The thesis explores architectural form generation through two behavior based artificial intelligence approaches: the communication of agents in an unpredictable simulation system, and the codification of information within an evolutionary process. Both concepts stem from the evaluation of potentially definable…

We show how to interoperate, semantically and inferentially, between the leading Semantic Web approaches to rules (RuleML Logic Programs) and ontologies (OWL/DAML+OIL Description Logic) via analyzing their expressive intersection. To do so, we define a new intermediate knowledge representation (KR)…

his dissertation analyzes how local knowledge improves environmental decisions. The premise is that controlling pollution and addressing public health disparities are not problems that professionals alone can solve. Concerned lay publics, especially low-income populations and people of color that experience…

We examine semantic interoperability problems in the fixed income securities industry
and propose a knowledge representation architecture for context interchange
mediation to support dynamic integration of autonomous database, web, and
procedural sources of information. For sources and…

We develop a qualitative method for understanding and representing phase space structures of complex systems and demonstrate the method with a program, MAPS --- Modeler and Analyzer for Phase Spaces, using deep domain knowledge of dynamical system theory. Given a…

Automated Program Analysis is the process of discovering decompositions of a system into sub-units such that the behavior of the whole program can be inferred from the behavior of its parts. Analysis can be employed to increase the explanatory power…

The studies of anatomy and physiology are fundamental ingredients of medical education. This paper identifies six ways in which such functional knowledge serves as the underpinnings for general medical reasoning, and outlines the design of a computational model of common…

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…

Consider a world of 'objects.' Our goal is to place these objects into categories that are useful to the observer using sensory data. One criterion for utility is that the categories allow the observer to infer the object's potential behaviors,…

There has been a long-standing desire in computer science for a way of collecting and using libraries of standard software components. The limited success in actually doing this stems not from any resistance to the idea, nor from any lack…