David Burth Kurka, J. Pitt, Peter R. Lewis, Alina Patelli, A. Ekárt
Non-compliance is an expected outcome in norm-governed multi-agent systems, justifying the specification of monitoring, enforcement and sanctioning mechanisms. However, the simplistic assumption is that the violated norm is 'right' and the violating agent is 'wrong'. More complex situations involve selective common-sense non-application of a sanction (the principled violation of policy), situations where the norm is 'right' but those applying it are wrong, and situations where the norm itself is 'wrong'. In complex organisations, the iron law of oligarchy implies that these latter situations will arise and will need to be identified, but cannot be addressed by conventional sanctioning mechanisms that focus on individual violation with respect to supposedly infallible norms and/or norm enforcers. In this paper, we investigate the role of collective disobedience as a transformative mechanism for rule-or ruler-change, through the integration of the principled violation of policy, interactional justice and social learning. Our experiments provide evidence that the inclusion of formal mechanisms for pardoning and reformation enable agents to identify unfairness and displace an oligarchic clique through a process of revolution.
{"title":"Disobedience as a Mechanism of Change","authors":"David Burth Kurka, J. Pitt, Peter R. Lewis, Alina Patelli, A. Ekárt","doi":"10.1109/SASO.2018.00011","DOIUrl":"https://doi.org/10.1109/SASO.2018.00011","url":null,"abstract":"Non-compliance is an expected outcome in norm-governed multi-agent systems, justifying the specification of monitoring, enforcement and sanctioning mechanisms. However, the simplistic assumption is that the violated norm is 'right' and the violating agent is 'wrong'. More complex situations involve selective common-sense non-application of a sanction (the principled violation of policy), situations where the norm is 'right' but those applying it are wrong, and situations where the norm itself is 'wrong'. In complex organisations, the iron law of oligarchy implies that these latter situations will arise and will need to be identified, but cannot be addressed by conventional sanctioning mechanisms that focus on individual violation with respect to supposedly infallible norms and/or norm enforcers. In this paper, we investigate the role of collective disobedience as a transformative mechanism for rule-or ruler-change, through the integration of the principled violation of policy, interactional justice and social learning. Our experiments provide evidence that the inclusion of formal mechanisms for pardoning and reformation enable agents to identify unfairness and displace an oligarchic clique through a process of revolution.","PeriodicalId":405522,"journal":{"name":"2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131451612","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}
Collaboration in teams is essential in robot collectives. In order to achieve goals, individual robots would otherwise not be able to accomplish. In a such a distributed and highly dynamic system, a global coordination might not be possible. In this paper, we analyse static team affiliations, defined at deployment time, and compare its efficiency against dynamic team affiliations generated during runtime using random selection. Since operators might not be able to determine all dynamic aspects of the given environment at the time of deployment, we further propose a novel, goal-aware approach to affiliate each robot with a team. This approach brings together insights from biology, sociology, and psychology. In this novel approach, robots only operate on aggregated information from the network which is potentially changing during runtime. Finally, we also introduce an approach to select a team affiliation during runtime using machine learning techniques. Using 60,000 randomised scenarios, we analyse the efficiency and further discuss the different benefits and drawbacks of the proposed approaches.
{"title":"Goal-Aware Team Affiliation in Collectives of Autonomous Robots","authors":"Lukas Esterle","doi":"10.1109/SASO.2018.00020","DOIUrl":"https://doi.org/10.1109/SASO.2018.00020","url":null,"abstract":"Collaboration in teams is essential in robot collectives. In order to achieve goals, individual robots would otherwise not be able to accomplish. In a such a distributed and highly dynamic system, a global coordination might not be possible. In this paper, we analyse static team affiliations, defined at deployment time, and compare its efficiency against dynamic team affiliations generated during runtime using random selection. Since operators might not be able to determine all dynamic aspects of the given environment at the time of deployment, we further propose a novel, goal-aware approach to affiliate each robot with a team. This approach brings together insights from biology, sociology, and psychology. In this novel approach, robots only operate on aggregated information from the network which is potentially changing during runtime. Finally, we also introduce an approach to select a team affiliation during runtime using machine learning techniques. Using 60,000 randomised scenarios, we analyse the efficiency and further discuss the different benefits and drawbacks of the proposed approaches.","PeriodicalId":405522,"journal":{"name":"2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127813012","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":"[Title page iii]","authors":"","doi":"10.1109/saso.2018.00002","DOIUrl":"https://doi.org/10.1109/saso.2018.00002","url":null,"abstract":"","PeriodicalId":405522,"journal":{"name":"2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)","volume":"165 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127380691","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}
Mirgita Frasheri, Baran Çürüklü, Mikael Esktröm, A. Papadopoulos
Adaptive autonomy plays a major role in the design of multi-robots and multi-agent systems, where the need of collaboration for achieving a common goal is of primary importance. In particular, adaptation becomes necessary to deal with dynamic environments, and scarce available resources. In this paper, a mathematical framework for modelling the agents' willingness to interact and collaborate, and a dynamic adaptation strategy for controlling the agents' behavior, which accounts for factors such as progress toward a goal and available resources for completing a task among others, are proposed. The performance of the proposed strategy is evaluated through a fire rescue scenario, where a team of simulated mobile robots need to extinguish all the detected fires and save the individuals at risk, while having limited resources. The simulations are implemented as a ROS-based multi agent system, and results show that the proposed adaptation strategy provides a more stable performance than a static collaboration policy.
