A Multi-Agent Architecture for Quantified Fruits: Design and Experience

Briot, Jean-Pierre; Nascimento, Nathalia; Lucena, Carlos José Pereira de

doi:10.18293/seke2016-102

Cited by 7 publications

(5 citation statements)

References 11 publications

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“…It is possible to extend our approach to test these kinds of applications. For example, Briot et al [25] describe a multiagent architecture to monitor fruit storage and offer predictions about shelf life. Analogously, this application has a global goal of achieving an specific target accuracy.…”

Section: Testing Prediction and Self-adaptive Applicationsmentioning

confidence: 99%

Testing Self-Organizing Multiagent Systems

Nascimento,

Lucena,

Alencar

et al. 2019

Preprint

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Multiagent Systems (MASs) involve different characteristics, such as autonomy, asynchronous and social features, which make these systems more difficult to understand. Thus, there is a lack of procedures guaranteeing that multiagent systems would behave as desired. Further complicating the situation is the fact that current agent-based approaches may also involve non-deterministic characteristics, such as learning, self-adaptation and self-organization (SASO). Nonetheless, there is a gap in the literature regarding the testing of systems with these features. This paper presents a publish-subscribe-based approach to develop test applications that facilitate the process of failure diagnosis in a self-organizing MAS. These tests are able to detect failures at the global behavior of the system or at the local properties of its parts. To illustrate the use of this approach, we developed a self-organizing MAS system based on the context of the Internet of Things (IoT), which simulates a set of smart street lights, and we performed functional ad-hoc tests. The street lights need to interact with each other in order to achieve the global goals of reducing the energy consumption and maintaining the maximum visual comfort in illuminated areas.To achieve these global behaviors, the street lights develop local behaviors automatically through a self-organizing process based on machine learning algorithms.

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Section: Testing Prediction and Self-adaptive Applicationsmentioning

confidence: 99%

Testing Self-Organizing Multiagent Systems

Nascimento,

Lucena,

Alencar

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…In particular, the Internet of Things has considerably increased the number of approaches that propose the use of machine learning to automate software development [93]- [96], [101], [115]. None of this research contains a comparison of their results to experiments designed by IoT experts.…”

Section: Motivationmentioning

confidence: 99%

Software Engineers vs. Machine Learning Algorithms: An Empirical Study Assessing Performance and Reuse Tasks

Nascimento¹,

Lucena²,

Alencar³

et al. 2018

Preprint

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Several papers have recently contained reports on applying machine learning (ML) to the automation of software engineering (SE) tasks, such as project management, modeling and development. However, there appear to be no approaches comparing how software engineers fare against machine-learning algorithms as applied to specific software development tasks. Such a comparison is essential to gain insight into which tasks are better performed by humans and which by machine learning and how cooperative work or human-in-the-loop processes can be implemented more effectively. In this paper, we present an empirical study that compares how software engineers and machine-learning algorithms perform and reuse tasks. The empirical study involves the synthesis of the control structure of an autonomous streetlight application. Our approach consists of four steps. First, we solved the problem using machine learning to determine specific performance and reuse tasks. Second, we asked software engineers with different domain knowledge levels to provide a solution to the same tasks. Third, we compared how software engineers fare against machine-learning algorithms when accomplishing the performance and reuse tasks based on criteria such as energy consumption and safety. Finally, we analyzed the results to understand which tasks are better performed by either humans or algorithms so that they can work together more effectively. Such an understanding and the resulting human-in-the-loop approaches, which take into account the strengths and weaknesses of humans and machine-learning algorithms, are fundamental not only to provide a basis for cooperative work in support of software engineering, but also, in other areas.

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“…Integrating with our previous works for the dual-loop control model [11], the multi-agent software architecture provides support for accompanying model and observation modes through agent communication mechanisms. However, in this MASbased architecture, we don't consider the non-deterministic characteristic of MAS such as learning [12], self-adaptation and self-organization [13]. The description of the role that each agent plays in this multi-agent model is as follows:…”

Section: A Mas-based Software Architecturementioning

confidence: 99%

Accompanying Observation Modes and Software Architecture for Autonomous Robot Software

Liu

Mao²,

Yang³

2018

International Conferences on Software Engineering and Knowledge Engineering

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To support robust task execution in open environment, autonomous robots (AR) should include reactive capabilities to cope with the dynamics and uncertainties from real-world environment. The uncertainties pose great challenges for robots being sensitive to the environmental changes and flexible to adjust self-behaviors. To this end, this paper aims to improve the sensing and acting capabilities of autonomous robots by novel behavioral theories, observation modes and software architectures. Specifically, this paper has three main contribution: (1) presents an accompanying model that specifies a novel accompanying pattern for interacting robot behaviors;(2) proposes four types of accompanying observation modes that coordinate multiple robot sensing behaviors; (3) proposes a concrete multi-agent software architecture that implements aforementioned accompanying model and accompanying observation modes. To demonstrate the applicability and validity of our accompanying modes and MAS-based software architecture, this paper conducts a case study to implement a domestic service example, which requires the robot to run in a highly dynamic environment and can adapt its behaviors to unexpected situations. *

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A Multi-Agent Architecture for Quantified Fruits: Design and Experience

Cited by 7 publications

References 11 publications

Testing Self-Organizing Multiagent Systems

Testing Self-Organizing Multiagent Systems

Software Engineers vs. Machine Learning Algorithms: An Empirical Study Assessing Performance and Reuse Tasks

Accompanying Observation Modes and Software Architecture for Autonomous Robot Software

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