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In evolutionary algorithms, the notion of diversity has been adopted from biology and is used to describe the distribution of a population of solution candidates. While it has been known that maintaining a reasonable amount of diversity often benefits the overall result of the evolutionary optimization process by adjusting the exploration/exploitation trade-off, little has been known about what diversity is optimal. We introduce the notion of productive fitness based on the effect that a specific solution candidate has some generations down the evolutionary path. We derive the notion of final productive fitness, which is the ideal target fitness for any evolutionary process. Although it is inefficient to compute, we show empirically that it allows for an a posteriori analysis of how well a given evolutionary optimization process hit the ideal exploration/exploitation trade-off, providing insight into why diversity-aware evolutionary optimization often performs better.

Systems are becoming increasingly more adaptive, using techniques like machine learning to enhance their behavior on their own rather than only through human developers programming them. We analyze the impact the advent of these new techniques has on the discipline of rigorous software engineering, especially on the issue of quality assurance. To this end, we provide a general description of the processes related to machine learning and embed them into a formal framework for the analysis of adaptivity, recognizing that to test an adaptive system a new approach to adaptive testing is necessary. We introduce scenario coevolution as a design pattern describing how system and test can work as antagonists in the process of software evolution. While the general pattern applies to large-scale processes (including human developers further augmenting the system), we show all techniques on a smaller-scale example of an agent navigating a simple smart factory. We point out new aspects in software engineering for adaptive systems that may be tackled naturally using scenario coevolution. This work is a substantially extended take on Gabor et al. (International symposium on leveraging applications of formal methods, Springer, pp 137–154, 2018).

We discuss the synergetic connection between quantum computing and artificial intelligence. After surveying current approaches to quantum artificial intelligence and relating them to a formal model for machine learning processes, we deduce four major challenges for the future of quantum artificial intelligence: (i) Replace iterative training with faster quantum algorithms, (ii) distill the experience of larger amounts of data into the training process, (iii) allow quantum and classical components to be easily combined and exchanged, and (iv) build tools to thoroughly analyze whether observed benefits really stem from quantum properties of the algorithm., 6 pages, 4 figures, accepted at the 1st International Workshop on Quantum Software Engineering (Q-SE 2020) at ICSE 2020 and to be published in the corresponding proceedings

Adversarial learning has been established as a successful paradigm in reinforcement learning. We propose a hybrid adversarial learner where a reinforcement learning agent tries to solve a problem while an evolutionary algorithm tries to find problem instances that are hard to solve for the current expertise of the agent, causing the intelligent agent to co-evolve with a set of test instances or scenarios. We apply this setup, called scenario co-evolution, to a simulated smart factory problem that combines task scheduling with navigation of a grid world. We show that the so trained agent outperforms conventional reinforcement learning. We also show that the scenarios evolved this way can provide useful test cases for the evaluation of any (however trained) agent.

As automatic optimization techniques find their way into industrial applications, the behavior of many complex systems is determined by some form of planner picking the right actions to optimize a given objective function. In many cases, the mapping of plans to objective reward may change due to unforeseen events or circumstances in the real world. In those cases, the planner usually needs some additional effort to adjust to the changed situation and reach its previous level of performance. Whenever we still need to continue polling the planner even during re-planning, it oftentimes exhibits severely lacking performance. In order to improve the planner's resilience to unforeseen change, we argue that maintaining a certain level of diversity amongst the considered plans at all times should be added to the planner's objective. Effectively, we encourage the planner to keep alternative plans to its currently best solution. As an example case, we implement a diversity-aware genetic algorithm using two different metrics for diversity (differing in their generality) and show that the blow in performance due to unexpected change can be severely lessened in the average case. We also analyze the parameter settings necessary for these techniques in order to gain an intuition how they can be incorporated into larger frameworks or process models for software and systems engineering., Comment: ICAC, 2018, Trento

