The "Artificial Mathematician" Objection: Exploring the (Im)possibility of Automating Mathematical Understanding

Reuben Hersh confided to us that, about forty years ago, the late Paul Cohen predicted to him that at some unspecified point in the future, mathematicians would be replaced by computers. Rather than focus on computers replacing mathematicians, however, our aim is to consider the (im)possibility of human mathematicians being joined by “artificial mathematicians” in the proving practice—not just as a method of inquiry but as a fellow inquirer

Working Notes from the 1992 AAAI Workshop on Automating Software Design. Theme: Domain Specific Software Design

The goal of this workshop is to identify different architectural approaches to building domain-specific software design systems and to explore issues unique to domain-specific (vs. general-purpose) software design. Some general issues that cut across the particular software design domain include: (1) knowledge representation, acquisition, and maintenance; (2) specialized software design techniques; and (3) user interaction and user interface

Rules and principles in cognitive diagnoses

Cognitive simulation is concerned with constructing process models of human cognitive behavior. Our work on the ACM system (Automated Cognitive Modeler) is an attempt to automate this process. The basic assumption is that all goal-oriented cognitive behavior involves search through some problem space. Within this framework, the task of cognitive diagnosis is to identify the problem space in which the subject is operating, identify solution paths used by the subject, and find conditions on the operators that explain those solution paths and that predict the subject's behavior on new problems. The work presented in this paper uses techniques from machine learning to automate the tasks of finding solution paths and operator conditions. We apply this method to the domain of multi-column subtraction and present results that demonstrate ACM's ability to model incorrect subtraction strategies. Finally, we discuss the difference between procedural bugs and misconceptions, proposing that errors due to misconceptions can be viewed as violations of principles for the task domain

Biologically Inspired Approaches to Automated Feature Extraction and Target Recognition

Ongoing research at Boston University has produced computational models of biological vision and learning that embody a growing corpus of scientific data and predictions. Vision models perform long-range grouping and figure/ground segmentation, and memory models create attentionally controlled recognition codes that intrinsically cornbine botton-up activation and top-down learned expectations. These two streams of research form the foundation of novel dynamically integrated systems for image understanding. Simulations using multispectral images illustrate road completion across occlusions in a cluttered scene and information fusion from incorrect labels that are simultaneously inconsistent and correct. The CNS Vision and Technology Labs (cns.bu.edulvisionlab and cns.bu.edu/techlab) are further integrating science and technology through analysis, testing, and development of cognitive and neural models for large-scale applications, complemented by software specification and code distribution.Air Force Office of Scientific Research (F40620-01-1-0423); National Geographic-Intelligence Agency (NMA 201-001-1-2016); National Science Foundation (SBE-0354378; BCS-0235298); Office of Naval Research (N00014-01-1-0624); National Geospatial-Intelligence Agency and the National Society of Siegfried Martens (NMA 501-03-1-2030, DGE-0221680); Department of Homeland Security graduate fellowshi

A Decision Technology System To Advance the Diagnosis and Treatment of Breast Cancer

Geographical variations in cancer rates have been observed for decades. Described spatial patterns and trends have provided clues for generating hypotheses about the etiology of cancer. For breast cancer, investigators have demonstrated that some variation can be explained by differences in the population distribution of known breast cancer risk factors such as menstrual and reproductive variables (Laden, Spiegelman, and Neas, 1997; Robbins, Bescianini, and Kelsey, 1997; Sturgeon, Schairer, and Gail, 1995). However, regional patterns also may reflect the effects of Workshop on Hormones, Hormone Metabolism, Environment, and Breast Cancer (1995): (a) environmental hazards (such as air and water pollution), (b) demographics and the lifestyle of a mobile population, (c) subgroup susceptibility, (d) changes and advances in medical practice and healthcare management, and (e) other factors. To accurately measure breast cancer risk in individuals and population groups, it is necessary to singly and jointly assess the association between such risk and the hypothesized factors. Various statistical models will be needed to determine the potential relationships between breast cancer development and estimated exposures to environmental contamination. To apply the models, data must be assembled from a variety of sources, converted into the statistical models’ parameters, and delivered effectively to researchers and policy makers. A Web-enabled decision technology system can be developed to provide the needed functionality. This chapter will present a conceptual architecture for such a decision technology system. First, there will be a brief overview of a typical geographical analysis. Next, the chapter will present the conceptual Web-based decision technology system and illustrate how the system can assist users in diagnosing and treating breast cancer. The chapter will conclude with an examination of the potential benefits from system use and the implications for breast cancer research and practice

An overview of decision table literature 1982-1995.

This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.

Civil Space Technology Initiative: a First Step

This is the first published overview of OAST's focused program, the Civil Space Technology Initiative, (CSTI) which started in FY88. This publication describes the goals, technical approach, current status, and plans for CSTI. Periodic updates are planned