42,695 research outputs found
Embodied Evolution in Collective Robotics: A Review
This paper provides an overview of evolutionary robotics techniques applied
to on-line distributed evolution for robot collectives -- namely, embodied
evolution. It provides a definition of embodied evolution as well as a thorough
description of the underlying concepts and mechanisms. The paper also presents
a comprehensive summary of research published in the field since its inception
(1999-2017), providing various perspectives to identify the major trends. In
particular, we identify a shift from considering embodied evolution as a
parallel search method within small robot collectives (fewer than 10 robots) to
embodied evolution as an on-line distributed learning method for designing
collective behaviours in swarm-like collectives. The paper concludes with a
discussion of applications and open questions, providing a milestone for past
and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl
Approximating n-player behavioural strategy nash equilibria using coevolution
Coevolutionary algorithms are plagued with a set of problems related to intransitivity that make it questionable what the end product of a coevolutionary run can achieve. With the introduction of solution concepts into coevolution, part of the issue was alleviated, however efficiently representing and achieving game theoretic solution concepts is still not a trivial task. In this paper we propose a coevolutionary algorithm that approximates behavioural strategy Nash equilibria in n-player zero sum games, by exploiting the minimax solution concept. In order to support our case we provide a set of experiments in both games of known and unknown equilibria. In the case of known equilibria, we can confirm our algorithm converges to the known solution, while in the case of unknown equilibria we can see a steady progress towards Nash. Copyright 2011 ACM
Active causation and the origin of meaning
Purpose and meaning are necessary concepts for understanding mind and
culture, but appear to be absent from the physical world and are not part of
the explanatory framework of the natural sciences. Understanding how meaning
(in the broad sense of the term) could arise from a physical world has proven
to be a tough problem. The basic scheme of Darwinian evolution produces
adaptations that only represent apparent ("as if") goals and meaning. Here I
use evolutionary models to show that a slight, evolvable extension of the basic
scheme is sufficient to produce genuine goals. The extension, targeted
modulation of mutation rate, is known to be generally present in biological
cells, and gives rise to two phenomena that are absent from the non-living
world: intrinsic meaning and the ability to initiate goal-directed chains of
causation (active causation). The extended scheme accomplishes this by
utilizing randomness modulated by a feedback loop that is itself regulated by
evolutionary pressure. The mechanism can be extended to behavioural variability
as well, and thus shows how freedom of behaviour is possible. A further
extension to communication suggests that the active exchange of intrinsic
meaning between organisms may be the origin of consciousness, which in
combination with active causation can provide a physical basis for the
phenomenon of free will.Comment: revised and extende
Adaptation to criticality through organizational invariance in embodied agents
Many biological and cognitive systems do not operate deep within one or other
regime of activity. Instead, they are poised at critical points located at
phase transitions in their parameter space. The pervasiveness of criticality
suggests that there may be general principles inducing this behaviour, yet
there is no well-founded theory for understanding how criticality is generated
at a wide span of levels and contexts. In order to explore how criticality
might emerge from general adaptive mechanisms, we propose a simple learning
rule that maintains an internal organizational structure from a specific family
of systems at criticality. We implement the mechanism in artificial embodied
agents controlled by a neural network maintaining a correlation structure
randomly sampled from an Ising model at critical temperature. Agents are
evaluated in two classical reinforcement learning scenarios: the Mountain Car
and the Acrobot double pendulum. In both cases the neural controller appears to
reach a point of criticality, which coincides with a transition point between
two regimes of the agent's behaviour. These results suggest that adaptation to
criticality could be used as a general adaptive mechanism in some
circumstances, providing an alternative explanation for the pervasive presence
of criticality in biological and cognitive systems.Comment: arXiv admin note: substantial text overlap with arXiv:1704.0525
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