Artificial Intelligence and Cognitive Psychology: A New Look at Human Factors

1988 ◽  
pp. 274-289 ◽  
Author(s):  
David S. Brée
Keyword(s):  
Artificial Intelligence ◽  
Cognitive Psychology ◽  
Human Factors ◽  
New Look

Principles of induction and approaches to attribute based induction

1991 ◽  
Vol 6 (4) ◽  
pp. 307-333 ◽  
Author(s):  
G. Kalkanis ◽  
G. V. Conroy
Keyword(s):  
Artificial Intelligence ◽  
Pattern Recognition ◽  
Cognitive Psychology ◽  
Expert Systems ◽  
Basic Principles ◽  
Inductive Approach ◽  
Induction Process

AbstractThis paper presents a survey of machine induction, studied mainly from the field of artificial intelligence, but also from the fields of pattern recognition and cognitive psychology. The paper consists of two parts: Part I discusses the basic principles and features of the machine induction process; Part II uses these principles and features to review and criticize the major supervised attribute-based induction methods. Attribute-based induction has been chosen because it is the most commonly used inductive approach in the development of expert systems and pattern recognition models.

scholarly journals Beyond robotic speech: mutual benefits to cognitive psychology and artificial intelligence from the study of multimodal communication

2020 ◽  
Author(s):  
Beata Grzyb ◽  
Gabriella Vigliocco
Keyword(s):  
Artificial Intelligence ◽  
Cognitive Psychology ◽  
Language Learning ◽  
Language Processing ◽  
Computational Models ◽  
Eye Gaze ◽  
Human Communication ◽  
Embodied Agents ◽  
Human Machine Interaction ◽  
Neural Underpinnings

Language has predominately been studied as a unimodal phenomenon - as speech or text without much consideration of its physical and social context – this is true both in cognitive psychology/psycholinguistics as well as in artificial intelligence. However, in everyday life, language is most often used in face-to-face communication and in addition to structured speech it comprises a dynamic system of multiplex components such as gestures, eye gaze, mouth movements and prosodic modulation. Recently, cognitive scientists have started to realise the potential importance of multimodality for the understanding of human communication and its neural underpinnings; while AI scientists have begun to address how to integrate multimodality in order to improve communication between human and artificial embodied agent. We review here the existing literature on multimodal language learning and processing in humans and the literature on perception of artificial agents, their comprehension and production of multimodal cues and we discuss their main limitations. We conclude by arguing that by joining forces AI scientists can improve the effectiveness of human-machine interaction and increase the human-likeness and acceptance of embodied agents in society. In turn, computational models that generate language in artificial embodied agents constitute a unique research tool to investigate the underlying mechanisms that govern language processing and learning in humans.

Human Factors Considerations for Teaming between Construction Workersand Voice-based Intelligent Virtual Agent (VIVA)

2021 ◽  
Vol 65 (1) ◽  
pp. 72-76
Author(s):  
Maryam Rahimi Movassagh ◽  
Nazila Roofigari-Esfahan ◽  
Sang Won Lee ◽  
Carlos Evia ◽  
David Hicks ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Human Factors ◽  
Research Agenda ◽  
Task Environment ◽  
Virtual Agent ◽  
Work Processes ◽  
Construction Sites

Construction sites experience low productivity due to particular characteristics such as unique designs in each project, sporadic arrival of projects, and complexity of the process. Another contributing factor to low productivity is poor communication among workers, supervisors, and a site’s centralized knowledge hub. Research shows that introducing advanced artificial intelligence (AI) technology in construction can tackle these problems. In this paper, we analyzed human factors considerations–user, task, environment, and technology and identified their characteristics and challenges to design an interactive AI system to facilitate communication between workers and other stakeholders. Based on the analysis, we propose a voice-based intelligent virtual agent (VIVA) as a multi-purpose AI system on construction sites with a further research agenda. We hope that this effort can guide the design of construction-specific AI systems and that this worker-AI teaming can improve overall work processes, enhance productivity, and promote safety in construction.

scholarly journals Human factors challenges for the safe use of artificial intelligence in patient care

2019 ◽  
Vol 26 (1) ◽  
pp. e100081 ◽  
Author(s):  
Mark Sujan ◽  
Dominic Furniss ◽  
Kath Grundy ◽  
Howard Grundy ◽  
David Nelson ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Human Factors ◽  
Patient Care ◽  
Situation Awareness ◽  
Human Performance ◽  
Independent Evaluation ◽  
The Impact ◽  
Human Factors Research

