ACM Transactions on Autonomous and Adaptive Systems (TAAS)最新文献

{"title":"Efficient and Robust Emergence of Norms through Heuristic Collective Learning","authors":"Jianye Hao, Jun Sun, Guangyong Chen, Zan Wang, Chao Yu, Zhong Ming","doi":"10.1145/3127498","DOIUrl":"https://doi.org/10.1145/3127498","url":null,"abstract":"In multiagent systems, social norms serves as an important technique in regulating agents’ behaviors to ensure effective coordination among agents without a centralized controlling mechanism. In such a distributed environment, it is important to investigate how a desirable social norm can be synthesized in a bottom-up manner among agents through repeated local interactions and learning techniques. In this article, we propose two novel learning strategies under the collective learning framework, collective learning EV-l and collective learning EV-g, to efficiently facilitate the emergence of social norms. Extensive simulations results show that both learning strategies can support the emergence of desirable social norms more efficiently and be applicable in a wider range of multiagent interaction scenarios compared with previous work. The influence of different topologies is investigated, which shows that the performance of all strategies is robust across different network topologies. The influences of a number of key factors (neighborhood size, actions space, population size, fixed agents and isolated subpopulations) on norm emergence performance are investigated as well.","PeriodicalId":377078,"journal":{"name":"ACM Transactions on Autonomous and Adaptive Systems (TAAS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134367226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hierarchical Optimal Control Method for Controlling Large-Scale Self-Organizing Networks","authors":"Naomi Kuze, D. Kominami, K. Kashima, T. Hashimoto, M. Murata","doi":"10.1145/3124644","DOIUrl":"https://doi.org/10.1145/3124644","url":null,"abstract":"Self-organization has the potential for high scalability, adaptability, flexibility, and robustness, which are vital features for realizing future networks. The convergence of self-organizing control, however, is slow in some practical applications in comparison with control by conventional deterministic systems using global information. It is therefore important to facilitate the convergence of self-organizing controls. In controlled self-organization, which introduces an external controller into self-organizing systems, the network is controlled to guide systems to a desired state. Although existing controlled self-organization schemes could achieve the same state, it is difficult for an external controller to collect information about the network and to provide control inputs to the network, especially when the network size is large. This is because the computational cost for designing the external controller and for calculating the control inputs increases rapidly as the number of nodes in the network becomes large. Therefore, we partition a network into several sub-networks and introduce two types of controllers, a central controller and several sub-controllers that control the network in a hierarchical manner. In this study, we propose a hierarchical optimal feedback mechanism for self-organizing systems and apply this mechanism to potential-based self-organizing routing. Simulation results show that the proposed mechanism improves the convergence speed of potential-field construction (i.e., route construction) up to 10.6-fold with low computational and communication costs.","PeriodicalId":377078,"journal":{"name":"ACM Transactions on Autonomous and Adaptive Systems (TAAS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131309136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electronic Social Capital for Self-Organising Multi-Agent Systems","authors":"Patricio E. Petruzzi, J. Pitt, D. Busquets","doi":"10.1145/3124642","DOIUrl":"https://doi.org/10.1145/3124642","url":null,"abstract":"It is a recurring requirement in open systems, such as networks, distributed systems, and socio-technical systems, that a group of agents must coordinate their behaviour for the common good. In those systems—where agents are heterogeneous—unexpected behaviour can occur due to errors or malice. Agents whose practices free-ride the system can be accepted to a certain level; however, not only do they put the stability of the system at risk, but they also compromise the agents that behave according to the system’s rules. In social systems, it has been observed that social capital is an attribute of individuals that enhances their ability to solve collective action problems. Sociologists have studied collective action through human societies and observed that social capital plays an important role in maintaining communities though time as well as in simplifying the decision-making in them. In this work, we explore the use of Electronic Social Capital for optimising self-organised collective action. We developed a context-independent Electronic Social Capital framework to test this hypothesis. The framework comprises a set of handlers that capture events from the system and update three different forms of social capital: trustworthiness, networks, and institutions. Later, a set of metrics are generated by the forms of social capital and used for decision-making. The framework was tested in different scenarios such as two-player games, n-player games, and public goods games. The experimental results show that social capital optimises the outcomes (in terms of long-term satisfaction and utility), reduces the complexity of decision-making, and scales with the size of the population. This work proposes an alternative solution using Electronic Social Capital to represent and reason with qualitative, instead of traditional quantitative, values. This solution could be embedded into socio-technical systems to incentivise collective action without commodifying the resources or actions in the system.","PeriodicalId":377078,"journal":{"name":"ACM Transactions on Autonomous and Adaptive Systems (TAAS)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114324348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evolved Control of Natural Plants","authors":"D. Hofstadler, Mostafa Wahby, Mary Katherine Heinrich, Heiko Hamann, Payam Zahadat, P. Ayres, T. Schmickl","doi":"10.1145/3124643","DOIUrl":"https://doi.org/10.1145/3124643","url":null,"abstract":"Mixing