Event-driven Temporal Models for Explanations - ETeMoX: Explaining Reinforcement Learning

Modern software systems are increasingly expected to show higher degrees of autonomy and self-management to cope with uncertain and diverse situations. As a consequence, autonomous systems can exhibit unexpected and surprising behaviours. This is exacerbated due to the ubiquity and complexity of Artificial Intelligence (AI)-based systems. This is the case of Reinforcement Learning (RL), where autonomous agents learn through trial-and-error how to find good solutions to a problem. Thus, the underlying decision-making criteria may become opaque to users that interact with the system and who may require explanations about the system’s reasoning. Available work for eXplainable Reinforcement Learning (XRL) offers different trade-offs: e.g. for runtime explanations, the approaches are model-specific or can only analyse results after-the-fact. Different from these approaches, this paper aims to provide an online model-agnostic approach for XRL towards trustworthy and understandable AI. We present ETeMoX, an architecture based on temporal models to keep track of the decision-making processes of RL systems. In cases where the resources are limited (e.g. storage capacity or time to response), the architecture also integrates complex event processing, an event-driven approach, for detecting matches to event patterns that need to be stored, instead of keeping the entire history. The approach is applied to a mobile communications case study that uses RL for its decision-making. In order to test the generalisability of our approach, three variants of the underlying RL algorithms are used: Q-Learning, SARSA and DQN. The encouraging results show that using the proposed configurable architecture, RL developers are able to obtain explanations about the evolution of a metric, relationships between metrics, and were able to track situations of interest happening over time windows

Recent advances in CE analysis of antibiotics and its use as chiral selectors

Antibiotics are a class of therapeutic molecules widely employed in both human and veterinary medicine. This article reviews the most recent advances in the analysis of antibiotics by CE in pharmaceutical, environmental, food, and biomedical fields. Emphasis is placed on the strategies to increase sensitivity as diverse off-line, in-line, and on-line preconcentration approaches and the use of different detection systems. The use of CE in the microchip format for the analysis of antibiotics is also reviewed in this article. Moreover, since the use of antibiotics as chiral selectors in CE has grown in the last years, a new section devoted to this aspect has been included. This review constitutes an update of previous published reviews and covers the literature published from June 2011 until June 2013