Coordinating mobile activities by integrating simulated and physical software agents

Lin, Shyh-ming; Lin, Fu‐ren

doi:10.1016/j.ins.2016.01.085

Cited by 2 publications

(2 citation statements)

References 31 publications

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“…In this problem, users schedule and reschedule their tasks through mobile devices when they change their goals. In particular, Lin and Lin [13] formulated this problem as a multiobjective context-dependent distributed constraint optimization problem. In particular, they proposed a two-stage distributed optimality reaching approach and implemented it with a multiagent system (MAS) that integrated simulated and physical agents.…”

Section: Related Workmentioning

confidence: 99%

Agent-Based Simulators for Empowering Patients in Self-Care Programs Using Mobile Agents with Machine Learning

Kumar

García‐Magariño

Nasralla

et al. 2021

Mobile Information Systems

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E-health sustainable systems can be optimized by empowering patients in self-care programs through artificial intelligence ecosystems in which both doctors and patients interact in an agile way. This work proposes agent-based simulators as a mechanism for predicting the repercussions of certain self-care programs in certain patients for finding the most appropriate ones. In order to make this easy for both doctors and patients, mobile agents are used to configure an app for each patient, and this app provides the resources to each self-care program. Mobile agents include a machine-learning module for learning which programs are the most appropriate for each patient. This approach is illustrated with two agent-based simulators for respectively reducing negative emotions such as depression and controlling heart rate variability extreme values related to stress. The resulting app was evaluated with a group of users with the Usefulness, Satisfaction and Ease of use (USE) scale and obtained 73% in usefulness, 77% in satisfaction, and 68% in ease of use. This trial is registered with According to the recommendations of the International Committee of Medical Journal Editors (ICMJE), this manuscript states that all experiments have been approved with the ethical committee CEICA from Community of Aragon (Spain) with registration number C.I.PI18/099.

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Section: Related Workmentioning

confidence: 99%

Agent-Based Simulators for Empowering Patients in Self-Care Programs Using Mobile Agents with Machine Learning

Kumar

García‐Magariño

Nasralla

et al. 2021

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…Moreover, Lin and Lin [20] developed a MAS that coordinates several users through an app. User can change their goals, by scheduling or re-scheduling their tasks.…”

Section: Techniques For Developing Mass For Mobile Devicesmentioning

confidence: 99%

TABSAOND: A technique for developing agent-based simulation apps and online tools with nondeterministic decisions

García‐Magariño

Palacios-Navarro

Lacuesta

2017

Simulation Modelling Practice and Theory

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Agent-based simulators (ABSs) have successfully allowed practitioners to estimate the outcomes of certain input circumstances in several domains. Although some techniques and processes provide hints about the construction of these systems, there are still some aspects that are not discussed in the literature. In this context, the current approach presents a technique for developing ABSs. This technique especially discusses the simulation of the decision-making processes of agents and the different ways of evolving their attributes in nondeterministic scenarios. The current approach includes a framework for developing ABSs as mobile apps and online tools. This work illustrates the current approach with a case study in the field of health and welfare. This case study has also been implemented with the most similar technique from the literature for comparing both techniques. The presented technique improved the simulated outcomes in terms of similarity with the real ones, as proved with several statistical tests. The obtained ABS is more efficient and reliable for large amounts of agents (e.g. 10,000-400,000 agents). The development time was also lower. Both the framework and the implementation of the case study are freely distributed as open-source to ensure the reproducibility of the experiments and to assist practitioners in applying the current approach.

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Coordinating mobile activities by integrating simulated and physical software agents

Cited by 2 publications

References 31 publications

Agent-Based Simulators for Empowering Patients in Self-Care Programs Using Mobile Agents with Machine Learning

Agent-Based Simulators for Empowering Patients in Self-Care Programs Using Mobile Agents with Machine Learning

TABSAOND: A technique for developing agent-based simulation apps and online tools with nondeterministic decisions

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