Pub Date : 2018-09-01DOI: 10.1109/FAS-W.2018.00048
Anthony Stein, Sven Tomforde, A. Diaconescu, J. Hähner, C. Müller-Schloer
The research initiative of self-improving and self-integrating systems (SISSY) emerged as response to the dramatically increasing complexity in information and communication technology. Such systems' ability of autonomous online learning has been identified as a key enabler for SISSY as well as for the broader field of self-adaptive and self-organizing (SASO) systems, since it provides the technical basis for dealing with the inherent dynamics of non-stationary environments that continually challenge these systems with unforeseen situations, disturbances, and changing goals. However, the learning progress is guided by the experiences in terms of situations the system has been exposed to so far – this reactive learning strategy naturally results in missing or inappropriate knowledge. In this paper, we define a formal system model and formulate an abstract learning task for SISSY systems. We further introduce the notion of knowledge and knowledge gaps to subsequently present a novel concept to automatically assess a system's existing knowledge base and, consequently, to proactively acquire knowledge to prepare SISSY/SASO systems for coping with disturbances and other changes that occur at runtime. By the proposed a priori construction of knowledge, we pursue the overall goal to increase the robustness as well as the learning efficiency of self-learning autonomous systems. Endowing these systems with the ability of identifying regions in their knowledge base that are not appropriately covered, strengthens their self-awareness property.
{"title":"A Concept for Proactive Knowledge Construction in Self-Learning Autonomous Systems","authors":"Anthony Stein, Sven Tomforde, A. Diaconescu, J. Hähner, C. Müller-Schloer","doi":"10.1109/FAS-W.2018.00048","DOIUrl":"https://doi.org/10.1109/FAS-W.2018.00048","url":null,"abstract":"The research initiative of self-improving and self-integrating systems (SISSY) emerged as response to the dramatically increasing complexity in information and communication technology. Such systems' ability of autonomous online learning has been identified as a key enabler for SISSY as well as for the broader field of self-adaptive and self-organizing (SASO) systems, since it provides the technical basis for dealing with the inherent dynamics of non-stationary environments that continually challenge these systems with unforeseen situations, disturbances, and changing goals. However, the learning progress is guided by the experiences in terms of situations the system has been exposed to so far – this reactive learning strategy naturally results in missing or inappropriate knowledge. In this paper, we define a formal system model and formulate an abstract learning task for SISSY systems. We further introduce the notion of knowledge and knowledge gaps to subsequently present a novel concept to automatically assess a system's existing knowledge base and, consequently, to proactively acquire knowledge to prepare SISSY/SASO systems for coping with disturbances and other changes that occur at runtime. By the proposed a priori construction of knowledge, we pursue the overall goal to increase the robustness as well as the learning efficiency of self-learning autonomous systems. Endowing these systems with the ability of identifying regions in their knowledge base that are not appropriately covered, strengthens their self-awareness property.","PeriodicalId":164903,"journal":{"name":"2018 IEEE 3rd International Workshops on Foundations and Applications of Self* Systems (FAS*W)","volume":"93 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":"131812629","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}
Pub Date : 2018-09-01DOI: 10.1109/FAS-W.2018.00031
Vero Estrada-Galiñanes, K. Wac
Health-related data analysis plays an important role in self-knowledge, disease prevention, diagnosis, and quality of life assessment. With the advent of data-driven solutions, a myriad of apps and Internet of Things (IoT) devices (wearables, home-medical sensors, etc) facilitates data collection and provide cloud storage with a central administration. More recently, blockchain and other distributed ledgers became available as alternative storage options based on decentralised organisation systems. We bring attention to the human data bleeding problem and argue that neither centralised nor decentralised system organisations are a magic bullet for data-driven innovation if individual, community and societal values are ignored. The motivation for this position paper is to elaborate on strategies to protect privacy as well as to encourage data sharing and support open data without requiring a complex access protocol for researchers. Our main contribution is to outline the design of a self-regulated Open Health Archive (OHA) system with focus on quality of life (QoL) data.
