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Deep learning (DL) has greatly advanced audio classification, yet the field is limited by the scarcity of large-scale benchmark datasets that have propelled progress in other domains. While AudioSet is a pivotal step to bridge this gap as a universal-domain dataset, its restricted accessibility and limited range of evaluation use cases challenge its role as the sole resource. Therefore, we introduce BirdSet, a large-scale benchmark data set for audio classification focusing on avian bioacoustics. BirdSet surpasses AudioSet with over 6,800 recording hours ($\uparrow17\%$) from nearly 10,000 classes ($\uparrow18\times$) for training and more than 400 hours ($\uparrow7\times$) across eight strongly labeled evaluation datasets. It serves as a versatile resource for use cases such as multi-label classification, covariate shift or self-supervised learning. We benchmark six well-known DL models in multi-label classification across three distinct training scenarios and outline further evaluation use cases in audio classification. We host our dataset on Hugging Face for easy accessibility and offer an extensive codebase to reproduce our results.

Cracks are omnipresent in materials and lead to billions of dollars in losses annually due to catastrophic and spectacular failures. Nondestructive wave-based methods are used to identify cracks, but these methods are cumbersome and require experts, leading to limited investigation. This research propose MicroCracksAttNet50E model that leverages numerical data to detect and localize damage in materials and structures, with a particular focus on microcracks that are imperceptible to the naked eye or conventional imaging methods but have the potential to develop into larger, hazardous fissures. The paper also includes a comparative analysis between the current study and the previous work, specifically evaluating the model that performed best in the prior paper (1D-DenseNet-Resize&Conv). Despite having approximately eight times fewer layers and over 200,000 fewer trainable parameters than DENSE variants, MicroCracksAttNet50E achieves similar or even better performance, with an accuracy of 0.860 and a precision of 0.881, compared to the best-performing DENSE model with an accuracy of 0.836 and a precision of 0.875. This improvement primarily highlights the effectiveness of the attention mechanism in MicroCracksAttNet50E, which focuses on critical areas to detect smaller cracks more accurately.

Damage in metals, composites, and cemented porous solids, in the form of cracks, inclusions, and voids, is a nontrivial problem. Many experimental, numerical, and analytical methods have been proposed in the past, with some recent models deploying neural networks. However, past methods often lack the accuracy and precision needed to identify microcracks. This paper presents the MicroCracksMetaNet50E (MCMN) deep learning model, inspired by Meta's Segment Anything Model (SAM). MCMN is trained with numerical data produced by an advanced mesoscale numerical model for spatial crack detection inside various materials. MicroCracksMetaNet50E achieves an accuracy of 0.867% and a precision of 0.906% in identifying microcracks. The robust performance of MCMN is highlighted, showcasing a notable advancement that its capabilities and propels the field into uncharted territories by expanding oppor-tunities for the comprehensive exploration of additional datasets. The method could be adopted for damage detection in metals and composites in manufacturing as well as structural health monitoring.

Adapting to a dynamic environment is a critical challenge in deploying robust systems that will be tasked with transmitting media streams in 5G networks. The Web Real-Time Communication (WebRTC) protocol is one of the most popular solutions for real-time communication, providing sub-second latency. This paper deals with a model-free Deep Reinforcement Learning approach designed to improve the quality of user experience by controlling the data rate of media streams transmitted in the uplink direction by a moving, remote-controlled device. The model incorporates WebRTC-compliant metrics to facilitate its integration into real-world applications and aims to maximize the value of a reward function specifically designed to match user perception of video streams. Training and evaluation are performed in an active, online manner within a 5G simulation environment based on the OMNeT++ network simulator, with the addition of key WebRTC mechanisms. The results are compared to the Google Congestion Control algorithm, a baseline WebRTC rate adaptation mechanism. The results, validated on different scenarios, demonstrate the ability of the proposed Deep Reinforcement Learning model to maintain higher rates at comparable levels of loss rate and delay times to the baseline, thus providing a better quality of experience for an operator in the remote center, resulting in the more reliable control of the device.

