13,048 research outputs found
RankPL: A Qualitative Probabilistic Programming Language
In this paper we introduce RankPL, a modeling language that can be thought of
as a qualitative variant of a probabilistic programming language with a
semantics based on Spohn's ranking theory. Broadly speaking, RankPL can be used
to represent and reason about processes that exhibit uncertainty expressible by
distinguishing "normal" from" surprising" events. RankPL allows (iterated)
revision of rankings over alternative program states and supports various types
of reasoning, including abduction and causal inference. We present the
language, its denotational semantics, and a number of practical examples. We
also discuss an implementation of RankPL that is available for download
Stochastic Spin-Orbit Torque Devices as Elements for Bayesian Inference
Probabilistic inference from real-time input data is becoming increasingly
popular and may be one of the potential pathways at enabling cognitive
intelligence. As a matter of fact, preliminary research has revealed that
stochastic functionalities also underlie the spiking behavior of neurons in
cortical microcircuits of the human brain. In tune with such observations,
neuromorphic and other unconventional computing platforms have recently started
adopting the usage of computational units that generate outputs
probabilistically, depending on the magnitude of the input stimulus. In this
work, we experimentally demonstrate a spintronic device that offers a direct
mapping to the functionality of such a controllable stochastic switching
element. We show that the probabilistic switching of Ta/CoFeB/MgO
heterostructures in presence of spin-orbit torque and thermal noise can be
harnessed to enable probabilistic inference in a plethora of unconventional
computing scenarios. This work can potentially pave the way for hardware that
directly mimics the computational units of Bayesian inference
Progress on Intelligent Guidance and Control for Wind Shear Encounter
Low altitude wind shear poses a serious threat to air safety. Avoiding severe wind shear challenges the ability of flight crews, as it involves assessing risk from uncertain evidence. A computerized intelligent cockpit aid can increase flight crew awareness of wind shear, improving avoidance decisions. The primary functions of a cockpit advisory expert system for wind shear avoidance are discussed. Also introduced are computational techniques being implemented to enable these primary functions
Probabilistic Logic Programming with Beta-Distributed Random Variables
We enable aProbLog---a probabilistic logical programming approach---to reason
in presence of uncertain probabilities represented as Beta-distributed random
variables. We achieve the same performance of state-of-the-art algorithms for
highly specified and engineered domains, while simultaneously we maintain the
flexibility offered by aProbLog in handling complex relational domains. Our
motivation is that faithfully capturing the distribution of probabilities is
necessary to compute an expected utility for effective decision making under
uncertainty: unfortunately, these probability distributions can be highly
uncertain due to sparse data. To understand and accurately manipulate such
probability distributions we need a well-defined theoretical framework that is
provided by the Beta distribution, which specifies a distribution of
probabilities representing all the possible values of a probability when the
exact value is unknown.Comment: Accepted for presentation at AAAI 201
- …