15,236 research outputs found
Information-Based Physics: An Observer-Centric Foundation
It is generally believed that physical laws, reflecting an inherent order in
the universe, are ordained by nature. However, in modern physics the observer
plays a central role raising questions about how an observer-centric physics
can result in laws apparently worthy of a universal nature-centric physics.
Over the last decade, we have found that the consistent apt quantification of
algebraic and order-theoretic structures results in calculi that possess
constraint equations taking the form of what are often considered to be
physical laws. I review recent derivations of the formal relations among
relevant variables central to special relativity, probability theory and
quantum mechanics in this context by considering a problem where two observers
form consistent descriptions of and make optimal inferences about a free
particle that simply influences them. I show that this approach to describing
such a particle based only on available information leads to the mathematics of
relativistic quantum mechanics as well as a description of a free particle that
reproduces many of the basic properties of a fermion. The result is an approach
to foundational physics where laws derive from both consistent descriptions and
optimal information-based inferences made by embedded observers.Comment: To be published in Contemporary Physics. The manuscript consists of
43 pages and 9 Figure
Information Physics: The New Frontier
At this point in time, two major areas of physics, statistical mechanics and
quantum mechanics, rest on the foundations of probability and entropy. The last
century saw several significant fundamental advances in our understanding of
the process of inference, which make it clear that these are inferential
theories. That is, rather than being a description of the behavior of the
universe, these theories describe how observers can make optimal predictions
about the universe. In such a picture, information plays a critical role. What
is more is that little clues, such as the fact that black holes have entropy,
continue to suggest that information is fundamental to physics in general.
In the last decade, our fundamental understanding of probability theory has
led to a Bayesian revolution. In addition, we have come to recognize that the
foundations go far deeper and that Cox's approach of generalizing a Boolean
algebra to a probability calculus is the first specific example of the more
fundamental idea of assigning valuations to partially-ordered sets. By
considering this as a natural way to introduce quantification to the more
fundamental notion of ordering, one obtains an entirely new way of deriving
physical laws. I will introduce this new way of thinking by demonstrating how
one can quantify partially-ordered sets and, in the process, derive physical
laws. The implication is that physical law does not reflect the order in the
universe, instead it is derived from the order imposed by our description of
the universe. Information physics, which is based on understanding the ways in
which we both quantify and process information about the world around us, is a
fundamentally new approach to science.Comment: 17 pages, 6 figures. Knuth K.H. 2010. Information physics: The new
frontier. J.-F. Bercher, P. Bessi\`ere, and A. Mohammad-Djafari (eds.)
Bayesian Inference and Maximum Entropy Methods in Science and Engineering
(MaxEnt 2010), Chamonix, France, July 201
Modeling a Sensor to Improve its Efficacy
Robots rely on sensors to provide them with information about their
surroundings. However, high-quality sensors can be extremely expensive and
cost-prohibitive. Thus many robotic systems must make due with lower-quality
sensors. Here we demonstrate via a case study how modeling a sensor can improve
its efficacy when employed within a Bayesian inferential framework. As a test
bed we employ a robotic arm that is designed to autonomously take its own
measurements using an inexpensive LEGO light sensor to estimate the position
and radius of a white circle on a black field. The light sensor integrates the
light arriving from a spatially distributed region within its field of view
weighted by its Spatial Sensitivity Function (SSF). We demonstrate that by
incorporating an accurate model of the light sensor SSF into the likelihood
function of a Bayesian inference engine, an autonomous system can make improved
inferences about its surroundings. The method presented here is data-based,
fairly general, and made with plug-and play in mind so that it could be
implemented in similar problems.Comment: 18 pages, 8 figures, submitted to the special issue of "Sensors for
Robotics
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