Tecuci, Y. Kodratoff. Eds. Academic Press. 1994

Increasing Levels of Assistance in Refinement of Knowledge-based Retrieval Systems (extended)

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

No Information