societies of natural and artificial systems can provide interesting and potentially fruitful research targets. Here we mix robotic setups and natural plants in order to steer the motion behavior of plants while growing. The robotic setup uses a camera to observe the plant and uses a pair of light sources to trigger phototropic response, steering the plant to user-defined targets. An evolutionary robotic approach is used to design a controller for the setup. Initially, preliminary experiments are performed with a simple predetermined controller and a growing bean plant. The plant behavior in response to the simple controller is captured by image processing, and a model of the plant tip dynamics is developed. The model is used in simulation to evolve a robot controller that steers the plant tip such that it follows a number of randomly generated target points. Finally, we test the simulation-evolved controller in the real setup controlling a natural bean plant. The results demonstrate a successful crossing of the reality gap in the setup. The success of the approach allows for future extensions to more complex tasks including control of the shape of plants and pattern formation in multiple plant setups.","PeriodicalId":377078,"journal":{"name":"ACM Transactions on Autonomous and Adaptive Systems (TAAS)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133609401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-Adaptation to Device Distribution in the Internet of Things","authors":"J. Beal, Mirko Viroli, Danilo Pianini, Ferruccio Damiani","doi":"10.1145/3105758","DOIUrl":"https://doi.org/10.1145/3105758","url":null,"abstract":"A key problem when coordinating the behaviour of spatially situated networks, like those typically found in the Internet of Things (IoT), is adaptation to changes impacting network topology, density, and heterogeneity. Computational goals for such systems, however, are often dependent on geometric properties of the continuous environment in which the devices are situated rather than the particulars of how devices happen to be distributed through it. In this article, we identify a new property of distributed algorithms, eventual consistency, which guarantees that computation converges to a final state that approximates a predictable limit, based on the continuous environment, as the density and speed of devices increases. We then identify a large class of programs that are eventually consistent, building on prior results on the field calculus computational model (Beal et al. 2015; Viroli et al. 2015a) that identify a class of self-stabilizing programs. Finally, we confirm through simulation of IoT application scenarios that eventually consistent programs from this class can provide resilient behavior where programs that are only converging fail badly.","PeriodicalId":377078,"journal":{"name":"ACM Transactions on Autonomous and Adaptive Systems (TAAS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122796062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hyper-Learning Algorithms for Online Evolution of Robot Controllers","authors":"Fernando Silva, L. Correia, A. Christensen","doi":"10.1145/3092815","DOIUrl":"https://doi.org/10.1145/3092815","url":null,"abstract":"A long-standing goal in artificial intelligence and robotics is synthesising agents that can effectively learn and adapt throughout their lifetime. One open-ended approach to behaviour learning in autonomous robots is online evolution, which is part of the evolutionary robotics field of research. In online evolution approaches, an evolutionary algorithm is executed on the robots during task execution, which enables continuous optimisation and adaptation of behaviour. Despite the potential for automatic behaviour learning, online evolution has not been widely adopted because it often requires several hours or days to synthesise solutions to a given task. In this respect, research in the field has failed to develop a prevalent algorithm able to effectively synthesise solutions to a large number of different tasks in a timely manner. Rather than focusing on a single algorithm, we argue for more general mechanisms that can combine the benefits of different algorithms to increase the performance of online evolution of robot controllers. We conduct a comprehensive assessment of a novel approach called online hyper-evolution (OHE). Robots executing OHE use the different sources of feedback information traditionally associated with controller evaluation to find effective evolutionary algorithms during task execution. First, we study two approaches: OHE-fitness, which uses the fitness score of controllers as the criterion to select promising algorithms over time, and OHE-diversity, which relies on the behavioural diversity of controllers for algorithm selection. We then propose a novel class of techniques called OHE-hybrid, which combine diversity and fitness to search for suitable algorithms. In addition to their effectiveness at selecting suitable algorithms, the different OHE approaches are evaluated for their ability to construct algorithms by controlling which algorithmic components should be employed for controller generation (e.g., mutation, crossover, among others), an unprecedented approach in evolutionary robotics. Results show that OHE (i) facilitates the evolution of controllers with high performance, (ii) can increase effectiveness at different stages of evolution by combining the benefits of multiple algorithms over time, and (iii) can be effectively applied to construct new algorithms during task execution. Overall, our study shows that OHE is a powerful new paradigm that allows robots to improve their learning process as they operate in the task environment.","PeriodicalId":377078,"journal":{"name":"ACM Transactions on Autonomous and Adaptive Systems (TAAS)","volume":"2014 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127524289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Defining Emergent Software Using Continuous Self-Assembly, Perception, and Learning","authors":"Roberto Rodrigues Filho, Barry Porter","doi":"10.1145/3092691","DOIUrl":"https://doi.org/10.1145/3092691","url":null,"abstract":"Architectural self-organisation, in which different configurations of software modules are dynamically assembled based on the current context, has been