An Iowa Gambling Task-based experiment applied to robots: A Study on Long-term Decision Making

• Autor(a) principal: Berto, Leticia M.
• Data de Publicação: 2021
• Outros Autores: Costa, Paula D. P., Simoes, Alexandre S. [UNESP], Gudwin, Ricardo R., Colombini, Esther L.
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1109/ICDL49984.2021.9515632; http://hdl.handle.net/11449/222377
• topic: Action selection and planning; Exploration and Play; Intrinsic Motivation; Models of emotions and internal states

Designing a robot's decision-making process is challenging because it is still not completely understood even in humans. However, it is a fundamental process in the search for autonomous agents. When making decisions, we consider the short and long-term consequences of our actions, but some impairments prevent some people from seeing in the long run. Using as an inspiration an experiment carried out with humans in which decision-making is evaluated under the uncertainty of premises and results, rewards, and punishments, we created an equivalent robotics experiment. To model our agent's state, we use a set of drives. Our agent's goal is to reduce the distance between its homeostasis state and its needs. We trained a simulated robot with reinforcement learning, showing that long-term assessment agents can survive longer while satisfying other needs.