Technological systems have been a part of human life since prehistory. Although they initially took the form of passive tools, such as axes and spoons, the Industrial Revolution saw the advent of powered, mechanized technology, operating “under it’s own steam,” without direct human control over every action. By integrating more complex information processing machinery, automation evolved into autonomy as decision-making and self-regulation became features of modern technology. Now, so-called intelligent systems, embodying techniques from the field of artificial intelligence (AI), are designed with the explicit intention of replicating rational behaviors and the sorts of things that minds do, inside technological systems.At the same time, the study of Artificial Life (ALife) (Langton, 1987) has explored the properties of living systems, both as they are found in nature, as they might be, and as humans can build them. This has exposed a large variety of mechanisms that produce qualities typically associated with life. Examples include self-organization, homeostasis, self-replication, evolution, learning, self-awareness, and many others besides.The Lifelike Computing Systems initiative (Stein et al., 2021b) aims to learn from the study of life and living systems to develop new, useful, “lifelike” systems; a further aim is to identify when such features are of value. The focus of this research direction is primarily on engineered technological systems broadly within the domain of computing.The notion of “lifelike computing” is not intended to separate itself from or replace previous initiatives; in a large number of cases, there are already technologies and research efforts that strongly lean toward lifelike computing systems in specific aspects. Building on a long and highly successful tradition in biologically inspired computing, the “lifelike” vision not only seeks inspiration in the living world but also seeks to replicate its qualities explicitly in technological systems. Indeed, we cannot claim that all bio-inspired systems remain lifelike, nor is this in general even always a desirable outcome for those designing bio-inspired systems. The agenda also goes beyond fundamental ALife research, often rightly exploratory in nature, because it focuses explicitly on building purposeful and reliable technological systems for people, based on ALife principles. Therefore the vision of explicit replication of lifelike qualities in technological systems of value to humanity marks a sharpening of focus.This special issue is a follow-up to the workshop series “Lifelike Computing Systems,” held at the International Conference on Artificial Life in 2020 and 2021 (Stein et al., 2021a), and again in 2022 (Stein et al., 2023). The workshop series hosted diverse talks showcasing early-stage research and work in progress, with topics ranging from plasticity in technical systems to artificial DNA, from self-explaining systems to realistic humanoid and animal robots.We have therefore solicited papers that explore and contribute to the discussion on research questions we deem key to be further explored: Which qualities of life are of high relevance and benefit for the engineering of lifelike computing systems useful to people? Why? How?How can we integrate and combine insights and methodological approaches from existing, related research initiatives, such as cybernetics, self-aware computing, organic computing, and autonomic computing?Which methods from domains like artificial life, bio-inspired computing, artificial intelligence, and self-adaptive and self-organizing systems contribute to achieving lifelike features of computing systems?When is more “lifelike” technology appropriate? What are the challenges associated with embedding technology that is more “lifelike” in society? How can these be tackled?This special issue represents an opportunity for more mature work emerging from this line of research to be presented. It contains four papers that together provide a review, analysis, and critique of the integration of lifelike properties into engineered systems, in many cases proposing concrete recommendations for future research directions and methods.In “Lessons from the Evolutionary Computation Bestiary,” Campelo and Aranha explore and critique the explosion of metaphor-centered metaheuristic methods that have been published in recent years and that claim to be inspired by—in their view—increasingly absurd natural phenomena. Examples surveyed include several different types of birds, mammals, fish, and invertebrates; soccer and volleyball; and even reincarnation, zombies, and gods. The authors acknowledge that metaphors can be powerful inspiration and explanatory tools and that, indeed, the field of metaheuristics has a long history of finding inspiration in natural systems, starting from evolution strategies, genetic algorithms, and ant colony optimization. However, they question the value of the emergence of