Smart Decision-Making: New Paths

Dear Colleagues,

Advances in pervasive sensing technology have led to the development of sensor networks that can be seamlessly integrated into the living environment without interfering with daily life. The domains of actuation of these sensor networks are diverse, including robotics, health care, wellness applications, environmental management, disaster prevention, energy management, process control, and so forth. The collection of substantial amounts of data in these domains, from heterogeneous sources, has the potential to change them and improve the outcomes of the activities developed within them. However, the extraction of useful knowledge from these data, which in many cases can be considered big data, faces important challenges, such as dealing with corrupted sensor readings, data reduction, and the need for expensive human intervention, among others. The challenge in transforming the valuable insights provided by the data analytics process into decision models capable of refining the decision-making process also presents difficulties. Additionally, the way in which an agent actuates in the environment, based on the outcomes provided by the decision models, must also respect the nuances and constraints imposed by the domain.

In this context, this Special Issue on “Smart Decision-making: New Paths” invites authors to submit their novel and original works on data collection and analytics strongly tied to the development of decision models that enhance the intervention of agents in the domain. Works presenting mechanisms for belief-revision and adaption based on data are also welcome and within the scope of the Special Issue. Topics of interest include but are not limited to:

• Multisensor data fusion;
• Data-analytics;
• Self-adapting systems;
• Belief-revision;
• Decision making frameworks;
• Problem solving with incomplete information;
• Default reasoning;
• Argumentation;
• Semantic models and ontologies for dynamic environments;
• Models of trust and reputation;
• Reasoning under uncertainty;
• Fault-tolerant architectures;
• Applications (reasoning techniques in dynamic environments such as a medical setting, energy management, process control, ambient intelligence, ambient assisted living, etc.);

Dr. Tiago Oliveira
Prof. Dr. Paulo Novais
Prof. Dr. Luke Wallace
Guest Editors

Manuscript Submission Information

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• data fusion
• data analytics
• decision making
• uncertainty
• dynamic environments