Allostatic Control for Robot Behavior Regulation: A Comparative Rodent-Robot Study

2016

Phil. Trans. R. Soc. B

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One contribution of 13 to a theme issue 'The major synthetic evolutionary transitions'. Understanding the nature of consciousness is one of the grand outstanding scientific challenges. The fundamental methodological problem is how phenomenal first person experience can be accounted for in a third person verifiable form, while the conceptual challenge is to both define its function and physical realization. The distributed adaptive control theory of consciousness (DACtoc) proposes answers to these three challenges. The methodological challenge is answered relative to the hard problem and DACtoc proposes that it can be addressed using a convergent synthetic methodology using the analysis of synthetic biologically grounded agents, or quale parsing. DACtoc hypothesizes that consciousness in both its primary and secondary forms serves the ability to deal with the hidden states of the world and emerged during the Cambrian period, affording stable multi-agent environments to emerge. The process of consciousness is an autonomous virtualization memory, which serializes and unifies the parallel and subconscious simulations of the hidden states of the world that are largely due to other agents and the self with the objective to extract norms. These norms are in turn projected as value onto the parallel simulation and control systems that are driving action. This functional hypothesis is mapped onto the brainstem, midbrain and the thalamo-cortical and cortico-cortical systems and analysed with respect to our understanding of deficits of consciousness. Subsequently, some of the implications and predictions of DACtoc are outlined, in particular, the prediction that normative bootstrapping of conscious agents is predicated on an intentionality prior. In the view advanced here, human consciousness constitutes the ultimate evolutionary transition by allowing agents to become autonomous with respect to their evolutionary priors leading to a post-biological Anthropocene.This article is part of the themed issue 'The major synthetic evolutionary transitions'.

Section: Introduction 'That Is Very Hokey!'mentioning

confidence: 99%

Synthetic consciousness: the distributed adaptive control perspective

2016

Phil. Trans. R. Soc. B

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“…These sensorimotor loops are organized in fundamental and opposing behaviour systems that support basic needs such as fight, flight [ 15 ], seek and play [ 16 ]. Every behaviour system is homeostatic but importantly their collective properties are in turn regulated by an integrative allostatic loop [ 17 , 18 ]. This allostatic orchestration (see figure 1 ) is critical to goal-oriented behaviour because both at a behavioural and physiological level, different homeostatic subsystems are in a competitive relationship and priorities and hierarchies must be established on the fly dependent on internal needs, external threats and opportunities.…”

Section: Introductionmentioning

confidence: 99%

The why, what, where, when and how of goal-directed choice: neuronal and computational principles

Pennartz

Pezzulo

2014

Phil. Trans. R. Soc. B

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121

The central problems that goal-directed animals must solve are: ‘What do I need and Why, Where and When can this be obtained, and How do I get it?' or the H4W problem. Here, we elucidate the principles underlying the neuronal solutions to H4W using a combination of neurobiological and neurorobotic approaches. First, we analyse H4W from a system-level perspective by mapping its objectives onto the Distributed Adaptive Control embodied cognitive architecture which sees the generation of adaptive action in the real world as the primary task of the brain rather than optimally solving abstract problems. We next map this functional decomposition to the architecture of the rodent brain to test its consistency. Following this approach, we propose that the mammalian brain solves the H4W problem on the basis of multiple kinds of outcome predictions, integrating central representations of needs and drives (e.g. hypothalamus), valence (e.g. amygdala), world, self and task state spaces (e.g. neocortex, hippocampus and prefrontal cortex, respectively) combined with multi-modal selection (e.g. basal ganglia). In our analysis, goal-directed behaviour results from a well-structured architecture in which goals are bootstrapped on the basis of predefined needs, valence and multiple learning, memory and planning mechanisms rather than being generated by a singular computation.

“…With the addition of the adaptive layer and with the possibility of performing agent identification, a presence value of each group member is maintained that has similar dynamics than the reactive loop. It could be interesting to relate this multiloop regulation to the physiological concept of allostasis as we see it in [11] (a meta-regulation of homeostatic loops) and thus we foresee a possible unifying principle for regulating the loops of both reactive and adaptive layers.…”

Section: Discussionmentioning

confidence: 96%

Autonomous development of turn-taking behaviors in agent populations: A computational study

Moulin-Frier

Sánchez-Fibla

2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)

2015

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We provide an original computational model showing how turn-taking behaviors can self-organize out of sensorimotor interactions between vocalizing agents. These agents are equipped with a cognitive architecture based on two coupled control loops: a reactive one implementing a basic regulatory behavior to maintain vocal listening and an adaptive one learning an action policy to maximize an overall group presence estimation. We show that the reactive process allows to bootstrap the adaptive learning to converge toward a collective turn-taking strategy. This model provides a computational support to the hypothesis that turn-taking can emerge from functional constraints related to group cohesion and vocal signal interferences and suggests future directions of research to understand how social behaviors can result from sensorimotor interactions.