Grounding explanations in evolving, diagnostic situations

Certain fields of practice involve the management and control of complex dynamic systems. These include flight deck operations in commercial aviation, control of space systems, anesthetic management during surgery or chemical or nuclear process control. Fault diagnosis of these dynamic systems generally must occur with the monitored process on-line and in conjunction with maintaining system integrity.This research seeks to understand in more detail what it means for an intelligent system to function cooperatively, or as a 'team player' in complex, dynamic environments. The approach taken was to study human practitioners engaged in the management of a complex, dynamic process: anesthesiologists during neurosurgical operations. The investigation focused on understanding how team members cooperate in management and fault diagnosis and comparing this interaction to the situation with an Artificial Intelligence(AI) system that provides diagnoses and explanations. Of particular concern was to study the ways in which practitioners support one another in keeping aware of relevant information concerning the state of the monitored process and of the problem solving process

Making intelligent systems team players: Case studies and design issues. Volume 1: Human-computer interaction design

Initial results are reported from a multi-year, interdisciplinary effort to provide guidance and assistance for designers of intelligent systems and their user interfaces. The objective is to achieve more effective human-computer interaction (HCI) for systems with real time fault management capabilities. Intelligent fault management systems within the NASA were evaluated for insight into the design of systems with complex HCI. Preliminary results include: (1) a description of real time fault management in aerospace domains; (2) recommendations and examples for improving intelligent systems design and user interface design; (3) identification of issues requiring further research; and (4) recommendations for a development methodology integrating HCI design into intelligent system design

Behind Human Error

Human error is cited over and over as a cause of incidents and accidents. The result is a widespread perception of a 'human error problem', and solutions are thought to lie in changing the people or their role in the system. For example, we should reduce the human role with more automation, or regiment human behavior by stricter monitoring, rules or procedures. But in practice, things have proved not to be this simple. The label 'human error' is prejudicial and hides much more than it reveals about how a system functions or malfunctions. This book takes you behind the human error label. Divided into five parts, it begins by summarising the most significant research results. Part 2 explores how systems thinking has radically changed our understanding of how accidents occur. Part 3 explains the role of cognitive system factors - bringing knowledge to bear, changing mindset as situations and priorities change, and managing goal conflicts - in operating safely at the sharp end of systems. Part 4 studies how the clumsy use of computer technology can increase the potential for erroneous actions and assessments in many different fields of practice. And Part 5 tells how the hindsight bias always enters into attributions of error, so that what we label human error actually is the result of a social and psychological judgment process by stakeholders in the system in question to focus on only a facet of a set of interacting contributors. If you think you have a human error problem, recognize that the label itself is no explanation and no guide to countermeasures. The potential for constructive change, for progress on safety, lies behind the human error label

Introduction to knowledge spaces: How to build, test, and search them

This article gives a comprehensive description of a theory for the efficient assessment of knowledge. The essential concept is that the knowledge state of a subject with regard to a specified field of information can be represented by a particular subset of questions or problems that the subject is capable of solving. The family of all knowledge states forms the knowledge space. It is assumed that if 2 subsets K and K′ of questions are assumed to be states in a knowledge space ,then KU K′ is also assumed to be a state in K. Such a theory is consistent with the idea that at least some of the notions in the field may be acquired from different sets of prerequisites. Various aspects of the theory are discussed. In particular, the problem of constructing a knowledge space in practice is analyzed in detail. A first sketch of the knowledge space can be obtained by consulting expert teachers in the field. The mathematical theory necessary to render this consultation efficient is given. This preliminary construction can then be tested and refined on the basis of empirical data. To this end, a probabilistic version of the theory is developed, which is similar in spirit to some psychometric models, but it is grounded on the concept of a knowledge space rather than on that of skill or ability. An exemplary application of this probabilistic theory to a high school mathematics test is described, based on a sample of several hundred students. By standard likelihood ratio methods, it is shown how the preliminary knowledge space can be gradually refined, and the number of possible knowledge states substantially reduced. Two classes of Markovian knowledge assessment algorithms are outlined. Most of the results presented summarize previous articles published in various technical journals. The application of the probabilistic theory to the high school mathematics test is original to this article.SCOPUS: ar.jinfo:eu-repo/semantics/publishe