{"title":"Visions and Challenges in Managing and Preserving Data to Measure Quality of Life","authors":"Vero Estrada-Galiñanes, K. Wac","doi":"10.1109/FAS-W.2018.00031","DOIUrl":"https://doi.org/10.1109/FAS-W.2018.00031","url":null,"abstract":"Health-related data analysis plays an important role in self-knowledge, disease prevention, diagnosis, and quality of life assessment. With the advent of data-driven solutions, a myriad of apps and Internet of Things (IoT) devices (wearables, home-medical sensors, etc) facilitates data collection and provide cloud storage with a central administration. More recently, blockchain and other distributed ledgers became available as alternative storage options based on decentralised organisation systems. We bring attention to the human data bleeding problem and argue that neither centralised nor decentralised system organisations are a magic bullet for data-driven innovation if individual, community and societal values are ignored. The motivation for this position paper is to elaborate on strategies to protect privacy as well as to encourage data sharing and support open data without requiring a complex access protocol for researchers. Our main contribution is to outline the design of a self-regulated Open Health Archive (OHA) system with focus on quality of life (QoL) data.","PeriodicalId":164903,"journal":{"name":"2018 IEEE 3rd International Workshops on Foundations and Applications of Self* Systems (FAS*W)","volume":"15 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":"122286650","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}
Pub Date : 2018-09-01DOI: 10.1109/FAS-W.2018.00042
K. Bellman, J. Botev, A. Diaconescu, Lukas Esterle, Christian Gruhl, C. Landauer, Peter R. Lewis, Anthony Stein, Sven Tomforde, R. Würtz
The self-improving system integration (SISSY) initiative has emerged in recent years in response to a systems engineering trend towards the organisation of open, interconnected systems integrating a large set of heterogeneous and autonomous subsystems. Based on the idea to equip subsystems with capabilities to assess and maintain their own integration status within the overall system composition, a variety of concepts, techniques, and contributions have been proposed and fruitfully discussed at the particular events of the underlying workshop series. In this article, we summarise and categorise these research efforts and derive a roadmap towards full-scale SISSY systems.
{"title":"Self-Improving System Integration - Status and Challenges after Five Years of SISSY","authors":"K. Bellman, J. Botev, A. Diaconescu, Lukas Esterle, Christian Gruhl, C. Landauer, Peter R. Lewis, Anthony Stein, Sven Tomforde, R. Würtz","doi":"10.1109/FAS-W.2018.00042","DOIUrl":"https://doi.org/10.1109/FAS-W.2018.00042","url":null,"abstract":"The self-improving system integration (SISSY) initiative has emerged in recent years in response to a systems engineering trend towards the organisation of open, interconnected systems integrating a large set of heterogeneous and autonomous subsystems. Based on the idea to equip subsystems with capabilities to assess and maintain their own integration status within the overall system composition, a variety of concepts, techniques, and contributions have been proposed and fruitfully discussed at the particular events of the underlying workshop series. In this article, we summarise and categorise these research efforts and derive a roadmap towards full-scale SISSY systems.","PeriodicalId":164903,"journal":{"name":"2018 IEEE 3rd International Workshops on Foundations and Applications of Self* Systems (FAS*W)","volume":"1 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":"130713308","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}
Pub Date : 2018-09-01DOI: 10.1109/FAS-W.2018.00034
Yuanqiu Mo, J. Beal, S. Dasgupta
Leader election is one of the core coordination problems of distributed systems, and has been addressed in many different ways suitable for different classes of systems. It is unclear, however, whether existing methods will be effective for resilient device coordination in open, complex, networked distributed systems like smart cities, tactical networks, personal networks and the Internet of Things (IoT). Aggregate computing provides a layered approach to developing such systems, in which resilience is provided by a layer comprising a set of adaptive algorithms whose compositions have been shown to cover a large class of coordination activities. In this paper, we show how a feedback interconnection of these basis set algorithms can perform distributed leader election resilient to device topology and position changes. We also characterize a key design parameter that defines some important performance attributes: Too large a value impairs resilience to loss of existing leaders, while too small a value leads to multiple leaders. We characterize the smallest value of this parameter for which the only stationary points have single leaders, and demonstrate resilience of this algorithm through simulations.