hundreds of highly similar variants of essentially the same algorithm under different labels. The authors have curated a “bestiary” of such variants over the years, and their article in this issue reviews this, arguing that this proliferation has been counterproductive to scientific progress in the field. They argue that it does little to improve our ability to understand and simulate biological systems and that it can actively impede an improved understanding of how to design and analyze global optimization techniques. The article discusses why this social phenomenon in research may have occurred in recent years and its negative consequences, ending with a call to improve the scientific soundness of metaheuristic research.In “Does the Field of Nature-Inspired Computing Contribute to Achieving Lifelike Features?,” Tzanetos asks whether all nature-inspired algorithms remain lifelike. The article considers the history of evolutionary computation and, as in the first article, the proliferation of many so-called nature-inspired techniques in recent years. The author juxtaposes the value of such techniques in solving hard problems with an analysis to support an argument that the mathematics of these techniques often does not match the source behavior faithfully. In these cases, can it be said that the algorithms are indeed “lifelike,” and if not, does that matter, so long as they provide value in terms of their ability to solve problems intelligently? The article argues that historically, there was greater alignment between the algorithmic models and source behaviors, but this is often not seen in more recent attempts. The article ends by discussing if there is a need for new lifelike features of algorithms, concluding that this is not helpful—instead presenting recommendations for future research in nature-inspired computing, which, the authors argue, would move the field in “the right direction.”In “Assessing Model Requirements for Explainable AI: A Template and Exemplary Case Study,” Heider et al. explore the explainability of decision support systems that use evolutionary rule-based machine learning techniques, more precisely, learning classifier systems (LCSs). Self-adaptive and self-optimizing systems are necessarily dynamic, yet for them to be accepted by people in sociotechnical settings, explanations for machine-made decisions are often essential. The authors argue that rule-based machine learning models, such as LCSs, present an opportunity for transparent machine learning models that naturally support access to explanations. To assist with designing and evaluating such models, they also propose a generic and thus broadly applicable questionnaire template. The template is demonstrated to provide valuable insights for the design of such LCS models in specific scenarios. The approach is illustrated in a manufacturing case study.Finally, in “Artificial Collective Intelligence Engineering: A Survey of Concepts and Perspectives,” Casadei surveys computational techniques based on or harnessing “collectiveness,” often seen in many living systems, to produce capabilities beyond what can be achieved with individual or monolithic systems. A key concept common to these techniques is that such systems can exploit a large number of individuals to produce intelligent collective behavior out of not-so-intelligent components. The article argues that there is a trend in some areas of engineering toward this way of designing technological systems, citing examples such as the Internet of Things, swarm robotics, and crowd computing and emphasizing that these technologies span many techniques, systems, and application areas. An essential finding of the review is that there is substantial fragmentation of this research, however, and that the so-called “verticality” of research communities makes a common fundamental understanding of such systems challenging to achieve. The author argues that an important challenge is identifying, placing in a common structure, and ultimately connecting the different areas and methods addressing intelligent collectives. As such, the article presents a set of questions aimed at mapping out collective intelligence research. It uses this to develop a set of preliminary notions, concepts, and perspectives, as well as associated research opportunities, to develop a more fundamental understanding of computational collective intelligence engineering.The guest editors thank the authors of papers submitted to the “Lifelike Computing Systems” special issue as well as the reviewers, who gave valuable feedback to all the authors. We would also like to thank the organizers of the ALife conferences that hosted the Lifelike Computing Systems workshops as well as all the speakers and participants who contributed to many vibrant debates that informed the direction of the final set of articles in this issue. Last, we thank the Board of Editors of Artificial Life for supporting this special issue.