{"title":"Adaptive Autonomy in a Search and Rescue Scenario","authors":"Mirgita Frasheri, Baran Çürüklü, Mikael Esktröm, A. Papadopoulos","doi":"10.1109/SASO.2018.00026","DOIUrl":"https://doi.org/10.1109/SASO.2018.00026","url":null,"abstract":"Adaptive autonomy plays a major role in the design of multi-robots and multi-agent systems, where the need of collaboration for achieving a common goal is of primary importance. In particular, adaptation becomes necessary to deal with dynamic environments, and scarce available resources. In this paper, a mathematical framework for modelling the agents' willingness to interact and collaborate, and a dynamic adaptation strategy for controlling the agents' behavior, which accounts for factors such as progress toward a goal and available resources for completing a task among others, are proposed. The performance of the proposed strategy is evaluated through a fire rescue scenario, where a team of simulated mobile robots need to extinguish all the detected fires and save the individuals at risk, while having limited resources. The simulations are implemented as a ROS-based multi agent system, and results show that the proposed adaptation strategy provides a more stable performance than a static collaboration policy.","PeriodicalId":405522,"journal":{"name":"2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121396706","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":"[Publisher's information]","authors":"","doi":"10.1109/saso.2018.00033","DOIUrl":"https://doi.org/10.1109/saso.2018.00033","url":null,"abstract":"","PeriodicalId":405522,"journal":{"name":"2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121665122","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}
Nicolas Verstaevel, J. Georgé, C. Bernon, M. Gleizes
In a context of a rapidly growing population of elderly people, this paper introduces a novel method for behavioural anomaly detection relying on a self-organized learning process. This method first models the Circadian Activity Rhythm of a set of sensors and compares it to a nominal profile to determine variations in patients' activities. The anomalies are detected by a multi-agent system as a linear relation of those variations, weighted by influence parameters. The problem of adaptation to a particular patient then becomes the problem of learning the adequate influence parameters. Those influence parameters are self-adjusted, using feedback provided at any time by the medical staff. This approach is evaluated on a synthetic environment and results show both the capacity to effectively learn influence parameters and the resilience of this system to parameter size. Details on the ongoing real-world experimentation are provided.
{"title":"A Self-Organized Learning Model for Anomalies Detection: Application to Elderly People","authors":"Nicolas Verstaevel, J. Georgé, C. Bernon, M. Gleizes","doi":"10.1109/SASO.2018.00018","DOIUrl":"https://doi.org/10.1109/SASO.2018.00018","url":null,"abstract":"In a context of a rapidly growing population of elderly people, this paper introduces a novel method for behavioural anomaly detection relying on a self-organized learning process. This method first models the Circadian Activity Rhythm of a set of sensors and compares it to a nominal profile to determine variations in patients' activities. The anomalies are detected by a multi-agent system as a linear relation of those variations, weighted by influence parameters. The problem of adaptation to a particular patient then becomes the problem of learning the adequate influence parameters. Those influence parameters are self-adjusted, using feedback provided at any time by the medical staff. This approach is evaluated on a synthetic environment and results show both the capacity to effectively learn influence parameters and the resilience of this system to parameter size. Details on the ongoing real-world experimentation are provided.","PeriodicalId":405522,"journal":{"name":"2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121459002","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":"[Title page i]","authors":"","doi":"10.1109/saso.2018.00001","DOIUrl":"https://doi.org/10.1109/saso.2018.00001","url":null,"abstract":"","PeriodicalId":405522,"journal":{"name":"2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127552681","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}
Basic democracy has been proposed as a means of collective self-governance distinct from liberal democracy, i.e. it is a conventional rule-based system of empowerment, decision-making and public action that is both prior to and separate from concerns such as justice, morality and rights. In this paper, we investigate the automation of basic democracy as a framework for the self-organisation of collective governance in open systems. We present a series of simulation experiments in civic participation, legislation, and entrenchment, which demonstrate how an open system founded on the principles of basic democracy can mitigate the risks of oligarchy, autocracy and majoritarian tyranny. This implies that basic democracy can provide a stable platform for implementing value-driven requirements such as the supply of sustainable institutions and 'liberal' values like distributive justice. We conclude by considering the implications for the development and management of socio-technical systems, specifically that these systems should be 'supplied' based on the theory of basic democracy, codified as principles of democracy by design.
