Morphogenetic engineering represents an interesting field in which models, frameworks and algorithms can be tested in order to study how self-* properties and emergent behaviours can arise in potentially complex and distributed systems. In this field, the morphogenetic model we will refer to is swarm chemistry, since a well known challenge in this dynamical process concerns discovering mechanisms for providing evolution within coalescing systems of particles. These systems consist in sets of moving particles able to self-organise in order to create shapes or geometrical formations that provide robustness towards external perturbations. We present a novel mechanism for providing evolutionary features in swarm chemistry that takes inspiration from artificial immune system literature, more specifically regarding idiotypic networks. Starting from a restricted set of chemical recipes, we show that the system evolves to new states, using an autonomous method of detecting new shapes and behaviours free from any human interaction.
{"title":"Artificial Immune System Driven Evolution in Swarm Chemistry","authors":"Nicola Capodieci, E. Hart, Giacomo Cabri","doi":"10.1109/SASO.2014.16","DOIUrl":"https://doi.org/10.1109/SASO.2014.16","url":null,"abstract":"Morphogenetic engineering represents an interesting field in which models, frameworks and algorithms can be tested in order to study how self-* properties and emergent behaviours can arise in potentially complex and distributed systems. In this field, the morphogenetic model we will refer to is swarm chemistry, since a well known challenge in this dynamical process concerns discovering mechanisms for providing evolution within coalescing systems of particles. These systems consist in sets of moving particles able to self-organise in order to create shapes or geometrical formations that provide robustness towards external perturbations. We present a novel mechanism for providing evolutionary features in swarm chemistry that takes inspiration from artificial immune system literature, more specifically regarding idiotypic networks. Starting from a restricted set of chemical recipes, we show that the system evolves to new states, using an autonomous method of detecting new shapes and behaviours free from any human interaction.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":"126 1","pages":"40-49"},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73620611","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}
The study of complex networks and collective dynamics occurring in biological, social and technical systems has experienced a massive surge of interest both from academia and industry. Many of the results on the mechanisms underlying the self-organized formation of complex dynamic networks in natural and man-made systems have been derived based on a statistical physics perspective. In this tutorial, we provide a basic introduction to this perspective which will help attendees to benefit from the vast literature on self-organization and self-adaptation phenomena available in the fields of network science and complex systems. We cover basic models and abstractions for the study of static complex networks as well as dynamical processes like, e.g., information diffusion, random walks, synchronization or the propagation of cascading failures. We further introduce recent advances in the study of dynamic (social) networks and demonstrate how the resulting methods can be practically applied in the engineering of self-organizing and self-adaptive distributed systems and protocols.
{"title":"Complex Structures and Collective Dynamics in Networked Systems: Foundations for Self-Adaptation and Self-Organization","authors":"Ingo Scholtes, M. Esch","doi":"10.1109/SASOW.2014.7","DOIUrl":"https://doi.org/10.1109/SASOW.2014.7","url":null,"abstract":"The study of complex networks and collective dynamics occurring in biological, social and technical systems has experienced a massive surge of interest both from academia and industry. Many of the results on the mechanisms underlying the self-organized formation of complex dynamic networks in natural and man-made systems have been derived based on a statistical physics perspective. In this tutorial, we provide a basic introduction to this perspective which will help attendees to benefit from the vast literature on self-organization and self-adaptation phenomena available in the fields of network science and complex systems. We cover basic models and abstractions for the study of static complex networks as well as dynamical processes like, e.g., information diffusion, random walks, synchronization or the propagation of cascading failures. We further introduce recent advances in the study of dynamic (social) networks and demonstrate how the resulting methods can be practically applied in the engineering of self-organizing and self-adaptive distributed systems and protocols.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":"65 1","pages":"1-2"},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85015234","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}
Modelling Self-managing Multi Agent Systems (MSMAS) is a software development methodology that facilitates designing and developing complex distributed systems based on the multiagent systems paradigm. MSMAS uses a declarative modelling style to capture system requirements by specifying four types of what we call system norms over: the system goals, the system roles, the business activities, and communications. MSMAS utilises system norms to capture system requirements in a formal language which can subsequently be monitored and verified at runtime. In this paper we present the main elements of MSMAS and introduce MSMAS defined norm types. We model the life cycle of MSMAS norms as non-atomic activities and formally express them as Event Calculus (EC) theories. Our acclimatisation of MSMAS system norms as first-order EC allows for reasoning with a metric time representation which we illustrate through a monitoring example of two execution traces to verify the system compliance with its intended design requirements and show how to detect any violation of norms.
