title: The role of intrinsic motivations in attention allocation and shifting.
abstract: The concepts of attention and intrinsic motivations are of great interest within adaptive robotic systems, and can be exploited in order to guide, activate, and coordinate multiple concurrent behaviors. Attention allocation strategies represent key capabilities of human beings, which are strictly connected with action selection and execution mechanisms, while intrinsic motivations directly affect the allocation of attentional resources. In this paper we propose a model of Reinforcement Learning (RL), where both these capabilities are involved. RL is deployed to learn how to allocate attentional resources in a behavior-based robotic system, while action selection is obtained as a side effect of the resulting motivated attentional behaviors. Moreover, the influence of intrinsic motivations in attention orientation is obtained by introducing rewards associated with curiosity drives. In this way, the learning process is affected not only by goal-specific rewards, but also by intrinsic motivations.
authors.affiliation: Dipartimento di Matematica e Applicazioni, Università degli Studi di Napoli "Federico II," Napoli, Italy.
authors.forename: Dario
authors.initials: D
authors.lastname: Di Nocera
authors.affiliation: Dipartimento di Ingegneria Elettrica e Tecnologie dell'Informazione, Università degli Studi di Napoli "Federico II," Napoli, Italy.
authors.forename: Alberto
authors.initials: A
authors.lastname: Finzi
authors.affiliation: Dipartimento di Ingegneria Elettrica e Tecnologie dell'Informazione, Università degli Studi di Napoli "Federico II," Napoli, Italy.
authors.forename: Silvia
authors.initials: S
authors.lastname: Rossi
authors.affiliation: Dipartimento di Ingegneria Elettrica e Tecnologie dell'Informazione, Università degli Studi di Napoli "Federico II," Napoli, Italy.
authors.forename: Mariacarla
authors.initials: M
authors.lastname: Staffa
keywords: action selection; attention shifting; curiosity; intrinsic motivations; reinforcement learning
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