The research initiative “self-improving system integration” (SISSY) was established with the goal to master the ever-changing demands of system organisation in the presence of autonomous subsystems, evolving architectures, and highly-dynamic open environments. It aims to move integration-related decisions from design-time to run-time, implying a further shift of expertise and responsibility from human engineers to autonomous systems . This introduces a qualitative shift from existing self-adaptive and self-organising systems, moving from self-adaptation based on predefined variation types, towards more open contexts involving novel autonomous subsystems, collaborative behaviours, and emerging goals. In this article, we revisit existing SISSY research efforts and establish a corresponding terminology focusing on how SISSY relates to the broad field of integration sciences. We then investigate SISSY-related research efforts and derive a taxonomy of SISSY technology. This is concluded by establishing a research road-map for developing operational self-improving self-integrating systems.

No abstract available.

Self-adaptation has been proposed as a mechanism to counter complexity in control problems of technical systems. A major driver behind self-adaptation is the idea to transfer traditional design-time decisions to runtime and into the responsibility of systems themselves. To deal with unforeseen events and conditions, systems need creativity—typically realized by means of machine learning capabilities. Such learning mechanisms are based on different sources of knowledge. Feedback from the environment used for reinforcement purposes is probably the most prominent one within the self-adapting and self-organizing (SASO) systems community. However, the impact of other (sub-)systems on the success of the individual system’s learning performance has mostly been neglected in this context. In this article, we propose a novel methodology to identify effects of actions performed by other systems in a shared environment on the utility achievement of an autonomous system. Consider smart cameras (SC) as illustrating example: For goals such as 3D reconstruction of objects, the most promising configuration of one SC in terms of pan/tilt/zoom parameters depends largely on the configuration of other SCs in the vicinity. Since such mutual influences cannot be pre-defined for dynamic systems, they have to be learned at runtime. Furthermore, they have to be taken into consideration when self-improving their own configuration decisions based on a feedback loop concept, e.g., known from the SASO domain or the Autonomic and Organic Computing initiatives. We define a methodology to detect such influences at runtime, present an approach to consider this information in a reinforcement learning technique, and analyze the behavior in artificial as well as real-world SASO system settings.

Machine learning systems learn from data. It is not rare that those data are grouped into categories (also called classes). Thus, one of the goals of a learning algorithm is to find out and understand how to assign new, previously unknown data to the correct class. But, in many cases data is available or can be gathered at low costs, whereas acquiring the corresponding category (or class) involves high costs. This is precisely where active learning (AL) comes into its own: In order to reduce the annotation costs, it allows the learning system to deliberately select the samples which should be annotated. Subsequently, the selected samples are presented to an entity (e.g., humans, simulation systems, etc.), generally addressed under the term oracle, that provides the corresponding classes. Afterwards, the knowledge base of the learner is updated and, depending on a stopping criterion, new labels are queried or not. Such a system is self-aware of its own imperfection, thus, it uses a selection strategy to determine the next most informative sample. Hitherto, it has been shown that AL is a powerful paradigm that evinces the desired results, provided that the oracles are omniscient. But, human oracles are prone to error, so for that reason we can ask ourself: Does AL still work with error prone and uncertain human annotators? In this article we present the results of an active learning case study conducted on 30000 images labeled by two humans and propose a new type of labeling for better solving AL problems with error-prone annotators.

Organic Computing (OC) and Autonomic Computing (AC) systems are distinct from conventional systems through their ability to self-adapt and to self-organise. However, these properties are just means and not the end. What really makes OC and AC systems useful is their ability to survive in a real world, i.e. to recover from disturbances and attacks from the outside world. This property is called robustness. In this paper, we propose a metric to gauge robustness in order to be able to quantitatively compare the effectiveness of different self-organising and self-adaptive system designs with each other. In the following, we apply this metric to three experimental application scenarios and discuss their usefulness.

In this paper, we show how an evolutionary rule-based machine learning technique can be applied to tackle the task of self-configuration of smart camera networks.More precisely, the Extended Classifier System (XCS) is utilized to learn a configuration strategy for the pan, tilt, and zoom of smart cameras.Thereby, we extend our previous approach, which is based on Q-Learning, by harnessing the generalization capability of Learning Classifier Systems (LCS), i.e. avoiding to separately approximate the quality of each possible (re-)configuration (action) in reaction to a certain situation (state).Instead, situations in which the same reconfiguration is adequate are grouped to one single rule.We demonstrate that our XCS-based approach outperforms the Q-learning method on the basis of empirical evaluations on scenarios of different severity.

XCSF is a rule-based on-line learning system that makes use of local learning concepts in conjunction with gradient-based approximation techniques. It is mainly used to learn functions, or rather regression problems, by means of dividing the problem space into smaller subspaces and approximate the function values linearly therein. In this paper, we show how local interpolation can be incorporated to improve the approximation speed and thus to decrease the system error. We describe how a novel interpolation component integrates into the algorithmic structure of XCSF and thereby augments the well-established separation into the performance, discovery and reinforcement component. To underpin the validity of our approach, we present and discuss results from experiments on three test functions of different complexity, i.e. we show that by means of the proposed strategies for integrating the locally interpolated values, the overall performance of XCSF can be improved.

Distributed self-organising systems often face conflicts if more than one entity tries to access a limited resource. In order to solve this conflict, research focuses on techniques for resource allocation considering different priorities. In this paper, we propose to tackle the decision problem of whom to assign the resource by means of a co-evolutionary approach. We investigate appropriate fitness estimations, representation schemes, and configuration of the underlying genetic operators. We demonstrate the convergence and efficiency of our approach using an exemplary system model.