{"title":"Democracy by Design: Basic Democracy and the Self-Organisation of Collective Governance","authors":"J. Pitt, Josiah Ober","doi":"10.1109/SASO.2018.00013","DOIUrl":"https://doi.org/10.1109/SASO.2018.00013","url":null,"abstract":"Basic democracy has been proposed as a means of collective self-governance distinct from liberal democracy, i.e. it is a conventional rule-based system of empowerment, decision-making and public action that is both prior to and separate from concerns such as justice, morality and rights. In this paper, we investigate the automation of basic democracy as a framework for the self-organisation of collective governance in open systems. We present a series of simulation experiments in civic participation, legislation, and entrenchment, which demonstrate how an open system founded on the principles of basic democracy can mitigate the risks of oligarchy, autocracy and majoritarian tyranny. This implies that basic democracy can provide a stable platform for implementing value-driven requirements such as the supply of sustainable institutions and 'liberal' values like distributive justice. We conclude by considering the implications for the development and management of socio-technical systems, specifically that these systems should be 'supplied' based on the theory of basic democracy, codified as principles of democracy by design.","PeriodicalId":405522,"journal":{"name":"2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)","volume":"41 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120916467","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}
Vladimir Podolskiy, Anshul Jindal, M. Gerndt, Yury Oleynik
With the introduction of autoscaling, clouds have strengthened their position as self-adaptive systems. Nevertheless, the reactive nature of the existing autoscaling solutions provided by major Infrastructure-as-a-Service (IaaS) cloud services providers (CSP) heavily limits the ability of cloud applications for self-adaptation. The major reason of such limitations is the necessity for the manual configuration of the autoscaling rules. With the evolution of monitoring systems, it became possible to employ the data-driven approaches to derive the parameters of scaling rules in order to enable the autoscaling in advance, i.e. the predictive autoscaling. The change in the amount of requests to microservices could be considered as a reason to adapt the virtual infrastructure underlying the cloud application. By forecasting the amount of requests to cloud application, it is possible to estimate the upcoming demand to replicate the microservices in advance. Hence, anticipation of the demand on the cloud application helps to evolve its self-adaptive properties. In the scope of the paper, the authors have tested various extrapolation models on the real anonymized requests time series data for 261 microservices provided by the industry partner Instana. The tested models are: various seasonal ARIMA models with GARCH modifications and outliers detection, exponential smoothing models, singular spectrum analysis (SSA), support vector regression (SVR), and simple linear regression. In order to evaluate the accuracy of these models, an interval score was used. The time required to fit and use each model was also evaluated. Comparative results of this research and the classification of forecasting models based on the interval accuracy score and model fitting time are provided in the paper. The study provides an approach to evaluate the quality of forecasting models to be used for self-adapting cloud applications and virtual infrastructure.
{"title":"Forecasting Models for Self-Adaptive Cloud Applications: A Comparative Study","authors":"Vladimir Podolskiy, Anshul Jindal, M. Gerndt, Yury Oleynik","doi":"10.1109/SASO.2018.00015","DOIUrl":"https://doi.org/10.1109/SASO.2018.00015","url":null,"abstract":"With the introduction of autoscaling, clouds have strengthened their position as self-adaptive systems. Nevertheless, the reactive nature of the existing autoscaling solutions provided by major Infrastructure-as-a-Service (IaaS) cloud services providers (CSP) heavily limits the ability of cloud applications for self-adaptation. The major reason of such limitations is the necessity for the manual configuration of the autoscaling rules. With the evolution of monitoring systems, it became possible to employ the data-driven approaches to derive the parameters of scaling rules in order to enable the autoscaling in advance, i.e. the predictive autoscaling. The change in the amount of requests to microservices could be considered as a reason to adapt the virtual infrastructure underlying the cloud application. By forecasting the amount of requests to cloud application, it is possible to estimate the upcoming demand to replicate the microservices in advance. Hence, anticipation of the demand on the cloud application helps to evolve its self-adaptive properties. In the scope of the paper, the authors have tested various extrapolation models on the real anonymized requests time series data for 261 microservices provided by the industry partner Instana. The tested models are: various seasonal ARIMA models with GARCH modifications and outliers detection, exponential smoothing models, singular spectrum analysis (SSA), support vector regression (SVR), and simple linear regression. In order to evaluate the accuracy of these models, an interval score was used. The time required to fit and use each model was also evaluated. Comparative results of this research and the classification of forecasting models based on the interval accuracy score and model fitting time are provided in the paper. The study provides an approach to evaluate the quality of forecasting models to be used for self-adapting cloud applications and virtual infrastructure.","PeriodicalId":405522,"journal":{"name":"2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)","volume":"47 21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124716243","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 2018 Steering Committee","authors":"","doi":"10.1109/saso.2018.00007","DOIUrl":"https://doi.org/10.1109/saso.2018.00007","url":null,"abstract":"","PeriodicalId":405522,"journal":{"name":"2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)","volume":"12 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134219598","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}
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