DETC2002/CIE-34507 ARTIFICIAL INTELLIGENCE BASED INFERENCE TECHNIQUES FOR AUTOMATED PROCESS PLANNING FOR MACHINED PARTS

ABSTRACT Many areas of research in manufacturing are increasingly turning to applications of Artificial Intelligence (AI). The problem of developing inference strategies for automated process planning in machining is one such area of successful application of AI based approaches. Given the high complexity of the process planning expertise, development of inference techniques for automated process planning is a big challenge to researchers. The traditional inference methods based on variant and generative approaches using decision trees and decision tables suffer from a number of shortcomings, which have prompted researchers to seek alternative approaches and turn to AI for developing intelligent inference techniques. In this article, we have reviewed, categorized and summarized the research on applications of AI for developing inference methods for automated process planning systems. We have described our ongoing research work on developing an intelligent inference strategy based on artificial neural networks for implementing machining process selection for rotationally symmetric parts

Advancing the Empirical Research on Lobbying

This essay identifies the empirical facts about lobbying which are generally agreed upon in the literature. It then discusses challenges to empirical research in lobbying and provides examples of empirical methods that can be employed to overcome these challenges—with an emphasis on statistical measurement, identification, and casual inference. The essay then discusses the advantages, disadvantages, and effective use of the main types of data available for research in lobbying. It closes by discussing a number of open questions for researchers in the field and avenues for future work to advance the empirical research in lobbying

On perceptual expertise

Expertise is a cognitive achievement that clearly involves experience and learning, and often requires explicit, time-consuming training specific to the relevant domain. It is also intuitive that this kind of achievement is, in a rich sense, genuinely perceptual. Many experts—be they radiologists, bird watchers, or fingerprint examiners—are better perceivers in the domain(s) of their expertise. The goal of this paper is to motivate three related claims, by substantial appeal to recent empirical research on perceptual expertise: Perceptual expertise is genuinely perceptual and genuinely cognitive, and this phenomenon reveals how we can become epistemically better perceivers. These claims are defended against sceptical opponents that deny significant top-down or cognitive effects on perception, and opponents who maintain that any such effects on perception are epistemically pernicious

The Clinical Assessment in the Legal Field: An Empirical Study of Bias and Limitations in Forensic Expertise

According to the literature, psychological assessment in forensic contexts is one of the most controversial application areas for clinical psychology. This paper presents a review of systematic judgment errors in the forensic field. Forty-six psychological reports written by psychologists, court consultants, have been analyzed with content analysis to identify typical judgment errors related to the following areas: (a) distortions in the attribution of causality, (b) inferential errors, and (c) epistemological inconsistencies. Results indicated that systematic errors of judgment, usually referred also as "the man in the street," are widely present in the forensic evaluations of specialist consultants. Clinical and practical implications are taken into account. This article could lead to significant benefits for clinical psychologists who want to deal with this sensitive issue and are interested in improving the quality of their contribution to the justice system

Interpretable Machine Learning for Privacy-Preserving Pervasive Systems

Our everyday interactions with pervasive systems generate traces that capture various aspects of human behavior and enable machine learning algorithms to extract latent information about users. In this paper, we propose a machine learning interpretability framework that enables users to understand how these generated traces violate their privacy