{"title":"An Aggregate Computing Approach to Self-Stabilizing Leader Election","authors":"Yuanqiu Mo, J. Beal, S. Dasgupta","doi":"10.1109/FAS-W.2018.00034","DOIUrl":"https://doi.org/10.1109/FAS-W.2018.00034","url":null,"abstract":"Leader election is one of the core coordination problems of distributed systems, and has been addressed in many different ways suitable for different classes of systems. It is unclear, however, whether existing methods will be effective for resilient device coordination in open, complex, networked distributed systems like smart cities, tactical networks, personal networks and the Internet of Things (IoT). Aggregate computing provides a layered approach to developing such systems, in which resilience is provided by a layer comprising a set of adaptive algorithms whose compositions have been shown to cover a large class of coordination activities. In this paper, we show how a feedback interconnection of these basis set algorithms can perform distributed leader election resilient to device topology and position changes. We also characterize a key design parameter that defines some important performance attributes: Too large a value impairs resilience to loss of existing leaders, while too small a value leads to multiple leaders. We characterize the smallest value of this parameter for which the only stationary points have single leaders, and demonstrate resilience of this algorithm through simulations.","PeriodicalId":164903,"journal":{"name":"2018 IEEE 3rd International Workshops on Foundations and Applications of Self* Systems (FAS*W)","volume":"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":"131107943","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}
Pub Date : 2018-09-01DOI: 10.1109/FAS-W.2018.00022
Seontae Kim, Young-ri Choi
Virtualized systems consist of a large number of machines that are configured with different hardware and software, and execute a large number of virtual machines (VMs) for diverse applications. There can be various constraint conditions of placing VMs in such systems due to the concerns on security, availability, performance, etc. However, VM placement constraints can limit the choice of hosts for VMs, affecting the performance of the systems negatively. In this paper, we study constraint-aware VM placement in heterogeneous computing clusters. We first present a model of VM placement constraints that supports all types of constraints between VMs, and between VMs and hosts. Second, we discuss constraint-aware VM placement algorithms which optimize the performance for either energy saving or load balancing. Third, using simulations, we analyze the effects of different types of VM placement constraints on VM placement, and evaluate the performance of the algorithms over various settings. Our extensive simulation results demonstrate that the effects of VM placement constraints vary, depending on the optimization goal, the types of the constraints, and the system configurations.
{"title":"Effects of VM Placement Constraints in Heterogeneous Virtual Clusters","authors":"Seontae Kim, Young-ri Choi","doi":"10.1109/FAS-W.2018.00022","DOIUrl":"https://doi.org/10.1109/FAS-W.2018.00022","url":null,"abstract":"Virtualized systems consist of a large number of machines that are configured with different hardware and software, and execute a large number of virtual machines (VMs) for diverse applications. There can be various constraint conditions of placing VMs in such systems due to the concerns on security, availability, performance, etc. However, VM placement constraints can limit the choice of hosts for VMs, affecting the performance of the systems negatively. In this paper, we study constraint-aware VM placement in heterogeneous computing clusters. We first present a model of VM placement constraints that supports all types of constraints between VMs, and between VMs and hosts. Second, we discuss constraint-aware VM placement algorithms which optimize the performance for either energy saving or load balancing. Third, using simulations, we analyze the effects of different types of VM placement constraints on VM placement, and evaluate the performance of the algorithms over various settings. Our extensive simulation results demonstrate that the effects of VM placement constraints vary, depending on the optimization goal, the types of the constraints, and the system configurations.","PeriodicalId":164903,"journal":{"name":"2018 IEEE 3rd International Workshops on Foundations and Applications of Self* Systems (FAS*W)","volume":"8 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":"134131977","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}
Pub Date : 2018-09-01DOI: 10.1109/FAS-W.2018.00015
M. Burkhardt
Using concepts based on intelligent agents and microservices, several frameworks aim at making development of distributed and concurrent applications easier. Agent oriented programming frameworks are, in general, less popular than the ones dedicated to the development of microservices. Mastering the complexity of distributed and changing environments can be a painstaking challenge. Hence, a pragmatic approach is required to simplify the development of distributed applications. This paper identifies the challenges on this area for further research.