In this paper, we extend an existing approach for monitoring work-pieces in Self-Organizing Industrial Systems (SOIS) to be applicable to more complex SOIS settings, where autonomous agents can move and regulate their velocity completely free. The problem of having infeasible many possible valid routes is handled by extracting significant sub-paths (pathlets), by which valid routes can be reconstructed. We show that meaningful pathlets can also be extracted for complex SOIS settings. By adding a temporal component to the dictionary, we are able to detect anomalous trajectories regarding spatial movement and temporal flow. Furthermore, environmental changes, like added/removed machines, can be located using information gained from the reconstruction process. Additionally, we show that the global movement behavior of the agents can be analyzed, too, by monitoring specific movement parameters for the dictionary pathlets. The data for the experiments are generated using a SOIS simulation which implements a state-of-the-art architecture for multi-agent systems.

In collective adaptive systems (CAS), adaptation can be implemented by optimization wrt. utility. Agents in a CAS may be self-interested, while their utilities may depend on other agents’ choices. Independent optimization of agent utilities may yield poor individual and global reward due to locally interfering individual preferences. Joint optimization may scale poorly, and is impossible if agents cannot expose their preferences due to privacy or security issues.

Recent work on learning in multi-agent systems (MAS) is concerned with the ability of self-interested agents to learn cooperative behavior. In many settings such as resource allocation tasks the lack of cooperative behavior can be seen as a consequence of wrong incentives. I.e., when agents can not freely exchange their resources then greediness is not uncooperative but only a consequence of reward maximization. In this work, we show how the introduction of markets helps to reduce the negative effects of individual reward maximization. To study the emergence of trading behavior in MAS we use Deep Reinforcement Learning (RL) where agents are self-interested, independent learners represented through Deep Q-Networks (DQNs). Specifically, we propose Action Traders, referring to agents that can trade their atomic actions in exchange for environmental reward. For empirical evaluation we implemented action trading in the Coin Game – and find that trading significantly increases social efficiency in terms of overall reward compared to agents without action trading.

For describing the complete state of complex environments with multiple mobile and autonomous agents, spatial layer models (SLM) are popular data structures. These models consist of several planes describing the spatial structure of selected features. Those SLM can directly be used for deep reinforcement learning tasks. However, detecting anomalies in such SLM poses two major challenges: the state space explosion in such settings and the spatial relations between the features. In this paper, we present a method for anomaly detection in SLM which solves both challenges by first extracting significant sub-patterns from training data and storing them in a dictionary. Afterwards, the entries of this dictionary are used for reconstructing SLM, which have to be validated. The resulting covering rate is an indicator for the (ab)normality of the given SLM. We show the applicability of our approach for a simple multi-agent scenario, and more complex smart factory scenarios with autonomous agents.

Summary Fluorescence nanoscopy, or super-resolution microscopy, has become an important tool in cell biological research. However, because of its usually inferior resolution in the depth direction (50–80 nm) and rapidly deteriorating resolution in thick samples, its practical biological application has been effectively limited to two dimensions and thin samples. Here, we present the development of whole-cell 4Pi single-molecule switching nanoscopy (W-4PiSMSN), an optical nanoscope that allows imaging of three-dimensional (3D) structures at 10- to 20-nm resolution throughout entire mammalian cells. We demonstrate the wide applicability of W-4PiSMSN across diverse research fields by imaging complex molecular architectures ranging from bacteriophages to nuclear pores, cilia, and synaptonemal complexes in large 3D cellular volumes., Graphical Abstract, Highlights • Whole-cell 4Pi single-molecule switching nanoscopy allows 10- to 20-nm 3D resolution • Refined hardware and new data analysis allow imaging of cells as thick as ∼10 μm • Using structure-averaging, the 3D shape of a bacteriophage can be resolved • Wide applicability across diverse research fields is demonstrated, A new super-resolution microscope opens a window deep into cells to image organelles and subcellular structures spanning large volumes.