Open, distributed systems face the challenge to maintain an appropriate operation performance even in the presence of bad behaving or malicious agents.A promising mechanism to counter the resulting negative impact of such agents is to establish technical trust.In this paper, we investigate strategies to improve the efficiency of trust mechanisms regarding the isolation of undesired participants by means of reputation and accusation techniques.We demonstrate the potential benefit of the developed techniques within simulations of a Trusted Desktop Computing Grid. TRUSTED DESKTOP GRIDWe use an open, distributed Trusted Desktop Grid (TDG) as application scenario to show and prove the effective application of distributed algorithms as well as Organic Computing (Müller-Schloer et al., 2011) methods.In this scenario, we use an open and heterogeneous Multi-Agent System (MAS) and we do not assume benevolence.The agents in the system cooperate to gain an advantage.The mechanism determining this cooperation is Trust.Because of the openness of the system, different agents may try to exploit it.They may be uncooperative, malfunctioning or even malicious.An agent, which acts on behalf of the user, is submitting jobs it wants to have calculated (Klejnowski, 132

Information and communication technology witnesses a raise of open, distributed systems that consist of various heterogeneous elements. Within such an environment, individual elements have to efficiently fulfil their goals, which may require cooperation with others. As a consequence, a variety of threats appears that need to be handled and circumvented in the entity’s behaviour. One major technical approach to provide a working environment for such systems is to introduce technical trust. In this paper, we present a basic threat model that comprises the most important challenges in this context – related to the basic system and the trust management, respectively. In order to illustrate the particular hazardous aspects, we discuss a Desktop Computing Grid application as scenario.

Distributed grid systems offer possible benefits in terms of fast computation of tasks. This is accompanied by potential drawbacks due to their openness, the heterogeneity of participants, and the unpredictability of agent behaviour, since agents have to be considered as black-boxes. The utilisation of technical trust within adaptive collaboration strategies has been shown to counter negative effects caused by these characteristics. A major challenge in this context is the presence of colluding attackers that try to exploit or damage the system in a coordinated fashion. Therefore, this paper presents a novel approach to detect and isolate such colluding attackers. The concept is based on observations of interaction patterns and derives a classification of agent communities. Within the evaluation, we demonstrate the benefit of the approach and highlight the highly reliable classification.

Robust and optimized agent behavior can be achieved by allowing for learning mechanisms within the underlying adaptive control strategies.Therefore, a classic feedback loop concept is used that chooses the best action for an observed situation -and learns the success by analyzing the achieved performance.This typically reflects only the local scope of an agent and neglects the existence of other agents with impact on the reward calculation.However, there are significant mutual influences among agents population.For instance, the success of a Smart Camera's control strategy depends (in terms of person detection or 3D-reconstruction) largely on the current strategy performed by its spatially neighbors.In this paper, we compare two concepts to consider such influences within the adaptive control strategy: Distributed W-Learning and Q-Learning in combination with mutual influence detection.We demonstrate that the performance can be improved significantly, if taking detected influences into account.

Multi-agent systems typically consist of a large set of agents that act on behalf of different users. Due to inherent dynamics in the interaction patterns of these agents, the system structure is typically self-organising and appears at runtime. Testing self-organising systems is a severe challenge that has not received the necessary attention within the last decade. Obviously, traditional testing methods reach their limitations and are hardly applicable due to the runtime characteristics and dynamics of self-organisation. In this paper, we argue that we run into a paradoxon if we try to utilise self-organising testing systems. In order to circumvent parts of the underlying limitations, we propose to combine such an approach with instrumented testing.

Mobile communication and data services face a rapid growth and result in an ever increasing demand of bandwidth. As alternative to investing in the infrastructure by splitting cells down to nano or pico scale, this paper introduces and evaluates a concept for traffic data offloading to ad-hoc communication in terms of device-to-device communication. The idea is to combine infrastructure-aided route discovery with local mobile ad-hoc network communication to handle traffic with a local focus, i.e. intra-cell or neighbouring-cell traffic. We analyse the concept in terms of latencies and packet delivery ratio by using Omnet++ simulations. We demonstrate that the approach comes with low overhead and allows for an efficient distribution of traffic depending on the expected communication range.

