Signal processing plays a vital role in various areas of engineering, particularly in industrial automation. Signal processing applications are based on a data flow paradigm, which uses directed graphs to describe the flow of data between operations. Although industrial control applications are based on standards such as IEC 61499, their syntax, and semantics differ from the data flow paradigm, creating a challenge in integrating signal processing applications into industrial automation systems. Modeling signal processing applications using IEC 61499 is not yet a common practice. This paper addresses this gap by proposing an approach to transform data flow models into IEC 61499 models. This approach enables the design and transformation of signal processing applications using data flow models, without requiring manual modeling or knowledge of the IEC 61499 standard. The model transformation is formally defined and subsequently implemented as a separate tool. A running example illustrates the transformation process, demonstrating that the model is both syntactically correct and executable.

When robots perform complex and context-dependent tasks in our daily lives, deviations from expectations can confuse users. Explanations of the robot’s reasoning process can help users to understand the robot intentions. However, when to provide explanations and what they contain are important to avoid user annoyance. We have investigated user preferences for explanation demand and content for a robot that helps with daily cleaning tasks in a kitchen. Our results show that users want explanations in surprising situations and prefer concise explanations that clearly state the intention behind the confusing action and the contextual factors that were relevant to this decision. Based on these findings, we propose two algorithms to identify surprising actions and to construct effective explanations for Belief-Desire-Intention (BDI) robots. Our algorithms can be easily integrated in the BDI reasoning process and pave the way for better human-robot interaction with context- and user-specific explanations.

Explainability has been acknowledged as a fundamental requirement for modern information systems. However, there are currently only few guidelines available to assist software professionals in dealing with this requirement and integrating it into systems. More precisely, there is a lack of frameworks and guidelines that help to define and operationalize explainability requirements. To address this need, we present a quality framework that aggregates external dependencies, characteristics of explanations, and evaluation methods to facilitate the analysis, operationalization, and evaluation of explainability requirements. We conducted a literature study to construct the framework and demonstrated its applicability by using it as a guideline for incorporating explanations into an existing navigation system. Finally, we evaluated the quality and effect of the explanations through an experiment within our case study. Our results show that the quality framework is applicable and beneficial in an industrial context and leads to the construction of explanations that increase usage frequency, system acceptance and user satisfaction.

While the importance of autonomous systems in our daily lives and in the industry increases, we have to ensure that this development is accepted by their users. A crucial factor for a successful cooperation between humans and autonomous systems is a basic understanding that allows users to anticipate the behavior of the systems. Due to their complexity, complete understanding is neither achievable, nor desirable. Instead, we propose self-explainability as a solution. A self-explainable system autonomously explains behavior that differs from anticipated behavior. As a first step towards this vision, we present an approach for detecting anomalous behavior that requires an explanation and for reducing the huge search space of possible reasons for this behavior by classifying it into classes with similar reasons. We envision our approach to be part of an explanation component that can be added to any autonomous system.

A key challenge in cyber-physical systems is to autonomously maintain system goals in the presence of uncertainties concerning the environment behaviour at runtime. Self-adaptivity has shown to be powerful to cope with this. It is usually based on a feedback loop that continuously evaluates the satisfaction of system goals and adapts the system in case of violations. However, most approaches rely on reactive adaptation, where the system triggers adaptations when goals are already violated. In contrast, proactive adaptation anticipates possible goal violations in the future and triggers adaptation in time. To enable this, the system maintains a model that allows for predicting the environment behaviour in the near future. However, these models are often heavyweight and resource-consuming to predict future behaviour (e.g. based on model checking). To overcome this problem, we present a lightweight approach for continuous learning of environment profiles and integrate it with resource-efficient rule-based adaptation. Moreover, we combine proactive adaptation that uses future predictions based on learned profiles, and reactive adaptation. We illustrate our ideas with an autonomous production system.

A key challenge in cyber-physical systems is their highly dynamic nature including changing system goals. Therefore, these systems have to autonomously manage their system goals and continuously evaluate their achievement at run-time. However, with the increasing complexity of system goals including, e.g., priorities, dependencies, and conflicts among goals, a binary or qualitative judgement of achievement of goals is not sufficient anymore. Instead, it is necessary to quantify the degree to which the goals are fulfilled in order to balance the cost-benefit ratio at run-time. In this paper, we present a hierarchical and modular goal model that allows for capturing complex relations between subgoals, e.g., dependencies and conflicts. We provide an algorithm that efficiently evaluates gradual achievement of goals at run-time. Due to the modular structure of our model and our evaluation, goals can easily be added, removed, and changed at run-time. With our approach, we a) ease the design of goal-aware autonomous systems by providing an explicit structure that emphasises relations between subgoals, b) provide an automatic quantification of the satisfaction of complex system goals that can be used to, e.g., evaluate autonomous decisions at runtime, and c) enable runtime management of changing system goals.