The role of intrinsic motivations in attention allocation and shifting

Nocera, Dario Di; Finzi, Alberto; Rossi, Silvia; Staffa, Mariacarla

doi:10.3389/fpsyg.2014.00273

Cited by 20 publications

(9 citation statements)

References 38 publications

(77 reference statements)

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“…Comparison with extrinsic reward Cameron and Pierce, 1994;Barto et al, 2004;Di Nocera et al, 2014;Hester and Stone, 2015 Goal accomplishment Kulkarni et al, 2016 Comparison with random and least tried states Frank et al, 2015 Comparison with greedy approach Sequeira et al, 2011 Comparing single and multiple intrinsic rewards Sequeira et al, 2011 Analyzing performance over time Gatsoulis and Mcginnity, 2015 topping the list. This concurs with the very nature and definition of motivated behavior.…”

Section: Evaluation Methods Referencesmentioning

confidence: 99%

“…Reinforcement Learning Barto et al, 2004;Simşek and Barto, 2006;Schembri et al, 2007;Sequeira et al, 2011;Kompella et al, 2012;Baldassarre and Mirolli, 2013;Metzen and Kirchner, 2013;Di Nocera et al, 2014;Frank et al, 2015;Hester and Stone, 2015 Deep Learning Mohamed and Rezende, 2015;Kulkarni et al, 2016;Achiam and Sastry, 2017;Zhelo et al, 2018 Hierarchical Structure Schembri et al, 2007;Baranes and Oudeyer, 2010;Baldassarre and Mirolli, 2013;Santucci et al, 2013;Frank et al, 2015;Kulkarni et al, 2016 Active Learning Baranes and Oudeyer, 2009, 2010Kompella et al, 2017;Pathak et al, 2017 Motion Planning Frank et al, 2015 Affordance Discovery Hart et al, 2008;Hart, 2009 Goal Discovery/Goal Generation In Reinforcement Learning (RL), an agent learns from experience as it deals with a sequential decision problem. The agent interacts with an "environment" which contains a "critic" that provides the agent with rewards by evaluating the behavior.…”

Section: Settings Referencesmentioning

confidence: 99%

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Toward Computational Motivation for Multi-Agent Systems and Swarms

2018

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Motivation is a crucial part of animal and human mental development, fostering competence, autonomy, and open-ended development. Motivational constructs have proved to be an integral part of explaining human and animal behavior. Computer scientists have proposed various computational models of motivation for artificial agents, with the aim of building artificial agents capable of autonomous goal generation. Multi-agent systems and swarm intelligence are natural extensions to the individual agent setting. However, there are only a few works that focus on motivation theories in multi-agent or swarm settings. In this study, we review current computational models of motivation settings, mechanisms, functions and evaluation methods and discuss how we can produce systems with new kinds of functions not possible using individual agents. We describe in detail this open area of research and the major research challenges it holds.

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Section: Evaluation Methods Referencesmentioning

confidence: 99%

Section: Settings Referencesmentioning

confidence: 99%

Toward Computational Motivation for Multi-Agent Systems and Swarms

2018

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“…In the classical psychological and educational literature on motivation, most authors distinguish between two classes of motivations from an external observer's point of view. On the one hand, when the agent is perceived as trying "to obtain some separable outcome" [37], it is said to be extrinsically motivated [38]. The observer sees the agent seeking goal states where some observable explicit utility can be obtained.…”

Section: Operational and Cognitive Drivesmentioning

confidence: 99%

Motivation as a tool for designing lifelong learning robots

Romero¹,

Bellas²,

Becerra³

et al. 2020

ICA

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Designing robots has usually implied knowing beforehand the tasks to be carried out and in what domains. However, in the case of fully autonomous robots this is not possible. Autonomous robots need to operate in an open-ended manner, that is, deciding on the most interesting goals to achieve in domains that are not known at design time. This obviously poses a challenge from the point of view of designing the robot control structure. In particular, the main question that arises is how to endow the robot with a designer defined purpose and with means to translate that purpose into operational decisions without any knowledge of what situations the robot will find itself in. In this paper, we provide a formalization of motivation from an engineering perspective that allows for the structured design of purposeful robots. This formalization is based on a definition of the concepts of robot needs and drives, which are related through experience to the appropriate goals in specific domains. To illustrate the process, a motivational system to guide the operation of a real robot is constructed using this approach. A series of experiments carried out over it are discussed providing some insights on the design of purposeful motivated operation.

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“…What is the role of attention and priming behaviors in curiosity-driven exploration? Few studies tackle these issues, such as Forestier and Oudeyer (2015) and di Nocera et al (2014). However, these and similar questions must be better addressed, in order to allow these strategies to be adopted in the implementation of more complex learning mechanisms.…”

Section: Exploration As a Drive For Motor And Cognitive Developmentmentioning

confidence: 99%

Exploration Behaviors, Body Representations, and Simulation Processes for the Development of Cognition in Artificial Agents

2016

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Sensorimotor control and learning are fundamental prerequisites for cognitive development in humans and animals. Evidence from behavioral sciences and neuroscience suggests that motor and brain development are strongly intertwined with the experiential process of exploration, where internal body representations are formed and maintained over time. In order to guide our movements, our brain must hold an internal model of our body and constantly monitor its configuration state. How can sensorimotor control enable the development of more complex cognitive and motor capabilities? Although a clear answer has still not been found for this question, several studies suggest that processes of mental simulation of action-perception loops are likely to be executed in our brain and are dependent on internal body representations. Therefore, the capability to re-enact sensorimotor experience might represent a key mechanism behind the implementation of higher cognitive capabilities, such as behavior recognition, arbitration and imitation, sense of agency, and self-other distinction. This work is mainly addressed to researchers in autonomous motor and mental development for artificial agents. In particular, it aims at gathering the latest developments in the studies on exploration behaviors, internal body representations, and processes of sensorimotor simulations. Relevant studies in human and animal sciences are discussed and a parallel to similar investigations in robotics is presented.

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The role of intrinsic motivations in attention allocation and shifting

Cited by 20 publications

References 38 publications

Toward Computational Motivation for Multi-Agent Systems and Swarms

Toward Computational Motivation for Multi-Agent Systems and Swarms

Motivation as a tool for designing lifelong learning robots

Exploration Behaviors, Body Representations, and Simulation Processes for the Development of Cognition in Artificial Agents

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