{"title":"Towards a Pragmatic Approach to Engineer Cognitive, Agent-Oriented, Distributed Applications","authors":"M. Burkhardt","doi":"10.1109/FAS-W.2018.00015","DOIUrl":"https://doi.org/10.1109/FAS-W.2018.00015","url":null,"abstract":"Using concepts based on intelligent agents and microservices, several frameworks aim at making development of distributed and concurrent applications easier. Agent oriented programming frameworks are, in general, less popular than the ones dedicated to the development of microservices. Mastering the complexity of distributed and changing environments can be a painstaking challenge. Hence, a pragmatic approach is required to simplify the development of distributed applications. This paper identifies the challenges on this area for further research.","PeriodicalId":164903,"journal":{"name":"2018 IEEE 3rd International Workshops on Foundations and Applications of Self* Systems (FAS*W)","volume":"94 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":"116787127","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}
Pub Date : 2018-09-01DOI: 10.1109/FAS-W.2018.00045
Christian Krupitzer, Martin Pfannemüller, Jean Kaddour, C. Becker
Self-adaptive systems can adapt their managed resources to reflect changes in their environment or the resources themselves. However, sometimes these systems cannot handle situations due to uncertainty. Self-improvement enables the adaptation of the decision logic of such systems for coping with new situations. Proactive analysis predicts the need for self-improvement as well as reduces the delay for self-adaptation. However, implementing proactive analysis is a complex task which requires developers to analyze different algorithms and parameter combinations for finding the best fitting setting for the given data. This paper addresses this issue by presenting a model for a self-learning analyzer for proactive reasoning based on time series forecasting which can support self-improvement at runtime. We present a prototype implementation of such an analyzer and evaluate its performance for traffic prediction in an adaptive traffic management system.
{"title":"SATISFy: Towards a Self-Learning Analyzer for Time Series Forecasting in Self-Improving Systems","authors":"Christian Krupitzer, Martin Pfannemüller, Jean Kaddour, C. Becker","doi":"10.1109/FAS-W.2018.00045","DOIUrl":"https://doi.org/10.1109/FAS-W.2018.00045","url":null,"abstract":"Self-adaptive systems can adapt their managed resources to reflect changes in their environment or the resources themselves. However, sometimes these systems cannot handle situations due to uncertainty. Self-improvement enables the adaptation of the decision logic of such systems for coping with new situations. Proactive analysis predicts the need for self-improvement as well as reduces the delay for self-adaptation. However, implementing proactive analysis is a complex task which requires developers to analyze different algorithms and parameter combinations for finding the best fitting setting for the given data. This paper addresses this issue by presenting a model for a self-learning analyzer for proactive reasoning based on time series forecasting which can support self-improvement at runtime. We present a prototype implementation of such an analyzer and evaluate its performance for traffic prediction in an adaptive traffic management system.","PeriodicalId":164903,"journal":{"name":"2018 IEEE 3rd International Workshops on Foundations and Applications of Self* Systems (FAS*W)","volume":"72 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":"126105804","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}
Pub Date : 2018-09-01DOI: 10.1109/FAS-W.2018.00037
K. K. Budhraja, T. Oates
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. Without the involvement of manual behavior specification via code or reliance on a defined taxonomy of possible behaviors, the demonstrator specifies spatial motion of the agents over time, and retrieves agent-level parameters required to execute that motion. A framework for reproducing emergent behavior, given an abstract demonstration, is discussed in existing work. Our work extends that framework by refining the data that is aggregated to produce the agent-level parameters that the framework provides to the demonstrator. This is done using pruning and outlier detection based on information that is intrinsic to those data points (their source). Using pruning and outlier detection shows potential to refine the aggregation data to a fraction of its size, while maintaining or potentially improving performance in replication of demonstrations.
