Context plays an increasingly important role in modern IT applications. Context sensitivity and awareness is becoming essential, not only for mobile systems, ambient computing and the internet of things, but also for a wide range of other areas, such as learning and teaching solutions, collaborative software, web engineering, mobility logistics and health care work-flow. Advancing the use and understanding of context beyond stimuli-response systems suggests a knowledge perspective on modelling and reasoning.
For autonomous systems, recognising contextual information is vital if the system is to exhibit behaviour that is appropriate for the situation at hand. At the same time, such systems might change contextual parameters that are relevant for human and non-human agents present. Therefore, it is important to be able to predict changes in context that are due to the actions of intelligent systems to avoid clashing with user needs and expectations.
In multi-agent systems, contextual information might not be shared between the different actors explicitly or upfront. Therefore, it is vital for intelligent agents to identify the different context configurations and adapt their own behaviour accordingly.
From a machine learning perspective, contextual information might have to be learned from data before a contextualised system is being implemented. In many cases, contextual configurations might change over time, and cannot fully be modelled in the design phase of a system, necessitating machine learning methods to be employed during runtime.
With a renewed interest in explainable systems, context is also increasingly important to identify user needs and system capabilities in providing explanations of system behaviour at runtime.
From a general AI perspective, one of the challenges is to integrate context with other types of knowledge as a major additional source for reasoning, decision-making, and adaptation and to form a coherent and versatile architecture. There is a common understanding that achieving desired behaviour from intelligent systems will depend on the ability to represent and manipulate information about a rich range of contextual factors.
These factors may include not only physical characteristics of the task environment, but, possibly more importantly, many other aspects including cognitive factors such as the knowledge states (of both the application and user) or emotions, and social factors such as networks, relations, roles, and hierarchies. This representation and reasoning problem presents research challenges to which methodologies derived from areas such as artificial intelligence, knowledge management, human-computer interaction, semiotics and psychology can contribute solutions.
MRC invites papers on different aspects of context, on theory as well as on applications. We particularly invite contributions on topics of autonomy and context. Although hosted at the most prestigious AI conference, we explicitly invite contributions from other fields of study in order to further trans- and interdisciplinary approaches.
Areas of interest include, but are not limited to:
Please check our pages with author guidelines and submission procedure information for details. Prepared PDF can be submitted through the MRC-pages at EasyChair.
Last modified: Friday, 2018-02-23 23:18 UTC.
University of Hildesheim