{"title":"Run-Time Verification of MSMAS Norms Using Event Calculus","authors":"Emad Eldeen Elakehal, M. Montali, J. Padget","doi":"10.1109/SASOW.2014.31","DOIUrl":"https://doi.org/10.1109/SASOW.2014.31","url":null,"abstract":"Modelling Self-managing Multi Agent Systems (MSMAS) is a software development methodology that facilitates designing and developing complex distributed systems based on the multiagent systems paradigm. MSMAS uses a declarative modelling style to capture system requirements by specifying four types of what we call system norms over: the system goals, the system roles, the business activities, and communications. MSMAS utilises system norms to capture system requirements in a formal language which can subsequently be monitored and verified at runtime. In this paper we present the main elements of MSMAS and introduce MSMAS defined norm types. We model the life cycle of MSMAS norms as non-atomic activities and formally express them as Event Calculus (EC) theories. Our acclimatisation of MSMAS system norms as first-order EC allows for reasoning with a metric time representation which we illustrate through a monitoring example of two execution traces to verify the system compliance with its intended design requirements and show how to detect any violation of norms.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":"1 1","pages":"110-115"},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85555425","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}
T. Kohler, Jan-Philipp Steghöfer, D. Busquets, J. Pitt
Resource allocation problems are an important part of many distributed autonomous systems. In sensor networks, they determine which nodes get to use the communication links, in SmartGrid applications they decree which electric vehicle batteries are loaded, and in autonomous power management they select which generators produce the power required to satisfy the overall load. These cases have been considered in the literature before under the aspect of demand satisfaction: how well can distributed algorithms with local knowledge approximate the best allocation. A factor that has been ignored, however, is fairness: how fair is the resource allocation and -- in extension -- the distribution of revenue, wear, or recovery time. In this paper, we bring together previously disjoint approaches on dynamic distributed resource allocation and on fairness in electronic institutions. We show that fair allocations based on Ostrom's principles and on Rescher's canons of distributive justice create value in repeated resource allocations. We apply the scheme to solve the multi-objective problem of distributing load to generators fairly based on demands made by the individual generators. Our evaluation shows that a fair distribution increases satisfaction of the individual agents while reducing the hazard of optimising the problem in the short-term at the cost of long-term robustness and stability.
{"title":"The Value of Fairness: Trade-offs in Repeated Dynamic Resource Allocation","authors":"T. Kohler, Jan-Philipp Steghöfer, D. Busquets, J. Pitt","doi":"10.1109/SASO.2014.12","DOIUrl":"https://doi.org/10.1109/SASO.2014.12","url":null,"abstract":"Resource allocation problems are an important part of many distributed autonomous systems. In sensor networks, they determine which nodes get to use the communication links, in SmartGrid applications they decree which electric vehicle batteries are loaded, and in autonomous power management they select which generators produce the power required to satisfy the overall load. These cases have been considered in the literature before under the aspect of demand satisfaction: how well can distributed algorithms with local knowledge approximate the best allocation. A factor that has been ignored, however, is fairness: how fair is the resource allocation and -- in extension -- the distribution of revenue, wear, or recovery time. In this paper, we bring together previously disjoint approaches on dynamic distributed resource allocation and on fairness in electronic institutions. We show that fair allocations based on Ostrom's principles and on Rescher's canons of distributive justice create value in repeated resource allocations. We apply the scheme to solve the multi-objective problem of distributing load to generators fairly based on demands made by the individual generators. Our evaluation shows that a fair distribution increases satisfaction of the individual agents while reducing the hazard of optimising the problem in the short-term at the cost of long-term robustness and stability.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":"118 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75772023","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}
The notion of a computational field has been proposed as a unifying abstraction for constructing and reasoning about large and self-organising networks of devices, focusing on the computations and coordination of aggregates of devices instead of individual behaviour. Recently, firm mathematical foundations have been established for this approach, in the form of a minimal universal field calculus and a more restricted syntax that guarantees self-stabilisation. We now aim to raise the abstraction level for system construction by identifying a collection of general and reusable "building block" algorithms. By functional combination of these building blocks, it is possible to construct complex adaptive behaviours. Moreover, the building blocks we present are all self-stabilising, ensuring that any system constructed from them is guaranteed to rapidly converge to a correct behaviour.
