Volume 61, Issue 4 - Swarm Intelligence / Guest Editor: Rolf Wanka

Applying principles of swarm intelligence to the control of autonomous systems in industry can advance our ability to manage complexity in prominent and high-cost sectors—such as transportation, logistics, and construction. In swarm robotics, the exclusive use of decentralized control relying on local communication and information provides the key advantage first of scalability, and second of robustness against failure points. These are directly useful in certain applied tasks that can be studied in laboratory environments, such as self-assembly and self-organized construction. In this article, we give a brief introduction to swarm robotics for a broad audience, with the intention of targeting future industrial applications. We then present a summary of four examples of our recently published research results with simple models. First, we present our approach to self-reconfiguration, which uses collective adjustment of swarm density in a dynamic setting. Second, we describe our robot experiments for self-organized material deployment in structured and semi-structured environments, applicable to braided composites. Third, we present our machine learning approach for self-assembly, motivated as a simple model developing foundational methods, which generates self-organizing robot behaviors to form emergent patterns. Fourth, we describe our experiments implementing a bioinspired model in a robot swarm, where we show self-healing of damage as the robots collectively locate a resource. Overall, the four examples we present concern robustness, scalability, and self-X features, which we propose as potentially relevant to future research in swarm robotics applied to industry sectors. We summarize these approaches as an introduction to our recent research, targeting the broad audience of this journal.

This paper presents a survey on different showcases for potential measures on particle swarm optimization (PSO). First, a potential is analyzed to prove convergence to non-optimal points. Second, one can apply a minor modification to PSO to prevent convergence to non-optimal points by using an easy potential measure. Finally, analyzing this potential measure yields a reliable stopping criterion for the modified PSO.

A discrete particle swarm optimization (PSO) algorithm is a randomized search heuristic for discrete optimization problems. A fundamental question about randomized search heuristics is how long it takes, in expectation, until an optimal solution is found. We give an overview of recent developments related to this question for discrete PSO algorithms. In particular, we give a comparison of known upper and lower bounds of expected runtimes and briefly discuss the techniques used to obtain these bounds.

Automated theorem proving systems validate or refute whether a conjecture is a logical consequence of a given set of assumptions. Higher-order provers have been successfully applied in academic and industrial applications, such as planning, software and hardware verification, or knowledge-based systems. Recent studies moreover suggest that automation of higher-order logic, in particular, yields effective means for reasoning within expressive non-classical logics, enabling a whole new range of applications, including computer-assisted formal analysis of arguments in metaphysics. My work focuses on the theoretical foundations, effective implementation and practical application of higher-order theorem proving systems. This article briefly introduces higher-order reasoning in general and presents an overview of the design and implementation of the higher-order theorem prover Leo-III. In the second part, some example applications of Leo-III are discussed.

Software and software-controlled technical systems play an increasing role in our daily lives. In cyber-physical systems, which connect the physical and the digital world, software does not only influence how we perceive and interact with our environment but software also makes decisions that influence our behavior. Therefore, the ability of software systems to explain their behavior and decisions will become an important property that will be crucial for their acceptance in our society. We call software systems with this ability explainable software systems . In the past, we have worked on methods and tools to design explainable software systems. In this article, we highlight some of our work on how to design explainable software systems. More specifically, we describe an architectural framework for designing self-explainable software systems, which is based on the MAPE-loop for self-adaptive systems. Afterward, we show that explainability is also important for tools that are used by engineers during the development of software systems. We show examples from the area of requirements engineering where we use techniques from natural language processing and neural networks to help engineers comprehend the complex information structures embedded in system requirements.

Data-centric disciplines like machine learning and data science have become major research areas within computer science and beyond. However, the development of research processes and tools did not keep pace with the rapid advancement of the disciplines, resulting in several insufficiently tackled challenges to attain reproducibility, replicability, and comparability of achieved results. In this discussion paper, we review existing tools, platforms and standardization efforts for addressing these challenges. As a common ground for our analysis, we develop an open science centred process model for machine learning research, which combines openness and transparency with the core processes of machine learning and data science. Based on the features of over 40 tools, platforms and standards, we list the, in our opinion, 11 most central platforms for the research process in this paper. We conclude that most platforms cover only parts of the requirements for overcoming the identified challenges.