Special Issue on "Artificial Cognitive Systems for Assistance in Traffic, Industry, and Automatization"
• 大类 : 医学 - 4区
• 小类 : 计算机：人工智能 - 4区
• 小类 : 神经科学 - 4区
This special issue of "Cognitive Systems Research“ invites submissions addressing questions on how artificial cognitive systems for assistance in traffic, industry, and automatization should and could be designed, developed, and evaluated. Technical assistance systems, that is, artificial systems that generally support or help human users in specific tasks, have significantly advanced in the last years as they are becoming more and more widespread. For example, all modern vehicles are equipped with intelligent driver assistance systems, mobile devices feature voice-based personal assistants, operators in air traffic control are supported by technical systems with different degrees of autonomy, and workers in manufacturing settings are expected to collaborate with robots and automation system interfaces. As a result, humans come to interact with technical assistance systems on a daily basis, in everyday and professional life. This raises important questions in two inter-related domains specifically addressed with this special issue:
1. How to analyze and model the interaction process between users and assistance systems? How do humans understand, utilize and rely on them? And how should assistance systems present themselves in continuous communication with their users?
2. What key features and abilities do cognitive assistance systems need to have? How can they represent, reason about, and predict the relevant states of the user, the situation and the current task at a required level of detail? And how can modern approaches to learning and adaptation yield better cognitive assistance systems?
A considerable body of research has looked at these issues based on long established methods from fields such as planning, automated reasoning, or cognitive modeling. These classical cognitive approaches are increasingly complemented, if not replaced, by new kinds of assistance systems relying on the use of data-based machine learning. However, it is an open question which kind of methods, or combination thereof, will yield the machine intelligence that is required for robust, efficient and acceptable assistance with human users. Very recent approaches are starting to return to more cognitive, model-based approaches and their integration with data-based techniques to enable, e.g., human-like generalization, zero-shot learning, or more intuitive human-machine interaction. At the same time, the human-aware design of such systems and the need to guarantee (re-)liable A.I.-based assistance systems, both at the individual scale as well as at the societal scale, is reflected in a burgeoning interest in transparency and explainability.
We invite submission of articles presenting original approaches, studies, or findings on how cognitive assistance systems for traffic, industry, or automatization should and could be designed, developed, and evaluated. Submissions of new research ideas are encouraged in this special issue, related but not limited to the following topics:
Situation awareness in dynamic environments with human users/operators
Architectures and techniques for robustness and reliability
Learning and adaptation in assistance systems
Deep cognitive or affective user models
Measurement or recognition of cognitive load, emotions, or affect
Models of planning and decision-making for assistance
Shared or adjusted autonomy in human-agent/robot teamwork
Hand-over of control
Mixed-initiative, multimodal communication and collaboration
Empirical studies on user acceptance and usability of assistance systems
Evaluation measures and methodologies for cognitive assistance systems