{"title":"Building Blocks for Aggregate Programming of Self-Organising Applications","authors":"J. Beal, Mirko Viroli","doi":"10.1109/SASOW.2014.6","DOIUrl":"https://doi.org/10.1109/SASOW.2014.6","url":null,"abstract":"The notion of a computational field has been proposed as a unifying abstraction for constructing and reasoning about large and self-organising networks of devices, focusing on the computations and coordination of aggregates of devices instead of individual behaviour. Recently, firm mathematical foundations have been established for this approach, in the form of a minimal universal field calculus and a more restricted syntax that guarantees self-stabilisation. We now aim to raise the abstraction level for system construction by identifying a collection of general and reusable \"building block\" algorithms. By functional combination of these building blocks, it is possible to construct complex adaptive behaviours. Moreover, the building blocks we present are all self-stabilising, ensuring that any system constructed from them is guaranteed to rapidly converge to a correct behaviour.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":"80 1","pages":"8-13"},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77797677","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}
In this work, we present the implementation of a novel Agent-based Modelling and Simulation approach for the Schema Matching problem called "Schema Matching Agent-based Simulation" (SMAS). Our solution aims at generating high quality schema matchings with minimum uncertainty.
{"title":"Towards an Agent-Based Simulation Model for Schema Matching","authors":"Hicham Assoudi, H. Lounis","doi":"10.1109/SASO.2014.42","DOIUrl":"https://doi.org/10.1109/SASO.2014.42","url":null,"abstract":"In this work, we present the implementation of a novel Agent-based Modelling and Simulation approach for the Schema Matching problem called \"Schema Matching Agent-based Simulation\" (SMAS). Our solution aims at generating high quality schema matchings with minimum uncertainty.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":"19 1","pages":"197-198"},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73786736","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}
Reinforcement learning is a widespread mechanism for adapting the individual behaviour of autonomous agents, while norms are a well-established means for organising the common conduct of these agents. Therefore, norm-governed reinforcement learning agents appear to be a powerful bio-inspired, as well as socio-inspired, paradigm for the construction of decentralised, self-adapting, self-organising systems. However, the convergence of learning and norms is not as straightforward as it appears: learning can 'misguide' the development of norms, while norms can 'stall' the learning of optimal behaviour. In this paper, we investigate the self-governance of learning agents, or more specifically the domain-independent (de)construction at run-time of prescriptive systems from scratch, for and by learning agents, without any agent having complete information of the system. Most importantly, because prescriptions may also misguide agents, we allow them to repeal any misguiding prescriptions that have previously been enacted. Simulations illustrate the approach with experimental insights regarding scalability and timeliness in the construction of prescriptive systems.
{"title":"Self-Governance by Transfiguration: From Learning to Prescription Changes","authors":"Régis Riveret, A. Artikis, J. Pitt, E. Nepomuceno","doi":"10.1109/SASO.2014.19","DOIUrl":"https://doi.org/10.1109/SASO.2014.19","url":null,"abstract":"Reinforcement learning is a widespread mechanism for adapting the individual behaviour of autonomous agents, while norms are a well-established means for organising the common conduct of these agents. Therefore, norm-governed reinforcement learning agents appear to be a powerful bio-inspired, as well as socio-inspired, paradigm for the construction of decentralised, self-adapting, self-organising systems. However, the convergence of learning and norms is not as straightforward as it appears: learning can 'misguide' the development of norms, while norms can 'stall' the learning of optimal behaviour. In this paper, we investigate the self-governance of learning agents, or more specifically the domain-independent (de)construction at run-time of prescriptive systems from scratch, for and by learning agents, without any agent having complete information of the system. Most importantly, because prescriptions may also misguide agents, we allow them to repeal any misguiding prescriptions that have previously been enacted. Simulations illustrate the approach with experimental insights regarding scalability and timeliness in the construction of prescriptive systems.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":"15 1","pages":"70-79"},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81604206","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}
Biological organisms show a remarkable flexibility in how they organize their behavior and adapt it to changed circumstances. In this paper, we apply some of the more interesting concepts from biological theory to cyber-physical systems, especially those in such remote or hazardous environments that we cannot expect our control of them to be adequate for success or even survival. We propose a software architecture based on our Wrappings infrastructure, and show how it manages all of the resources necessary for autonomous operation, how it uses interacting planning and decision processes to organize its activity (determining that it cannot do something is one important aspect of the decision and planning processes), and how it uses various analyses of detailed behavioral instrumentation to improve that behavior or determine that improvement is not possible. We describe several difficult questions that arise when implementing our system architecture, and discuss how they might be addressed.