shown to be an effective way for software to self-optimise over time. Current approaches to this rely heavily on human-led definitions: models, policies, and processes to control how self-organisation works. We present the case for a paradigm shift to fully emergent computer software that places the burden of understanding entirely into the hands of software itself. These systems are autonomously assembled at runtime from discovered constituent parts and their internal health and external deployment environment continually monitored. An online, unsupervised learning system then uses runtime adaptation to continuously explore alternative system assemblies and locate optimal solutions. Based on our experience over the past 3 years, we define the problem space of emergent software and present a working case study of an emergent web server as a concrete example of the paradigm. Our results demonstrate two main aspects of the problem space for this case study: that different assemblies of behaviour are optimal in different deployment environment conditions and that these assemblies can be autonomously learned from generalised perception data while the system is online.","PeriodicalId":377078,"journal":{"name":"ACM Transactions on Autonomous and Adaptive Systems (TAAS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129138885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SASO 2016","authors":"Giacomo Cabri, Gauthier Picard, Niranjan Suri","doi":"10.1145/3127332","DOIUrl":"https://doi.org/10.1145/3127332","url":null,"abstract":"The IEEE International Conference on Self-Adapting and Self-Organizing Systems (SASO) is the main forum for studying and discussing the foundations of a principled approach to engineering systems, networks, and services based on self-adaptation and self-organization. Over the past decade, it has consolidated as the primary scientific conference for sharing ideas on algorithms, technologies, tools, and applications across a wide range of scientific fields. In 2016, the conference was hosted by the University of Augsburg, in Augsburg, Germany; its scientific program comprised full papers, short papers, poster and demo presentations, workshops, doctoral symposium and tutorials. This special issue of ACM TAAS champions some of the most solid research results of SASO 2016, presenting selected, revised, and extended best articles.","PeriodicalId":377078,"journal":{"name":"ACM Transactions on Autonomous and Adaptive Systems (TAAS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122870488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Budget-Driven Scheduling of Scientific Workflows in IaaS Clouds with Fine-Grained Billing Periods","authors":"M. A. Rodriguez, R. Buyya","doi":"10.1145/3041036","DOIUrl":"https://doi.org/10.1145/3041036","url":null,"abstract":"With the advent of cloud computing and the availability of data collected from increasingly powerful scientific instruments, workflows have become a prevailing mean to achieve significant scientific advances at an increased pace. Scheduling algorithms are crucial in enabling the efficient automation of these large-scale workflows, and considerable effort has been made to develop novel heuristics tailored for the cloud resource model. The majority of these algorithms focus on coarse-grained billing periods that are much larger than the average execution time of individual tasks. Instead, our work focuses on emerging finer-grained pricing schemes (e.g., per-minute billing) that provide users with more flexibility and the ability to reduce the inherent wastage that results from coarser-grained ones. We propose a scheduling algorithm whose objective is to optimize a workflow’s execution time under a budget constraint; quality of service requirement that has been overlooked in favor of optimizing cost under a deadline constraint. Our proposal addresses fundamental challenges of clouds such as resource elasticity, abundance, and heterogeneity, as well as resource performance variation and virtual machine provisioning delays. The simulation results demonstrate our algorithm’s responsiveness to environmental uncertainties and its ability to generate high-quality schedules that comply with the budget constraint while achieving faster execution times when compared to state-of-the-art algorithms.","PeriodicalId":377078,"journal":{"name":"ACM Transactions on Autonomous and Adaptive Systems (TAAS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125129261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feature Construction for Controlling Swarms by Visual Demonstration","authors":"K. K. Budhraja, J. Winder, T. Oates","doi":"10.1145/3084541","DOIUrl":"https://doi.org/10.1145/3084541","url":null,"abstract":"Agent-based modeling is a paradigm of modeling dynamic systems of interacting agents that are individually governed by specified behavioral rules. Training a model of such agents to produce an emergent behavior by specification of the emergent (as opposed to agent) behavior is easier from a demonstration perspective. While many approaches involve manual behavior specification via code or reliance on a defined taxonomy of possible behaviors, the meta-modeling framework in Miner [2010] generates mapping functions between agent-level parameters and swarm-level parameters, which are re-usable once generated. This work builds on that framework by integrating demonstration by image or video. The demonstrator specifies spatial motion of the agents over time and retrieves agent-level parameters required to execute that motion. The framework, at its core, uses computationally cheap image-processing algorithms. Our work is tested with a combination of primitive visual feature extraction methods (contour area and shape) and features generated using a pre-trained deep neural network in different stages of image featurization. The framework is also evaluated for its potential using complex visual features for all image featurization stages. Experimental results show significant coherence between demonstrated behavior and predicted behavior based on estimated agent-level parameters specific to the spatial arrangement of agents.","PeriodicalId":377078,"journal":{"name":"ACM Transactions on Autonomous and Adaptive Systems (TAAS)","volume":"167 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126735944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}