Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Automation is often problematic because people fail to rely upon it appropriately. Because people respond to technology socially, trust influences reliance on automation. In particular, trust guides reliance when complexity and unanticipated situations make a complete understanding of the automation impractical. This review considers trust from the organizational, sociological, interpersonal, psychological, and neurological perspectives. It considers how the context, automation characteristics, and cognitive processes affect the appropriateness of trust. The context in which the automation is used influences automation performance and provides a goal-oriented perspective to assess automation characteristics along a dimension of attributional abstraction. These characteristics can influence trust through analytic, analogical, and affective processes. The challenges of extrapolating the concept of trust in people to trust in automation are discussed. A conceptual model integrates research regarding trust in automation and describes the dynamics of trust, the role of context, and the influence of display characteristics. Actual or potential applications of this research include improved designs of systems that require people to manage imperfect automation.

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Trust in Automation: Designing for Appropriate Reliance

人教版高中英语新课程教材中,语言运用（Using Language）是每个单元必不可少的部分,提供了围绕单元中心话题的听、说、读、写的综合性练习,是单元中心话题的延续和升华.如何设计Using Language部分的教学,使自己的教学模式既不落俗套,又能真正体现新课程标准所倡导的教学理念,正是广大一线英语教师一直努力探索的问题.

打磨Using Language,倡导新理念

Objective:We systematically review recent empirical research on factors that influence trust in automation to present a three-layered trust model that synthesizes existing knowledge.Background:Much...

Trust in Automation: Integrating Empirical Evidence on Factors That Influence Trust

Cognitive engineering

One component in the successful use of automated systems is the extent to which people trust the automation to perform effectively. In order to understand the relationship between trust in computerized systems and the use of those systems, we need to be able to effectively measure trust. Although questionnaires regarding trust have been used in prior studies, these questionnaires were theoretically rather than empirically generated and did not distinguish between three potentially different types of trust: human-human trust, human-machine trust, and trust in general. A 3-phased experiment, comprising a word elicitation study, a questionnaire study, and a paired comparison study, was performed to better understand similarities and differences in the concepts of trust and distrust, and among the different types of trust. Results indicated that trust and distrust can be considered opposites, rather than different concepts. Components of trust, in terms of words related to trust, were similar across the three...

https://apps.dtic.mil/sti/pdfs/ADA395339.pdf

Foundations for an Empirically Determined Scale of Trust in Automated Systems

The abstraction hierarchy (AH) is a multileveled representation framework, consisting of physical and functional system models, which has been proposed as a useful framework for developing representations of complex work environments Despite the fact that the AH is well known and widely cited in the cognitive engineering community, there are surprisingly few examples of its application Accordingly, the intent of this paper is to provide a concrete example of how the AH can be applied as a knowledge representation framework A formal instantiation of the AH as the basis for a computer program is presented in the context of a thermal-hydraulic process This model of the system is complemented by a relatively simple reasoning mechanism which is independent of the information contained in the knowledge representation This reasoning mechanism uses the AH model, along with qualitative user input about system states, to generate reasoning trajectories for different types of events and problems Simulation outputs showing how the AH model can provide an effective basis for reasoning under different classes of situations, including challenging faults of various types, are presented These detailed examples illustrate the various benefits of adopting the AH as a knowledge representation framework, namely: providing sufficient representations to allow reasoning about unanticipated fault and control situations, allowing the use of reasoning mechanisms that are independent of domain information, and having psychological relevance

Making the abstraction hierarchy concrete

Emergency department communication links and patterns.

This paper describes the integration of cognitive analysis into the early stages of design of a new, large-scale system--a next generation US Navy Surface combatant. Influencing complex system designs in ways cognizant of human-system integration principles requires work products that are timely and tightly coupled to other elements of the design process. Because analyses were conducted simultaneously with the design processes regarding ship functionality and staffing, it was necessary to select and adapt cognitive work analysis methods to fit the demands of a time pressured and information-limited design situation. Interviews were conducted and analyzed based on aspects of an abstraction hierarchy and control task models. An abstraction hierarchy, a series of cross-linked matrices, and a set of decision ladder models were developed to provide a principled mapping between system function decompositions produced by system engineering teams and cognitive tasks, information needs, automation requirements, and concepts for displays. Cross-referencing the matrices supported design traceability and facilitated the integration of cognitive analyses with functional analyses being performed by other design teams. Results fed into design recommendations with respect to level of automation, human roles and initial display prototypes for the ship combat command center. The case study illustrates the utility of cognitive work analysis models (specifically, abstraction hierarchies and decision-ladder models) in the design of large-scale, first-of-a-kind systems, and presents new design artifacts that link concepts used in cognitive analyses to those used in systems engineering for more effective integration within the systems engineering process.

Integrating cognitive analyses in a large-scale system design process

Cognitive Work Analysis (Vicente, 1999) is a work-centered conceptual framework developed by Rasmussen, Pejtersen & Goodstein (1994) to analyze cognitive work. The purpose of Cognitive Work Analysis (CWA) is to guide the design of technology for use in the work place. It is unique because of its ability to analyze real-life phenomena while retaining the complexity inherent in them. When applied to information behavior, the approach guides the analysis of human-information interaction in order to inform the design of information systems.

Ann M. Bisantz

Papers

Foundations for an Empirically Determined Scale of Trust in Automated Systems

Making the abstraction hierarchy concrete

Emergency department communication links and patterns.

Integrating cognitive analyses in a large-scale system design process

Cognitive Work Analysis