Simulation-Based Internal Models for Safer Robots

Blum, Christian; Winfield, Alan F. T.; Hafner, Verena V.

doi:10.3389/frobt.2017.00074

Cited by 35 publications

(28 citation statements)

References 46 publications

(54 reference statements)

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“…Understanding what others think, and intend, is important for any collaborative effort. Blum et al (2018) describe work on similar functionality in robots. In an interview, Winfield, as one of the authors, describes how such a theory of mind might enable robots to understand each other, which takes them "one stop closer to understanding us" (Baraniuk 2018).…”

Section: The Call Of the Voidmentioning

confidence: 99%

The Parasitic Nature of Social AI: Sharing Minds with the Mindless

Sætra

2020

Integr. psych. behav.

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Can artificial intelligence (AI) develop the potential to be our partner, and will we be as sensitive to its social signals as we are to those of human beings? I examine both of these questions and how cultural psychology might add such questions to its research agenda. There are three areas in which I believe there is a need for both a better understanding and added perspective. First, I will present some important concepts and ideas from the world of AI that might be beneficial for pursuing research topics focused on AI within the cultural psychology research agenda. Second, there are some very interesting questions that must be answered with respect to central notions in cultural psychology as these are tested through human interactions with AI. Third, I claim that social robots are parasitic to deeply ingrained human social behaviour, in the sense that they exploit and feed upon processes and mechanisms that evolved for purposes that were originally completely alien to human-computer interactions.

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Section: The Call Of the Voidmentioning

confidence: 99%

The Parasitic Nature of Social AI: Sharing Minds with the Mindless

Sætra

2020

Integr. psych. behav.

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“…"Theory of mind is the term given by philosophers and psychologists for the ability to form a predictive model of self and others" Winfield (2018). These internal simulations show how to increase robot safety (Blum et al, 2018) by anticipating self and other behavior (Winfield and Hafner, 2018).…”

Section: Sensorimotor Simulations and Predictive Processesmentioning

confidence: 99%

Prerequisites for an Artificial Self

et al. 2020

Self Cite

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Traditionally investigated in philosophy, body ownership and agency-two main components of the minimal self-have recently gained attention from other disciplines, such as brain, cognitive and behavioral sciences, and even robotics and artificial intelligence. In robotics, intuitive human interaction in natural and dynamic environments becomes more and more important, and requires skills such as self-other distinction and an understanding of agency effects. In a previous review article, we investigated studies on mechanisms for the development of motor and cognitive skills in robots (Schillaci et al., 2016). In this review article, we argue that these mechanisms also build the foundation for an understanding of an artificial self. In particular, we look at developmental processes of the minimal self in biological systems, transfer principles of those to the development of an artificial self, and suggest metrics for agency and body ownership in an artificial self.

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“…Inverse Reinforcement Learning, for one, teaches algorithms to adapt behaviour to circumstances and learn from human–machine continued interaction [ 257 ]. Successful “consequence engines” in bots are also already capable of internally modeling their environment and other entities in order to avoid collisions, coordinate without communication, and reach their goals [ 30 ]. Likewise, using deep neural nets, Google’s DeepMind is developing ToM with the AI agent ToMnet, which is capable of building heuristics from basic mind models of other agents that are derived from meta-learning observations of their behaviour [ 242 ].…”

Section: Towards Eroboticsmentioning

confidence: 99%

Foundations of Erobotics

Dubé

Anctil

2020

Int J of Soc Robotics

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Technology is giving rise to artificial erotic agents, which we call erobots ( erôs + bot). Erobots, such as virtual or augmented partners, erotic chatbots, and sex robots, increasingly expose humans to the possibility of intimacy and sexuality with artificial agents. Their advent has sparked academic and public debates: some denounce their risks (e.g., promotion of harmful sociosexual norms), while others defend their potential benefits (e.g., health, education, and research applications). Yet, the scientific study of human–machine erotic interaction is limited; no comprehensive theoretical models have been proposed and the empirical literature remains scarce. The current research programs investigating erotic technologies tend to focus on the risks and benefits of erobots, rather than providing solutions to resolve the former and enhance the latter. Moreover, we feel that these programs underestimate how humans and machines unpredictably interact and co-evolve, as well as the influence of sociocultural processes on technological development and meaning attribution. To comprehensively explore human–machine erotic interaction and co-evolution, we argue that we need a new unified transdisciplinary field of research—grounded in sexuality and technology positive frameworks—focusing on human-erobot interaction and co-evolution as well as guiding the development of beneficial erotic machines. We call this field Erobotics . As a first contribution to this new discipline, this article defines Erobotics and its related concepts; proposes a model of human-erobot interaction and co-evolution; and suggests a path to design beneficial erotic machines that could mitigate risks and enhance human well-being.

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Simulation-Based Internal Models for Safer Robots

Cited by 35 publications

References 46 publications

The Parasitic Nature of Social AI: Sharing Minds with the Mindless

The Parasitic Nature of Social AI: Sharing Minds with the Mindless

Prerequisites for an Artificial Self

Foundations of Erobotics

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