{"title":"Improved Reverse Mapping for Controlling Swarms by Visual Demonstration","authors":"K. K. Budhraja, T. Oates","doi":"10.1109/FAS-W.2018.00037","DOIUrl":"https://doi.org/10.1109/FAS-W.2018.00037","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. Without the involvement of manual behavior specification via code or reliance on a defined taxonomy of possible behaviors, the demonstrator specifies spatial motion of the agents over time, and retrieves agent-level parameters required to execute that motion. A framework for reproducing emergent behavior, given an abstract demonstration, is discussed in existing work. Our work extends that framework by refining the data that is aggregated to produce the agent-level parameters that the framework provides to the demonstrator. This is done using pruning and outlier detection based on information that is intrinsic to those data points (their source). Using pruning and outlier detection shows potential to refine the aggregation data to a fraction of its size, while maintaining or potentially improving performance in replication of demonstrations.","PeriodicalId":164903,"journal":{"name":"2018 IEEE 3rd International Workshops on Foundations and Applications of Self* Systems (FAS*W)","volume":"36 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":"123309687","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}
Pub Date : 2018-09-01DOI: 10.1109/fas-w.2018.00006
Sebastian Götz, N. Herbst, N. Bencomo, K. Bellman, Peter R. Lewis, J. C. Moreno, Niklas Karlsson, Lukas Esterle, M. Autili, L. Seinturier, Ta'Id Holmes, A. Filieri, C. Landauer, Mahdi Derakhshanmanesh, M. Tichy, T. Vogel, B. Cheng, Samuel Kounev, H. Giese
{"title":"MRT-SeAC 2018 Foreword","authors":"Sebastian Götz, N. Herbst, N. Bencomo, K. Bellman, Peter R. Lewis, J. C. Moreno, Niklas Karlsson, Lukas Esterle, M. Autili, L. Seinturier, Ta'Id Holmes, A. Filieri, C. Landauer, Mahdi Derakhshanmanesh, M. Tichy, T. Vogel, B. Cheng, Samuel Kounev, H. Giese","doi":"10.1109/fas-w.2018.00006","DOIUrl":"https://doi.org/10.1109/fas-w.2018.00006","url":null,"abstract":"","PeriodicalId":164903,"journal":{"name":"2018 IEEE 3rd International Workshops on Foundations and Applications of Self* Systems (FAS*W)","volume":"16 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":"121349126","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}
Pub Date : 2018-09-01DOI: 10.1109/FAS-W.2018.00018
Abhishek Dixit, A. Norta
Distributed Ledgers, such as blockchains implement business collaboration processes in the form of smart contracts (SCs). Blockchain technology and smart contracts have received significant attention as they exhibit autonomy, decentralization, trust, and transparency over peer-to-peer networks while moving the assets digitally among peers without a third-party such as lawyers in conventional contracts (CCs). Smart contracts are computerized scripts or protocols that execute contractual clauses when certain pre-defined conditions meet and thereby digitally enforce the negotiation and performance of a contract. Smart contracts, although, being self-executable and self-enforceable, are irreversible once written and lack contractual flexibility in the face of a contingency. A high degree of automation is sought to manifest blockchain enabled smart contracts into the so-called self-aware contracts (SAC) that can be aware of its internal contextual environment and the external environment or real-world events. This Ph.D. work aims to develop the belief-desire-intention (BDI) model based multi-agent system on the top of the blockchain technology-enabled smart contracts to yield the so-called self-aware contracts. This research follows the guidelines of Design Science Research (DSR) methodology.
{"title":"A Self-Aware Contract for Decentralized Peer-to-Peer (P2P) Commerce","authors":"Abhishek Dixit, A. Norta","doi":"10.1109/FAS-W.2018.00018","DOIUrl":"https://doi.org/10.1109/FAS-W.2018.00018","url":null,"abstract":"Distributed Ledgers, such as blockchains implement business collaboration processes in the form of smart contracts (SCs). Blockchain technology and smart contracts have received significant attention as they exhibit autonomy, decentralization, trust, and transparency over peer-to-peer networks while moving the assets digitally among peers without a third-party such as lawyers in conventional contracts (CCs). Smart contracts are computerized scripts or protocols that execute contractual clauses when certain pre-defined conditions meet and thereby digitally enforce the negotiation and performance of a contract. Smart contracts, although, being self-executable and self-enforceable, are irreversible once written and lack contractual flexibility in the face of a contingency. A high degree of automation is sought to manifest blockchain enabled smart contracts into the so-called self-aware contracts (SAC) that can be aware of its internal contextual environment and the external environment or real-world events. This Ph.D. work aims to develop the belief-desire-intention (BDI) model based multi-agent system on the top of the blockchain technology-enabled smart contracts to yield the so-called self-aware contracts. This research follows the guidelines of Design Science Research (DSR) methodology.","PeriodicalId":164903,"journal":{"name":"2018 IEEE 3rd International Workshops on Foundations and Applications of Self* Systems (FAS*W)","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":"126975512","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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