This report investigates some techinques appropriate to representing the knowledge necessary for understanding a class of electronic machines -- radio receivers. A computational performance model - WATSON - is presented. WATSONs task is to isolate failures in radio receivers whose…

Combining numerical techniques with ideas from symbolic computation and with methods incorporating knowledge of science and mathematics leads to a new category of intelligent computational tools for scientists and engineers. These tools autonomously prepare simulation experiments from high-level specifications of…

The goal of this paper is to determine if the results for dynamic job-shop scheduling problems are affected by the assumptions made with regard to the processing time distributions and the scheduler's knowledge of the processing times. Three dynamic jobshop…

This thesis proposes a methodology for the design of man-machine interfaces by combining top-down and bottom-up processes in vision. From a computational perspective, we propose that the scientific-cognitive question of combining top-down and bottom-up knowledge is similar to the engineering…

In this thesis, the opportunities and challenges involved in applying natural language processing techniques to the control of unmanned aerial vehicles (UAVs) are addressed. The problem of controlling an unmanned aircraft via natural language inputs is formulated as a feedback…

Nature and Gnosticism are problematic terms. With the discovery of a library of so called heretical Gnostic texts in 1945, theologians, historians, and believers the world over have been exposed to Christian texts that call into question the liturgical lineage…

Causal reconstruction is the task of reading a written causal description of a physical behavior, forming an internal model of the described activity, and demonstrating comprehension through question answering. T his task is difficult because written d escriptions often do…

We had previously shown that regularization principles lead to approximation schemes, as Radial Basis Functions, which are equivalent to networks with one layer of hidden units, called Regularization Networks. In this paper we show that regularization networks encompass a much…

A key problem in model-based object recognition is selection, namely, the problem of isolating regions in an image that are likely to come from a single object. This isolation can be either based solely on image data (data-driven) or can…

(cont.) which is expected, given their additional knowledge resources. The respondents' urgency in looking for the relevant knowledge is the primary explanation for this difference between the two networks. This difference in knowledge networking among the different groups did not…