A motivational-based learning model for mobile robots

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Autor(es): dc.contributorUniversidade Estadual de Campinas (UNICAMP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorArtificial Intelligence and Cognitive Architectures Hub (H.IAAC). Av. Albert Einstein-
Autor(es): dc.creatorBerto, Letícia-
Autor(es): dc.creatorCosta, Paula-
Autor(es): dc.creatorSimões, Alexandre-
Autor(es): dc.creatorGudwin, Ricardo-
Autor(es): dc.creatorColombini, Esther-
Data de aceite: dc.date.accessioned2025-08-21T18:38:44Z-
Data de disponibilização: dc.date.available2025-08-21T18:38:44Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-11-30-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.cogsys.2024.101278-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/298270-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/298270-
Descrição: dc.descriptionHumans have needs motivating their behavior according to intensity and context. However, we also create preferences associated with each action's perceived pleasure, which is susceptible to changes over time. This makes decision-making more complex, requiring learning to balance needs and preferences according to the context. To understand how this process works and enable the development of robots with a motivational-based learning model, we computationally model a motivation theory proposed by Hull. In this model, the agent (an abstraction of a mobile robot) is motivated to keep itself in a state of homeostasis. We introduced hedonic dimensions to explore the impact of preferences on decision-making and employed reinforcement learning to train our motivated-based agents. In our experiments, we deploy three agents with distinct energy decay rates, simulating different metabolic rates, within two diverse environments. We investigate the influence of these conditions on their strategies, movement patterns, and overall behavior. The findings reveal that agents excel at learning more effective strategies when the environment allows for choices that align with their metabolic requirements. Furthermore, we observe that incorporating pleasure as a component of the motivational mechanism affects behavior learning, particularly for agents with regular metabolisms depending on the environment. Our study also unveils that, when confronted with survival challenges, agents prioritize immediate needs over pleasure and equilibrium. These insights shed light on how robotic agents can adapt and make informed decisions in demanding scenarios, demonstrating the intricate interplay between motivation, pleasure, and environmental context in autonomous systems.-
Descrição: dc.descriptionMinistério da Ciência, Tecnologia e Inovação-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionInstitute of Computing University of Campinas. Av. Albert Einstein 1251 - Cidade Universitária-
Descrição: dc.descriptionSchool of Electrical and Computer Engineering University of Campinas. Av. Albert Einstein N° 400 - Cidade Universitária-
Descrição: dc.descriptionDept. of Control and Automation Engineering São Paulo State University. Av. Três de Março, 511 - Alto da Boa Vista-
Descrição: dc.descriptionArtificial Intelligence and Cognitive Architectures Hub (H.IAAC). Av. Albert Einstein, 1251 - Cidade Universitária-
Descrição: dc.descriptionDept. of Control and Automation Engineering São Paulo State University. Av. Três de Março, 511 - Alto da Boa Vista-
Descrição: dc.descriptionMinistério da Ciência, Tecnologia e Inovação: 01245.003479/2024 -10-
Descrição: dc.descriptionFAPESP: 2021/07050-0-
Descrição: dc.descriptionCNPq: 312323/2022-0-
Descrição: dc.descriptionCNPq: 315468/2021-1-
Idioma: dc.languageen-
Relação: dc.relationCognitive Systems Research-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectAction selection and planning-
Palavras-chave: dc.subjectInternal reinforces-
Palavras-chave: dc.subjectModels of internal states-
Palavras-chave: dc.subjectMotivation-
Título: dc.titleA motivational-based learning model for mobile robots-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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