The use of artificial intelligence (AI) in patient care can offer significant benefits. However, there is a lack of independent evaluation considering AI in use. The paper argues that consideration should be given to how AI will be incorporated into clinical processes and services. Human factors challenges that are likely to arise at this level include cognitive aspects (automation bias and human performance), handover and communication between clinicians and AI systems, situation awareness and the impact on the interaction with patients. Human factors research should accompany the development of AI from the outset.

scholarly journals The Wisdom of the Market: Using Human Factors to Design Prediction Markets for Collective Intelligence

2019 ◽  
Vol 63 (1) ◽  
pp. 1471-1475
Author(s):  
Lorenzo Barberis Canonico ◽  
Christopher Flathmann ◽  
Nathan McNeese
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Game Theory ◽  
Human Factors ◽  
Cognitive Science ◽  
Collective Intelligence ◽  
Human Intelligence ◽  
Prediction Markets ◽  
New Model ◽  
Large Groups

There is an ever-growing literature on the power of prediction markets to harness “the wisdom of the crowd” from large groups of people. However, traditional prediction markets are not designed in a human-centered way, often restricting their own potential. This creates the opportunity to implement a cognitive science perspective on how to enhance the collective intelligence of the participants. Thus, we propose a new model for prediction markets that integrates human factors, cognitive science, game theory and machine learning to maximize collective intelligence. We do this by first identifying the connections between prediction markets and collective intelligence, to then use human factors techniques to analyze our design, culminating in the practical ways with which our design enables artificial intelligence to complement human intelligence.

Design & Evaluation of HCI in Subsea Pipeline Examine & Repair System

2000 ◽  
Vol 44 (22) ◽  
pp. 541-544
Author(s):  
Yi Hong ◽  
Zhang Yufang ◽  
Wang Jiangbo ◽  
Ge Tong ◽  
Zhou Longxiang
Keyword(s):  
Cognitive Psychology ◽  
Human Factors ◽  
Quantitative Evaluation ◽  
Evaluation Criterion ◽  
Design Evaluation ◽  
Repair System ◽  
Human Factors Engineering ◽  
Subsea Pipeline ◽  
Operating Reliability ◽  
Full Consideration

Based on human cognitive psychology and information transferring characters, this paper introduces human factors engineering and human operating reliability into the evaluation of HCI design. Human factors and information transferring characters of human & computer are taken into full consideration during the HCI design. Human computer (HC) reliability is brought forward as a quantitative evaluation criterion for the HCI design. Quantitative evaluation is achieved by building HC information transferring model and HET model.

scholarly journals Logical Modeling of Emotions for Ambient Intelligence

2011 ◽  
pp. 108-127 ◽  
Author(s):  
Carole Adam ◽  
Benoit Gaudou ◽  
Dominique Login ◽  
Emiliano Lorini
Keyword(s):  
Artificial Intelligence ◽  
Human Factors ◽  
Ambient Intelligence ◽  
Modal Logics ◽  
Human Reasoning ◽  
Counterfactual Reasoning ◽  
Attribution Of Responsibility ◽  
Logical Approach ◽  
Complex Emotions ◽  
Logical Modeling

Ambient Intelligence (AmI) is the art of designing intelligent and user-focused environments. It is thus of great importance to take human factors into account. In this chapter we especially focus on emotions, that have been proved to be essential in human reasoning and interaction. To this end, we assume that we can take advantage of the results obtained in Artificial Intelligence about the formal modeling of emotions. This chapter specifically aims at showing the interest of logic as a tool to design agents endowed with emotional abilities useful for Ambient Intelligence applications. In particular, we show that modal logics allow the representation of the mental attitudes involved in emotions such as beliefs, goals or ideals. Moreover, we illustrate how modal logics can be used to represent complex emotions (also called self-conscious emotions) involving elaborated forms of reasoning, such as self-attribution of responsibility and counterfactual reasoning. Examples of complex emotions are regret and guilt. We illustrate our logical approach by formalizing some case studies concerning an intelligent house taking care of its inhabitants.

Sign in / Sign up

Export Citation Format

Share Document