608 research outputs found
On the relativistic viability of multi-automaton systems: essential concepts, challenges and prospects
Our understanding of the Universe breaks down for very small spacetime
intervals, corresponding to an extremely high level of granularity (and
energy), commonly referred to as the ``Planck scale''. At this fundamental
level, there are attempts of describing physics in terms of interacting
automata that perform classical, deterministic computation. On one hand,
various mathematical arguments have already illustrated how quantum laws (which
describe elementary particles and interactions) could in principle arise as
low-granularity approximations of automata-based systems. On the other hand,
understanding how such systems might give rise to relativistic laws (which
describe spacetime and gravity) remains a major problem. I explain here a few
ideas that seem crucial for overcoming this problem, along with related
algorithmic challenges that need to be addressed. Giving emphasis to meaningful
computational counterparts of locality and general covariance, I outline basic
ingredients of a distributed communication-rewiring protocol that would allow
us to construct multi-automaton models that are viable from a relativistic
perspective. I also explain how viable models can be evaluated using a variety
of criteria, and discuss related aspects pertaining to the falsifiability and
plausibility of the automata paradigm.Comment: 7 pages, 1 figur
Feasible, Robust and Reliable Automation and Control for Autonomous Systems
The Special Issue book focuses on highlighting current research and developments in the automation and control field for autonomous systems as well as showcasing state-of-the-art control strategy approaches for autonomous platforms. The book is co-edited by distinguished international control system experts currently based in Sweden, the United States of America, and the United Kingdom, with contributions from reputable researchers from China, Austria, France, the United States of America, Poland, and Hungary, among many others. The editors believe the ten articles published within this Special Issue will be highly appealing to control-systems-related researchers in applications typified in the fields of ground, aerial, maritime vehicles, and robotics as well as industrial audiences
Applications of artificial intelligence techniques to a spacecraft control problem
Artificial intelligence applied to spacecraft control proble
Learning Certifiably Robust Controllers Using Fragile Perception
Advances in computer vision and machine learning enable robots to perceive
their surroundings in powerful new ways, but these perception modules have
well-known fragilities. We consider the problem of synthesizing a safe
controller that is robust despite perception errors. The proposed method
constructs a state estimator based on Gaussian processes with input-dependent
noises. This estimator computes a high-confidence set for the actual state
given a perceived state. Then, a robust neural network controller is
synthesized that can provably handle the state uncertainty. Furthermore, an
adaptive sampling algorithm is proposed to jointly improve the estimator and
controller. Simulation experiments, including a realistic vision-based
lane-keeping example in CARLA, illustrate the promise of the proposed approach
in synthesizing robust controllers with deep-learning-based perception
Recognition of pen-based music notation with finite-state machines
This work presents a statistical model to recognize pen-based music compositions using stroke recognition algorithms and finite-state machines. The series of strokes received as input is mapped onto a stochastic representation, which is combined with a formal language that describes musical symbols in terms of stroke primitives. Then, a Probabilistic Finite-State Automaton is obtained, which defines probabilities over the set of musical sequences. This model is eventually crossed with a semantic language to avoid sequences that does not make musical sense. Finally, a decoding strategy is applied in order to output a hypothesis about the musical sequence actually written. Comprehensive experimentation with several decoding algorithms, stroke similarity measures and probability density estimators are tested and evaluated following different metrics of interest. Results found have shown the goodness of the proposed model, obtaining competitive performances in all metrics and scenarios considered.This work was supported by the Spanish Ministerio de Educación, Cultura y Deporte through a FPU Fellowship (Ref. AP2012–0939) and the Spanish Ministerio de EconomÃa y Competitividad through the TIMuL Project (No. TIN2013-48152-C2-1-R, supported by UE FEDER funds)
A Developmental Organization for Robot Behavior
This paper focuses on exploring how learning and development can be structured in synthetic (robot) systems. We present a developmental assembler for constructing reusable and temporally extended actions in a sequence. The discussion adopts the traditions
of dynamic pattern theory in which behavior
is an artifact of coupled dynamical systems
with a number of controllable degrees of freedom. In our model, the events that delineate
control decisions are derived from the pattern
of (dis)equilibria on a working subset of sensorimotor policies. We show how this architecture can be used to accomplish sequential
knowledge gathering and representation tasks
and provide examples of the kind of developmental milestones that this approach has
already produced in our lab
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