About this Research Topic

Manuscript Submission Deadline 15 July 2022
Manuscript Extension Submission Deadline 12 August 2022

Decision Support Systems (DDS) have become quite popular in several contexts like health services to enhance interaction between patients and their physicians, in supporting business decision making, and many other areas using technologies which commonly draw on an existing knowledge base of evidences and guidelines to provide logical reasoning-based expert advice. In general, the DSS’s logical reasoning architecture is based on a set of logic rules which can be predefined by experts (model driven) or by leverage computer-based methods like statistical pattern recognition and machine learning models (data driven).

The main advantage of DSSs is that they allow for combining different kinds of data allowing for defining a very accurate characterisation of the phenomenon under investigation. For example, it would be possible to combine video (face features), speech (voice features), neurophysiological (brain activity), and environmental (noise, lights, etc.) data to assess in real-time the users’ stress level and eventually suggest them to take a break accordingly. Data fusion mainly relies on ontologies. An ontology is a formal definition of concepts belonging to a domain and permits to describe a common framework for heterogeneous data.



Research on reasoning models focus on various areas, including the core engine optimizations, debugging, expressivity of rules and knowledge representation in general. Recent research included uncertainty in their proposed models leading to engines able to handle probabilistic rules. In this regard, the aim of the Research Topic is to collect the latest DSS, ontology, rule set model’s definition algorithms, implementations (e.g. model vs. data driven), and applications to be applied in everyday life, contexts, and various research areas, in which different data like neurophysiological signals (Electroencephalogram - EEG, Electrocardiogram - ECG, Electrodermal Activity - EDA), behavioural, subjective, environmental, video, audio are considered and combined for an objective and comprehensive user’s assessment and enhanced human-machine interaction.

Areas covered by this section include but are not limited to the following:

• Knowledge representation and reasoning

• Explainable AI (XAI) approaches for decision support

• Multimodal assessment

• Human – Machine teaming

• Teamwork assessment

• Ontology development

• Transfer learning

• Human–Computer Interaction

• Adaptive automation

• Human Performance Envelope (HPE)

• Wearable technologies

• Neuroergonomics

• Natural Language Processing (NLP)

• Reasoning under uncertainty



All types of manuscripts are considered, including original basic science reports, translational research, clinical studies, review articles, and methodology papers.



Dr. Gianluca Borghini

Prof. Hatice Gunes

Dr. Roberto Tedesco

Prof. Maurizio Atzori

Prof. Hong Zeng

Keywords: Decision Support Systems - Explainable Artificial Intelligence - Human Performance Envelope - Natural Language Processing - Neuroergonomics - Human – Machine teaming - Transfer learning - Wearable Technology - Neurophysiological assessment


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Decision Support Systems (DDS) have become quite popular in several contexts like health services to enhance interaction between patients and their physicians, in supporting business decision making, and many other areas using technologies which commonly draw on an existing knowledge base of evidences and guidelines to provide logical reasoning-based expert advice. In general, the DSS’s logical reasoning architecture is based on a set of logic rules which can be predefined by experts (model driven) or by leverage computer-based methods like statistical pattern recognition and machine learning models (data driven).

The main advantage of DSSs is that they allow for combining different kinds of data allowing for defining a very accurate characterisation of the phenomenon under investigation. For example, it would be possible to combine video (face features), speech (voice features), neurophysiological (brain activity), and environmental (noise, lights, etc.) data to assess in real-time the users’ stress level and eventually suggest them to take a break accordingly. Data fusion mainly relies on ontologies. An ontology is a formal definition of concepts belonging to a domain and permits to describe a common framework for heterogeneous data.



Research on reasoning models focus on various areas, including the core engine optimizations, debugging, expressivity of rules and knowledge representation in general. Recent research included uncertainty in their proposed models leading to engines able to handle probabilistic rules. In this regard, the aim of the Research Topic is to collect the latest DSS, ontology, rule set model’s definition algorithms, implementations (e.g. model vs. data driven), and applications to be applied in everyday life, contexts, and various research areas, in which different data like neurophysiological signals (Electroencephalogram - EEG, Electrocardiogram - ECG, Electrodermal Activity - EDA), behavioural, subjective, environmental, video, audio are considered and combined for an objective and comprehensive user’s assessment and enhanced human-machine interaction.

Areas covered by this section include but are not limited to the following:

• Knowledge representation and reasoning

• Explainable AI (XAI) approaches for decision support

• Multimodal assessment

• Human – Machine teaming

• Teamwork assessment

• Ontology development

• Transfer learning

• Human–Computer Interaction

• Adaptive automation

• Human Performance Envelope (HPE)

• Wearable technologies

• Neuroergonomics

• Natural Language Processing (NLP)

• Reasoning under uncertainty



All types of manuscripts are considered, including original basic science reports, translational research, clinical studies, review articles, and methodology papers.



Dr. Gianluca Borghini

Prof. Hatice Gunes

Dr. Roberto Tedesco

Prof. Maurizio Atzori

Prof. Hong Zeng

Keywords: Decision Support Systems - Explainable Artificial Intelligence - Human Performance Envelope - Natural Language Processing - Neuroergonomics - Human – Machine teaming - Transfer learning - Wearable Technology - Neurophysiological assessment


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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