Dataset Information

Automated curriculum learning for embodied agents a neuroevolutionary approach.

ABSTRACT: We demonstrate how the evolutionary training of embodied agents can be extended with a curriculum learning algorithm that automatically selects the environmental conditions in which the evolving agents are evaluated. The environmental conditions are selected to adjust the level of difficulty to the ability level of the current evolving agents, and to challenge the weaknesses of the evolving agents. The method does not require domain knowledge and does not introduce additional hyperparameters. The results collected on two benchmark problems, that require to solve a task in significantly varying environmental conditions, demonstrate that the method proposed outperforms conventional learning methods and generates solutions which are robust to variations and able to cope with different environmental conditions.

SUBMITTER: Milano N

PROVIDER: S-EPMC8076209 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Automated curriculum learning for embodied agents a neuroevolutionary approach.

Milano Nicola N Nolfi Stefano S

Scientific reports 20210426 1

We demonstrate how the evolutionary training of embodied agents can be extended with a curriculum learning algorithm that automatically selects the environmental conditions in which the evolving agents are evaluated. The environmental conditions are selected to adjust the level of difficulty to the ability level of the current evolving agents, and to challenge the weaknesses of the evolving agents. The method does not require domain knowledge and does not introduce additional hyperparameters. Th ...[more]

PMID: 33903698

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Project description:BackgroundEmbodied conversational agents (ECAs) are computer-generated animated humanlike characters that interact with users through verbal and nonverbal behavioral cues. They are increasingly used in a range of fields, including health care.ObjectiveThis scoping review aims to identify the current practice in the development and evaluation of ECAs for chronic diseases.MethodsWe applied a methodological framework in this review. A total of 6 databases (ie, PubMed, Embase, CINAHL, ACM Digital Library, IEEE Xplore Digital Library, and Web of Science) were searched using a combination of terms related to ECAs and health in October 2023. Two independent reviewers selected the studies and extracted the data. This review followed the PRISMA-ScR (Preferred Reporting Items of Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) statement.ResultsThe literature search found 6332 papers, of which 36 (0.57%) met the inclusion criteria. Among the 36 studies, 27 (75%) originated from the United States, and 28 (78%) were published from 2020 onward. The reported ECAs covered a wide range of chronic diseases, with a focus on cancers, atrial fibrillation, and type 2 diabetes, primarily to promote screening and self-management. Most ECAs were depicted as middle-aged women based on screenshots and communicated with users through voice and nonverbal behavior. The most frequently reported evaluation outcomes were acceptability and effectiveness.ConclusionsThis scoping review provides valuable insights for technology developers and health care professionals regarding the development and implementation of ECAs. It emphasizes the importance of technological advances in the embodiment, personalized strategy, and communication modality and requires in-depth knowledge of user preferences regarding appearance, animation, and intervention content. Future studies should incorporate measures of cost, efficiency, and productivity to provide a comprehensive evaluation of the benefits of using ECAs in health care.

Dataset Information

Automated curriculum learning for embodied agents a neuroevolutionary approach.

Publications

Automated curriculum learning for embodied agents a neuroevolutionary approach.

Similar Datasets

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