Increasing mobility and raising traffic demands lead to serious congestion problems. Intelligent traffic management systems try to alleviate this problem with optimised signalisation of traffic lights and dynamic route guidance (DRG). One solution for the former aspect is Organic Traffic Control (OTC), offering a self-organised, decentralised traffic control system. Based on OTC, this paper presents two proactive routing protocols, resembling techniques known from the Internet domain, applied to the traffic routing problem: Distance Vector Routing and Link State Routing. These protocols were adapted to utilise forecasts of traffic flows to offer anticipatory and time-dependant DRG for road users. The efficiency of these protocols is demonstrated with simulations of two Manhattan-type road networks under disturbed and undisturbed conditions. The results indicate their benefit in terms of lower travel times and emissions, even under low compliance rates.

Self-organized systems typically consist of distributed autonomous entities. An increasing part of such systems is characterized by openness and heterogeneity of participants. For instance, open desktop computing grids provide a framework for unrestrictedly joining in. However, openness and heterogeneity present severe challenges to the overall system’s stability and efficiency since uncooperative and even malicious participants are free to join. A promising solution for this problem is to introduce technical trust as a basis; however, in turn, the utilization of trust opens space for negative emergent behavior. This article introduces a system-wide observation and control loop that influences the self-organized behavior to provide a performant and robust platform for benevolent participants. Thereby, the observation part is responsible for gathering information and deriving a system description. We introduce a graph-based approach to identify groups of suspicious or malicious agents and demonstrate that this clustering process is highly successful for the considered stereotype agent behaviors. In addition, the controller part guides the system behavior by issuing norms that make use of incentives and sanctions. We further present a concept for closing the control loop and show experimental results that highlight the potential benefit of establishing such a control loop.

Desktop Computing Grids provide a framework for joining in and sharing resources with others. The result is a self-organised system that typically consists of numerous distributed autonomous entities. Openness and heterogeneity postulate severe challenges to the overall system’s stability and efficiency since uncooperative and even malicious participants are free to join. In this paper, we present a concept for identifying agents with exploitation strategies that works on a system-wide analysis of trust and work relationships. Afterwards, we introduce a system-wide control loop to isolate these malicious elements using a norm-based approach – due to the agents’ autonomy, we have to build on indirect control actions. Within simulations of a Desktop Computing Grid scenario, we show that the intelligent control loop works highly successful: these malicious elements are identified and isolated with a low error rate. We further demonstrate that the approach results in a significant increa se of utility for all participating benevolent agents.

The increasing coupling of former isolated systems towards an interwoven complex structure poses questions about the controllability and maintainability of the corresponding systems. This paper discusses challenges resulting from the growing complexity of technical systems and derives solution perspectives by utilising concepts from the domain of self-organising and self-optimising systems, in particular from the Organic Computing and Autonomic Computing initiatives.

Network protocols are deployed in highly dynamic environments, but typically configured with a static setup of configurations. The Organic Network Control system (ONC) has been developed to alter protocol configurations at runtime. ONC is equipped with online learning capabilities and safety considerations. This paper presents a first TCP-based study on how this approach can be applied to end-to-end protocols and simultaneously alleviating the drawbacks of a simulation-based optimisation procedure. The paper explains the developed algorithm and demonstrates the benefit of the solution in an Omnet++ scenario.

Grid Computing Systems are examples for open systems with heterogeneous and potentially malicious entities. Such systems can be controlled by system-wide intelligent control mechanisms working on trust relationships between these entities. Trust relationships are based on ratings among individual entities and represent system-wide information. In this paper, we propose to utilise a normative approach for the system-level control loop working on basis of these trust values. Thereby, a normative approach does not interfere with the entities’ autonomy and handles each system as black box. Implicit rules already existing in the system are turned into explicit norms – which in turn are becoming mandatory for all entities. This allows the distributed systems to derive the desired behaviour and cooperate in reaction to disturbed situations such as attacks.

