Toward automatic generation of linguistic advice for saving energy at home

Conde-Clemente, Patricia; Alonso, José M.; Triviño, Gracián

doi:10.1007/s00500-016-2430-5

Cited by 36 publications

(24 citation statements)

References 42 publications

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“…In the literature, there is an increasing number of projects concerning the application of NLG for supporting people to adopt a virtuous behaviour, e.g., [17][18][19][20][21]. For instance, in the pioneering work [17], a tailored email was automatically generated and sent with the aim to help people with quitting smoke.…”

Section: Related Workmentioning

confidence: 99%

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Building a Persuasive Virtual Dietitian

Anselma

Mazzei

2020

Informatics

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This paper describes the Multimedia Application for Diet Management (MADiMan), a system that supports users in managing their diets while admitting diet transgressions. MADiMan consists of a numerical reasoner that takes into account users’ dietary constraints and automatically adapts the users’ diet, and of a natural language generation (NLG) system that automatically creates textual messages for explaining the results provided by the reasoner with the aim of persuading users to stick to a healthy diet. In the first part of the paper, we introduce the MADiMan system and, in particular, the basic mechanisms related to reasoning, data interpretation and content selection for a numeric data-to-text NLG system. We also discuss a number of factors influencing the design of the textual messages produced. In particular, we describe in detail the design of the sentence-aggregation procedure, which determines the compactness of the final message by applying two aggregation strategies. In the second part of the paper, we present the app that we developed, CheckYourMeal!, and the results of two human-based quantitative evaluations of the NLG module conducted using CheckYourMeal! in a simulation. The first evaluation, conducted with twenty users, ascertained both the perceived usefulness of graphics/text and the appeal, easiness and persuasiveness of the textual messages. The second evaluation, conducted with thirty-nine users, ascertained their persuasive power. The evaluations were based on the analysis of questionnaires and of logged data of users’ behaviour. Both evaluations showed significant results.

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Section: Related Workmentioning

confidence: 99%

“…Four recent data-to-text systems, similar to MADiMan in motivations and techniques, were described in [20,21,32,33].…”

Section: Related Workmentioning

confidence: 99%

Building a Persuasive Virtual Dietitian

Anselma

Mazzei

2020

Informatics

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“…Ramos-Soto et al [31] describe an approach to short weather forecast generation using a combination of fuzzy sets and NLG approaches. Conde-Clemente et al [32] also present an approach which combines soft computing with NLG in the domain of decision support for energy saving at home. Alonso et al [33] describe an approach to LDD in the context of medical support systems using fuzzy modelling.…”

Section: B Linguistic Description Of Data Using Soft Computingmentioning

confidence: 99%

Data-to-Text Generation Improves Decision-Making Under Uncertainty

Gkatzia

Lemon

Rieser

2017

IEEE Comput. Intell. Mag.

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Decision-making is often dependent on uncertain data, e.g. data associated with confidence scores or probabilities. This article presents a comparison of different information presentations for uncertain data and, for the first time, measures their effects on human decision-making, in the domain of weather forecast generation. We use a game-based setup to evaluate the different systems. We show that the use of Natural Language Generation (NLG) enhances decision-making under uncertainty, compared to state-of-the-art graphical-based representation methods. In a task-based study with 442 adults, we found that presentations using NLG led to 24% better decision-making on average than the graphical presentations, and to 44% better decision-making when NLG is combined with graphics. We also show that women achieve significantly better results when presented with NLG output (an 87% increase on average compared to graphical presentations). Finally, we present a further analysis of demographic data and its impact on decision-making, and we discuss implications for future NLG systems.

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“…Once the model has been set, the focus is now to establish linguistic descriptions that sum up a characterization of the daily behavior of each building and the error made by the model of each meter defined with phrases of the type: the activity consumption of a building is large or the model underestimates the expected consumption, respectively. For instance, the authors in [4] presents the adaptation of methodologies to generate customized linguistic descriptions of data.…”

Section: Introductionmentioning

confidence: 99%

Linguistic Modeling and Synthesis of Heterogeneous Energy Consumption Time Series Sets

Martínez-Municio¹,

Rodríguez-Benítez²,

Castillo-Herrera³

et al. 2018

IJCIS

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Thanks to the presence of sensors and the boom in technologies typical of the Internet of things, we can now monitor and record the energy consumption of buildings over time. By effectively analyzing these data to capture consumption patterns, significant reductions in consumption can be achieved and this can contribute to a building's sustainability. In this work, we propose a framework from which we can define models that capture this casuistry, gathering a set of time series of electrical consumption. The objective of these models is to obtain a linguistic summary based on y is P protoforms that describes in natural language the consumption of a given building or group of buildings in a specific time period. The definition of these descriptions has been solved by means of fuzzy linguistic summaries. As a novelty in this field, we propose an extension that is able to capture situations where the membership of the fuzzy sets is not very marked, which obtains an enriched semantics. In addition, to support these models, the development of a software prototype has been carried out and a small applied study of actual consumption data from an educational organization based on the conclusions that can be drawn from the techniques that we have described, demonstrating its capabilities in summarizing consumption situations. Finally, it is intended that this work will be useful to managers of buildings or organizational managers because it will enable them to better understand consumptionin a brief and concise manner, allowing them to save costs derived from energy supply by establishing sustainable policies.

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Toward automatic generation of linguistic advice for saving energy at home

Cited by 36 publications

References 42 publications

Building a Persuasive Virtual Dietitian

Building a Persuasive Virtual Dietitian

Data-to-Text Generation Improves Decision-Making Under Uncertainty

Linguistic Modeling and Synthesis of Heterogeneous Energy Consumption Time Series Sets

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