Self discovery enables robot social cognition: Are you my teacher?

Kaipa, Krishnanand N.; Bongard, Josh; Meltzoff, Andrew N.

doi:10.1016/j.neunet.2010.07.009

Cited by 12 publications

(13 citation statements)

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“…Arbib & Fellous 2004;Asada 2015;Bostrom 2014;Briegel 2012;Cardon 2006;Clark 2015; in press a;Davies 2016;Fung 2015;Han et al 2013;Kaipa et al 2010;Lake et al 2018;Lumaca & Baggio 2017;Mathews et al 2017;McShea 2013;Parisi 1997;Thomaz & Cakmak 2013;Wallach et al 2010). Trends in state-of-the-art digital life research and development emphasize dynamic machine-learning construction, operation, and evolution of whole semi-autonomous artificial organisms, such as OpenWorm's simulated nematode Caenorhabditis elegans and the plant-science community's crop-on-a-chip Arabidopsis thaliana.…”

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confidence: 99%

Thinking through other minds: A variational approach to cognition and culture

et al. 2019

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The processes underwriting the acquisition of culture remain unclear. How are shared habits, norms, and expectations learned and maintained with precision and reliability across large-scale sociocultural ensembles? Is there a unifying account of the mechanisms involved in the acquisition of culture? Notions such as “shared expectations,” the “selective patterning of attention and behaviour,” “cultural evolution,” “cultural inheritance,” and “implicit learning” are the main candidates to underpin a unifying account of cognition and the acquisition of culture; however, their interactions require greater specification and clarification. In this article, we integrate these candidates using the variational (free-energy) approach to human cognition and culture in theoretical neuroscience. We describe the construction by humans of social niches that afford epistemic resources called cultural affordances. We argue that human agents learn the shared habits, norms, and expectations of their culture through immersive participation in patterned cultural practices that selectively pattern attention and behaviour. We call this process “thinking through other minds” (TTOM) – in effect, the process of inferring other agents’ expectations about the world and how to behave in social context. We argue that for humans, information from and about other people's expectations constitutes the primary domain of statistical regularities that humans leverage to predict and organize behaviour. The integrative model we offer has implications that can advance theories of cognition, enculturation, adaptation, and psychopathology. Crucially, this formal (variational) treatment seeks to resolve key debates in current cognitive science, such as the distinction between internalist and externalist accounts of theory of mind abilities and the more fundamental distinction between dynamical and representational accounts of enactivism.

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confidence: 99%

Thinking through other minds: A variational approach to cognition and culture

et al. 2019

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“…Within a modern framework of promising, yet still inadequate state-of-the-art artificial intelligence, Lake et al construct an optimistic, ambitious plan for innovating truer representative neural network-inspired machine emulations of human consciousness and cognition, elusive pinnacle goals of many cognitive, semiotic, and cybernetic scientists (Cardon 2006;Clark 2012;Kaipa et al 2010;McShea 2013). Their machine learning-based agenda, possibly requiring future generations of pioneering hybrid neuromorphic computing architectures and other sorts of technologies to be fully attained (Lande 1998;Indiveri & Liu 2015;Schuller & Stevens 2015), relies on implementing sets of data-/theory-established "core ingredients" typical of natural human intelligence and development (cf.…”

Section: Theories or Fragments?mentioning

confidence: 99%

“…Amid even further examples, they may also include the presence or absence of empathy, morality, or ethics in response to severe human plight and need. Regardless, to completely simulate the range of human intelligence, particularly solitary to sociable and selfish to selfless tendencies critical for now-nascent social-like human-machine and machine-machine interactions, scientists and technologists must account for, and better understand, personality trait formation and development in autonomous artificial technologies (Cardon 2006;Clark 2012;Kaipa et al 2010;McShea 2013). These kinds of undertakings will help yield desirable insights into the evolution of technology-augmented human nature and, perhaps more importantly, will inform best practices when establishing advisable failsafe contingencies against unwanted serendipitous or designed human-like machine behavior.…”

Section: Theories or Fragments?mentioning

confidence: 99%

“…Even simplistic artificial cognitive-emotional profiles and personalities thus effect varying control over acquisition and lean of machine domain-general/-specific knowledge, perception and expression of flat or excessive machine affect, and rationality and use of inferential machine attitudes/opinions/beliefs (Arbib & Fellous 2004;Asada 2015;Berdahl 2010;Cardon 2006;Davies 2016;Di & Wu 2015;Han et al 2013;Hiolle et al 2014;Kaipa et al 2010;McShea 2013;Read et al 2010;Wallach et al 2010;Youyou et al 2015). And, by favoring certain artificial personality traits, such as openness, a learner machine's active and passive pedagogical experiences may be radically directed by the quality of teacher-student rapport (e.g., Thomaz & Cakmak 2013), enabling opportunities for superior nurturing and growth of distinctive, well-adjusted thoughtful machine behavior while, in part, restricting harmful rogue machine behavior, caused by impoverished learning environments and predictable pathological Gödel-type incompleteness/inconsistency for axiomatic neuropsychological systems (cf.…”

Section: Theories or Fragments?mentioning

confidence: 99%

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The architecture challenge: Future artificial-intelligence systems will require sophisticated architectures, and knowledge of the brain might guide their construction

Baldassarre¹,

Santucci²,

Cartoni³

et al. 2017

Behav Brain Sci

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“…Meltzoff’s “Like-Me” hypothesis has previously been applied in robotics to tackle the important problem of “whom to imitate” [ 47 ]: In this case, a robot first builds a self model, and then uses this self-model to discover self-other equivalences with other robots so as to distinguish an appropriate from an inappropriate “mentor” robot. Note that in this case as well as in our own work, the “mentor” agent is not necessarily an active teacher but is only being observed (cf.…”

Section: Contributions and Comparisons With Related Workmentioning

confidence: 99%

A Bayesian Developmental Approach to Robotic Goal-Based Imitation Learning

et al. 2015

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A fundamental challenge in robotics today is building robots that can learn new skills by observing humans and imitating human actions. We propose a new Bayesian approach to robotic learning by imitation inspired by the developmental hypothesis that children use self-experience to bootstrap the process of intention recognition and goal-based imitation. Our approach allows an autonomous agent to: (i) learn probabilistic models of actions through self-discovery and experience, (ii) utilize these learned models for inferring the goals of human actions, and (iii) perform goal-based imitation for robotic learning and human-robot collaboration. Such an approach allows a robot to leverage its increasing repertoire of learned behaviors to interpret increasingly complex human actions and use the inferred goals for imitation, even when the robot has very different actuators from humans. We demonstrate our approach using two different scenarios: (i) a simulated robot that learns human-like gaze following behavior, and (ii) a robot that learns to imitate human actions in a tabletop organization task. In both cases, the agent learns a probabilistic model of its own actions, and uses this model for goal inference and goal-based imitation. We also show that the robotic agent can use its probabilistic model to seek human assistance when it recognizes that its inferred actions are too uncertain, risky, or impossible to perform, thereby opening the door to human-robot collaboration.

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Self discovery enables robot social cognition: Are you my teacher?

Cited by 12 publications

References 20 publications

Thinking through other minds: A variational approach to cognition and culture

Thinking through other minds: A variational approach to cognition and culture

The architecture challenge: Future artificial-intelligence systems will require sophisticated architectures, and knowledge of the brain might guide their construction

A Bayesian Developmental Approach to Robotic Goal-Based Imitation Learning

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