17 research outputs found
Using fuzzy logic to integrate neural networks and knowledge-based systems
Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems
An analysis of the requirements traceability problem
In this paper1, we investigate and discuss the underlying nature
of the requirements traceability problem. Our work is based on
empirical studies, involving over 100 practitioners, and an
evaluation of current support. We introduce the distinction
between pre-requirements specification (pre-RS) traceability
and post-requirements specification (post-RS) traceability, to
demonstrate why an all-encompassing solution to the problem is
unlikely, and to provide a framework through which to
understand its multifaceted nature. We report how the majority
of the problems attributed to poor requirements traceability are
due to inadequate pre-RS traceability and show the fundamental
need for improvements here. In the remainder of the paper, we
present an analysis of the main barriers confronting such
improvements in practice, identify relevant areas in which
advances have been (or can be) made, and make
recommendations for research
'It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisions
Data-driven decision-making consequential to individuals raises important
questions of accountability and justice. Indeed, European law provides
individuals limited rights to 'meaningful information about the logic' behind
significant, autonomous decisions such as loan approvals, insurance quotes, and
CV filtering. We undertake three experimental studies examining people's
perceptions of justice in algorithmic decision-making under different scenarios
and explanation styles. Dimensions of justice previously observed in response
to human decision-making appear similarly engaged in response to algorithmic
decisions. Qualitative analysis identified several concerns and heuristics
involved in justice perceptions including arbitrariness, generalisation, and
(in)dignity. Quantitative analysis indicates that explanation styles primarily
matter to justice perceptions only when subjects are exposed to multiple
different styles---under repeated exposure of one style, scenario effects
obscure any explanation effects. Our results suggests there may be no 'best'
approach to explaining algorithmic decisions, and that reflection on their
automated nature both implicates and mitigates justice dimensions.Comment: 14 pages, 3 figures, ACM Conference on Human Factors in Computing
Systems (CHI'18), April 21--26, Montreal, Canad
Explainable expert systems: A research program in information processing
Our work in Explainable Expert Systems (EES) had two goals: to extend and enhance the range of explanations that expert systems can offer, and to ease their maintenance and evolution. As suggested in our proposal, these goals are complementary because they place similar demands on the underlying architecture of the expert system: they both require the knowledge contained in a system to be explicitly represented, in a high-level declarative language and in a modular fashion. With these two goals in mind, the Explainable Expert Systems (EES) framework was designed to remedy limitations to explainability and evolvability that stem from related fundamental flaws in the underlying architecture of current expert systems
The Need for User Models in Generating Expert System Explanations
An explanation facility is an important component of an expert system, but current systems for the most part have neglected the importance of tailoring a system\u27s explanations to the user. This paper explores the role of user modeling in generating expert system explanations, making the claim that individualized user models are essential to produce good explanations when the system users vary in their knowledge of the domain, or in their goals, plans, and preferences. To make this argument, a characterization of explanation, and good explanation is made, leading to a presentation of how knowledge about the user affects the various aspects of a good explanation. Individualized user models are not only important, it is practical to obtain them. A method for acquiring a model of the user\u27s beliefs implicitly by eavesdropping on the interaction between user and system is presented, along with examples of how this information can be used to tailor an explanation
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Artificial intelligence at OSU
This report describes of current research in Artificial Intelligence at Oregon State University. The five areas of active research are ( a) intelligent aids for mechanical engineering design, (b) active experimentation as a method in machine learning, ( c) techniques for combining logic programming and assumption-based truth maintenance, ( d) methods for combining symbolic and numeric approaches to reasoning under uncertainty, and (e) problem-solving strategies for working with multiple qualitative models