Contributions to the emergence and consolidation of Agent-oriented Software Engineering

Lucena, Carlos José Pereira de; Nunes, Ingrid

doi:10.1016/j.jss.2012.09.016

Cited by 5 publications

(5 citation statements)

References 55 publications

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“…Agents are software components with autonomy, reactiveness, proactiveness and social capabilities [3]. Agent autonomy comes in handy when a system needs to make its own decisions.…”

Section: Doi Reference Number: 1018293/seke2018-074mentioning

confidence: 99%

An Agent-based Software Framework for Machine Learning Tuning

Sastre¹,

Viana²,

Lucena³

2018

International Conferences on Software Engineering and Knowledge Engineering

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Nowadays, the challenge of knowledge discovery is to mine massive amounts of data available online. The most widely used approaches to tackle that challenge are based on machine learning techniques. In spite of being very powerful, those techniques require their parameters to be calibrated in order to generate models with better quality. Such calibration processes are time-consuming and rely on the skills of machine learning experts. Within this context, this research presents a framework based on software agents for automating the calibration of machine learning models. This approach integrates concepts from Agent Oriented Software Engineering (AOSE) and Machine Learning (ML). As a proof of concept, we first train a model for the IRIS dataset and then we show how our approach improves the quality of new models generated by our framework. Keywords. Agent oriented Software Engineering (AOSE); Machine Learning; Time-consuming abstractions with the following properties [6]: (i) autonomyit is the capability of taking their own actions within their environment; (ii) reactivityit is the capability of response to the changes in the environment, which involves a notion of perception of the environment; (iii) social abilityit is the capability of interaction with other agents and possibly humans, and (iv) proactive abilityit is the capability to take actions towards the agent's goals.

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“…Agents are software components with autonomy, reactiveness, proactiveness and social capabilities [3]. Agent autonomy comes in handy when a system needs to make its own decisions.…”

Section: Doi Reference Number: 1018293/seke2018-074mentioning

confidence: 99%

An Agent-based Software Framework for Machine Learning Tuning

Sastre¹,

Viana²,

Lucena³

2018

International Conferences on Software Engineering and Knowledge Engineering

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“…Dabhi et al [4] identified the need for intelligencebased automatic workflow generation and the prediction of run-time of tasks for optimal allocation and reservation of resources in grid computing environment. MAS has certain special characteristics that makes them autonomous (agents have the ability to operate without the direct intervention of humans or others, and they have some kind of control over their actions and internal state) and be pro-active (agents do not simply act in response to their environment, they are able to exhibit goal-directed behaviour by taking the initiative) [15]. Their work is similar to the one proposed in this paper, as it does not also require human intervention in workflow generation and in finding and allocating optimal resource for task execution.…”

Section: Related Workmentioning

confidence: 99%

“…However, the system does not employ the intelligence, agility, and reasoning ability of MAS. MAS has certain special characteristics that makes them autonomous (agents have the ability to operate without the direct intervention of humans or others, and they have some kind of control over their actions and internal state) and be pro‐active (agents do not simply act in response to their environment, they are able to exhibit goal‐directed behaviour by taking the initiative) . We argue that because of these characteristics, agents are a natural metaphor to understand complex and distributed systems .…”

Section: Related Workmentioning

confidence: 99%

Neural network‐based multi‐agent approach for scheduling in distributed systems

Ezugwu

Frîncu

Adediran

et al. 2016

Concurrency and Computation

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A distributed system consists of a collection of autonomous heterogeneous resources that provide resource sharing and a common platform for running parallel compute-intensive applications. The different application characteristics combined with the heterogeneity and performance variations of the distributed system make it difficult to find the optimal set of needed resources. When deployed, user applications are usually handled by application domain experts or system administrators who depending on the infrastructure provide a scheduling strategy for selecting the best candidate resource over a set of available resources. However, the provided strategy is usually generic, aimed at handling a wide array of applications and does not take into consideration specific application resource requirements. As such, an intelligent method for selecting the best resources based on expert knowledge is needed. In this paper, we propose a neural network-based multi-agent resource selection technique capable of mimicking the services of an expert user. In addition, to cope with the geographical distribution of the underlying system, we employ a multi-agent coordination mechanism. The proposed neural network-based scheduling framework combined with the multi-agent intelligence is a unique approach to efficiently deal with the resource selection problem. Results run on a simulated environment show the efficiency of our proposed method. Several scheduling simulations were conducted to compare the performance of some conventional resource selection methods against the proposed agent-based neural network technique. The results obtained indicate that the agent-based approach outperformed the classical algorithms by reducing the amount of time required to search for suitable resources irrespective of the resource size.Our system relies on both ANN and MPGP to train and coordinate team of agents on how to discover and select best candidate resource that matches users' application resource needs.

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“…Many of the issues addressed with multiagent ways, for instance, distributed coordination and self-organization, are now becoming part of industrial and business systems. However, MASs are still not widely adopted in the industry owing to the lack of a connection between MAS and software engineering [17].…”

Section: Introductionmentioning

confidence: 99%

Agent‐Oriented Software Engineering Methodologies: Analysis and Future Directions

Abdalla

Mishra

2021

Complexity

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The Internet of Things (IoT) facilitates in building cyber-physical systems, which are significant for Industry 4.0. Agent-based computing represents effective modeling, programming, and simulation paradigm to develop IoT systems. Agent concepts, techniques, methods, and tools are being used in evolving IoT systems. Over the last years, in particular, there has been an increasing number of agent approaches proposed along with an ever-growing interest in their various implementations. Yet a comprehensive and full-fledged agent approach for developing related projects is still lacking despite the presence of agent-oriented software engineering (AOSE) methodologies. One of the moves towards compensating for this issue is to compile various available methodologies, ones that are comparable to the evolution of the unified modeling language (UML) in the domain of object-oriented analysis and design. These have become de facto standards in software development. In line with this objective, the present research attempts to comprehend the relationship among seven main AOSE methodologies. More specifically, we intend to assess and compare these seven approaches by conducting a feature analysis through examining the advantages and limitations of each competing process, structural analysis, and a case study evaluation method. This effort is made to address the significant characteristics of AOSE approaches. The main objective of this study is to conduct a comprehensive analysis of selected AOSE methodologies and provide a proposal of a draft unified approach that drives strengths (best) of these methodologies towards advancement in this area.

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Contributions to the emergence and consolidation of Agent-oriented Software Engineering

Cited by 5 publications

References 55 publications

An Agent-based Software Framework for Machine Learning Tuning

An Agent-based Software Framework for Machine Learning Tuning

Neural network‐based multi‐agent approach for scheduling in distributed systems

Agent‐Oriented Software Engineering Methodologies: Analysis and Future Directions

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