The utilisation of cell phone networks increases continuously, especially driven by the introduction of new mobile services and smart phones. Network operators can follow two directions to deal with the problem: either install new hardware or increase the efficiency of the existing infrastructure. This paper presents a novel algorithm to improve the efficiency of current networks by allowing for a self-organised load-dependent reconfiguration of antennas. The algorithm is capable of identifying hotspot traffic, assigning this to a neighbouring cell, and learning the best strategy at runtime. This leads to a self-improving intelligent control mechanism. The simulation-based evaluation results demonstrate the potential benefit, while simultaneously keeping the hardware’s deterioration at a comparable level.

In this paper we present an approach to designing swarms of autonomous, adaptive robots. An observer/controller framework that has been developed as part of the Organic Computing initiative provides the architectural foundation for the individuals' adaptivity. Relying on an extended Learning Classifier System (XCS) in combination with adequate simulation techniques, it empowers the individuals to improve their collaborative performance and to adapt to changing goals and changing conditions. We elaborate on the conceptual details, and we provide first results addressing different aspects of our multi-layered approach. Not only for the sake of generalisability, but also because of its enormous transformative potential, we stage our research design in the domain of quad-copter swarms that organise to collaboratively fulfil spatial tasks such as maintenance of building facades. Our elaborations detail the architectural concept, provide examples of individual self-optimisation as well as of the optimisation of collaborative efforts, and we show how the user can control the swarm at multiple levels of abstraction. We conclude with a summary of our approach and an outlook on possible future steps.

System engineers are facing demanding challenges in terms of complexity and interconnectedness. Current research initiatives like Organic or Autonomic Computing propose to increase the freedom of the system to be developed using concepts like adaptivity and self-organisation. Adaptivity means that for such systems we defer a part of the design process from design time to runtime. Therefore, we need a runtime infrastructure which takes care of runtime modifications. This paper presents a meta-design process to develop adaptive systems and parametrise the runtime infrastructure in a unified way. To demonstrate the proposed design process, we applied it to a communication scenario and evaluate the resulting system in a realistic setting.

Today, high performance and feature rich surveillance systems are very costly as they require an expensive set of infrastructure components. As a consequence, such systems including, e.g., complex automatic video content analysis, are restricted to large scale applications, such as airports or train stations. In smaller settings, e.g. in shop surveillance, mostly low-cost display or record-only systems are in use. In this position paper we propose to combine two well-known approaches in order to make Intelligent Video Surveillance applicable and affordable in small to medium-scale scenarios. The proposal includes to combine the concept of Smart Cameras, i.e. cameras equipped with local processing resources, with the ideas of Cloud Computing, i.e. the on-demand provisioning of computing and storage services for complex calculations, and the management of large amounts of data, i.e. video storage. The former allows for the cost effective pre-processing of video data close to the sensor , while using the latter concept does not require large initial investments into expensive infrastructure components such as powerful compute servers. The paper presents research issues of the necessary system design, including precise system goal and system model aspects. Based on this, we discuss several research issues required to be addressed for solving the overall goals.

Systems capable of adapting to changing conditions have gained increasing attention in the last decade. Typically, vast situation and configuration spaces do not allow for using a predefined set of adaptation policies. Based on the principles of Organic Computing, a 3-layered learning architecture has been developed which is capable of coping with the problem by enabling self-adaptation and self-improvement. A major focus has been set on developing safety-based and efficient machine learning concepts founding on evolutionary search heuristics and rule-based learning. The general design has been successfully applied to safety critical real-world applications like urban traffic control and data communication protocols. This paper investigates the question for which class of technical systems the design is applicable. Thus, a generalised model based on mathematical functions is introduced and evaluated. The evaluation demonstrates that the approach works well for systems where the configuration spaces are steadily representable by functions of the situation space. This statement holds even in the presence of noise.

Due to steadily increasing mobility and the resulting rising traffic demands, serious congestion problems can be observed in many cities. One promising approach to alleviate the congestion effects is the coordination of the network's traffic signals in response to the traffic flow. The recently introduced Decentralised Progressive Signal Systems approach is an adaptive coordination mechanism for traffic signals in urban road networks that relies on local traffic data only. Since the decentralised process cannot lead to optimal results in some special cases, it is extended with an optional hierarchical component introduced in this paper. Based on a broader view on the current network traffic, this Regional Manager is responsible for determining which intersections are coordinated. The efficiency of the coordination determined by the Regional Manager is demonstrated in a simulation-based evaluation that considers the decentralised mechanism and an uncoordinated system for comparison.