{"title":"Process Planning and Self-Improvement in Cyber-Physical Systems","authors":"C. Landauer, K. Bellman","doi":"10.1109/SASOW.2014.24","DOIUrl":"https://doi.org/10.1109/SASOW.2014.24","url":null,"abstract":"Biological organisms show a remarkable flexibility in how they organize their behavior and adapt it to changed circumstances. In this paper, we apply some of the more interesting concepts from biological theory to cyber-physical systems, especially those in such remote or hazardous environments that we cannot expect our control of them to be adequate for success or even survival. We propose a software architecture based on our Wrappings infrastructure, and show how it manages all of the resources necessary for autonomous operation, how it uses interacting planning and decision processes to organize its activity (determining that it cannot do something is one important aspect of the decision and planning processes), and how it uses various analyses of detailed behavioral instrumentation to improve that behavior or determine that improvement is not possible. We describe several difficult questions that arise when implementing our system architecture, and discuss how they might be addressed.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":"71 1","pages":"144-149"},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85917669","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}
Activity Recognition (AR) Systems more and more find their way into our daily lives, from monitoring daily activities to support in medical care. However, such systems tend to be used with narrowly defined specifications, demanding for application-dependent setup and configuration by their users. A long term goal are autonomous systems, being able to work with no (or minimal) user interaction. Closely related to that vision is the ability of autonomously adding further input sources (e.g., sensors) at run-time, leading to an increased dimensionality of the input-space. Our approach aims at systematically investigating methods necessary for the creation of self-adapting classification systems. This includes an architecture, based on Organic Computing (OC) principles, as well as the development of measures for comparing probabilistic models and procedures for evaluating classifiers of different dimensionality. With such evaluation techniques, systems should be able to adapt their system model at run-time in a self-organized manner. Besides self-improvement (adding a new sensor) we also address the problem of self-healing (replacing a sensor that dropped out).
{"title":"Self-Adapting Multi-sensor Systems: A Concept for Self-Improvement and Self-Healing Techniques","authors":"Martin Jänicke, B. Sick, P. Lukowicz, D. Bannach","doi":"10.1109/SASOW.2014.22","DOIUrl":"https://doi.org/10.1109/SASOW.2014.22","url":null,"abstract":"Activity Recognition (AR) Systems more and more find their way into our daily lives, from monitoring daily activities to support in medical care. However, such systems tend to be used with narrowly defined specifications, demanding for application-dependent setup and configuration by their users. A long term goal are autonomous systems, being able to work with no (or minimal) user interaction. Closely related to that vision is the ability of autonomously adding further input sources (e.g., sensors) at run-time, leading to an increased dimensionality of the input-space. Our approach aims at systematically investigating methods necessary for the creation of self-adapting classification systems. This includes an architecture, based on Organic Computing (OC) principles, as well as the development of measures for comparing probabilistic models and procedures for evaluating classifiers of different dimensionality. With such evaluation techniques, systems should be able to adapt their system model at run-time in a self-organized manner. Besides self-improvement (adding a new sensor) we also address the problem of self-healing (replacing a sensor that dropped out).","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":"1 1","pages":"128-136"},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91091120","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}
Jan-Philipp Steghöfer, Gerrit Anders, Jan Kantert, C. Müller-Schloer, W. Reif
We discuss the implementation of a normative system in an open self-organising system, including an OCL-based format for norms we settled on, the design of the feedback loops for their observation and adaptation, as well as a corresponding software architecture. These elements allow designers to quickly integrate a normative sub-system in a MAS and to define norms based on existing design concepts.
{"title":"An Effective Implementation of Norms in Trust-Aware Open Self-Organising Systems","authors":"Jan-Philipp Steghöfer, Gerrit Anders, Jan Kantert, C. Müller-Schloer, W. Reif","doi":"10.1109/SASOW.2014.34","DOIUrl":"https://doi.org/10.1109/SASOW.2014.34","url":null,"abstract":"We discuss the implementation of a normative system in an open self-organising system, including an OCL-based format for norms we settled on, the design of the feedback loops for their observation and adaptation, as well as a corresponding software architecture. These elements allow designers to quickly integrate a normative sub-system in a MAS and to define norms based on existing design concepts.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":"48 1","pages":"76-77"},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84867324","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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