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Explanation is necessary for humans to understand and accept decisions made by an AI system when the system's goal is known. It is even more important when the AI system makes decisions in multi-agent environments where the human does not know the systems' goals since they may depend on other agents' preferences. In such situations, explanations should aim to increase user satisfaction, taking into account the system's decision, the user's and the other agents' preferences, the environment settings and properties such as fairness, envy and privacy. Generating explanations that will increase user satisfaction is very challenging; to this end, we propose a new research direction: Explainable decisions in Multi-Agent Environments (xMASE). We then review the state of the art and discuss research directions towards efficient methodologies and algorithms for generating explanations that will increase users' satisfaction from AI systems' decisions in multi-agent environments.

With the increasing number of companies actively collecting data, the number of data breaches has exploded. It can be observed that affected often discontinue their relationship with the company. In order to avoid this kind of response, companies should develop and deploy their own recovery strategies. In our paper, we examined the effectiveness of different recovery strategies geared towards retaining customer satisfaction immediately after a data breach. We examine a data breach of a fitness tracker that varies in severity and tests the recovery actions compensation and remorse. The results found that customer satisfaction depends on the severity of the data breach, while combining compensation and remorse together demonstrates itself as the best strategy for increasing customer satisfaction in almost all cases. However, it was also discovered that in case of a severe data breach, customer satisfaction is difficult to restore and in the end remorse has virtually no effect.

Unlocking and managing flexibility is an important contribution to the integration of renewable energy and an efficient and resilient operation of the power system. In this paper, we discuss how the potential of a fleet of battery-electric transportation vehicles can be used to provide frequency containment reserve. To this end, we first examine the use case in detail and then present the system designed to meet this challenge. We give an overview of the tasks and individual sub-components, consisting of (a) an artificial neural network to predict the availability of Automated Guided Vehicles (AGVs) day-ahead, (b) a heuristic approach to compute marketable flexibility, (c) a simulation to check the plausibility of flexibility schedules, (d) a multi-agent system to continuously monitor and control the AGVs and (e) the integration of fleet flexibility into a virtual power plant. We also present our approach to the economic analysis of this provision of a system-critical service in a logistical context characterised by high uncertainty and variability.

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