Argumentation and eXplainable Artificial Intelligence (XAI) are closely related, as in the recent years, Argumentation has been used for providing Explainability to AI. Argumentation can show step by step how an AI System reaches a decision; it can provide reasoning over uncertainty and can find solutions when conflicting information is faced. In this survey, we elaborate over the topics of Argumentation and XAI combined, by reviewing all the important methods and studies, as well as implementations that use Argumentation to provide Explainability in AI. More specifically, we show how Argumentation can enable Explainability for solving various types of problems in decision-making, justification of an opinion, and dialogues. Subsequently, we elaborate on how Argumentation can help in constructing explainable systems in various applications domains, such as in Medical Informatics, Law, the Semantic Web, Security, Robotics, and some general purpose systems. Finally, we present approaches that combine Machine Learning and Argumentation Theory, toward more interpretable predictive models.

In the previous two decades, knowledge graphs (KGs) have evolved significantly, inspiring developers to build ever-more context-related KGs. Due to this development, artificial intelligence (AI) applications can now access open domain-specific information in a format that is both semantically rich and machine comprehensible. In this article, a framework that depicts functional design for indoor workspaces and urban adaptive design, in order to help architects, artists, and interior designers for the design and construction of an urban or indoor workspace, based on the emotions of human individuals, is introduced. For the creation of online adaptive environments, the framework may incorporate emotional, physiological, visual, and textual measures. Additionally, an information retrieval mechanism that extracts critical information from the framework in order to assist the architects, artists, and the interior designers is presented. The framework provides access to commonsense knowledge about the (re-)design of an urban area and an indoor workspace, by suggesting objects that need to be placed, and other modifications that can be applied to the location, in order to achieve positive emotions. The emotions referred reflect to the emotions experienced by an individual when being in the indoor or urban area, which are pointers for the functionality, the memorability, and the admiration of the location. The framework also performs semantic matching between entities from the web KG ConceptNet, using semantic knowledge from ConceptNet and WordNet, with the ones existing in the KG of the framework. The paper provides a set of predefined SPARQL templates that specifically handle the ontology upon which the knowledge retrieval system is based. The framework has an additional argumentation function that allows users to challenge the knowledge retrieval component findings. In the event that the user prevails in the reasoning, the framework will learn new knowledge.

In the field of domestic cognitive robotics, it is important to have a rich representation of knowledge about how household objects are related to each other and with respect to human actions. In this paper, we present a domain dependent knowledge retrieval framework for household environments which was constructed by extracting knowledge from the VirtualHome dataset (http://virtual-home.org). The framework provides knowledge about sequences of actions on how to perform human scaled tasks in a household environment, answers queries about household objects, and performs semantic matching between entities from the web knowledge graphs DBpedia, ConceptNet, and WordNet, with the ones existing in our knowledge graph. We offer a set of predefined SPARQL templates that directly address the ontology on which our knowledge retrieval framework is built, and querying capabilities through SPARQL. We evaluated our framework via two different user evaluations.

In the previous two decades, Knowledge Graphs (KGs) have evolved, inspiring developers to build ever-more context-related KGs. Because of this development, Artificial Intelligence (AI) applications can now access open domain-specific information in a format that is both semantically rich and machine comprehensible. In this article, we introduce the XR4DRAMA framework. The KG of the XR4DRAMA framework can represent data for media preparation and disaster management. More specifically, the KG of the XR4DRAMA framework can represent information about: (a) Observations and Events (e.g., data collection of biometric sensors, information in photos and text messages), (b) Spatio-temporal (e.g., highlighted locations and timestamps), (c) Mitigation and response plans in crisis (e.g., first responder teams). In addition, we provide a mechanism that allows Points of Interest (POI) to be created or updated based on videos, photos, and text messages sent by users. For improved disaster management and media coverage of a location, POI serve as markers to journalists and first responders. A task creation mechanism is also provided for the disaster management scenario with the XR4DRAMA framework, which indicates to first responders and citizens what tasks need to be performed in case of an emergency. Finally, the XR4DRAMA framework has a danger zone creation mechanism. Danger zones are regions in a map that are considered as dangerous for citizens and first responders during a disaster management scenario and are annotated by a severity score. The last two mechanisms are based on a Decision Support System (DSS).