scholarly journals Lessons from Speciation Dynamics: How to Generate Selective Pressure Towards Diversity

2015 ◽  
Vol 21 (4) ◽  
pp. 464-480 ◽  
Author(s):  
Heiko Hamann
Keyword(s):  
Selective Pressure ◽  
A Priori ◽  
Evolutionary Robotics ◽  
Search Space ◽  
Alternative Methods ◽  
A Priori Knowledge ◽  
Behavioral Diversity ◽  
Definition Of ◽  
Priori Knowledge

Recent approaches in evolutionary robotics (ER) propose to generate behavioral diversity in order to evolve desired behaviors more easily. These approaches require the definition of a behavioral distance, which often includes task-specific features and hence a priori knowledge. Alternative methods, which do not explicitly force selective pressure towards diversity (SPTD) but still generate it, are known from the field of artificial life, such as in artificial ecologies (AEs). In this study, we investigate how SPTD is generated without task-specific behavioral features or other forms of a priori knowledge and detect how methods of generating SPTD can be transferred from the domain of AE to ER. A promising finding is that in both types of systems, in systems from ER that generate behavioral diversity and also in the investigated speciation model, selective pressure is generated towards unpopulated regions of search space. In a simple case study we investigate the practical implications of these findings and point to options for transferring the idea of self-organizing SPTD in AEs to the domain of ER.

On Kitcher’s Definition of A Priori Knowledge

2004 ◽  
Vol 48 (3) ◽  
pp. 235-244
Author(s):  
Charles Cassini ◽  
Keyword(s):  
A Priori ◽  
A Priori Knowledge ◽  
Definition Of ◽  
Priori Knowledge

scholarly journals Pseudo-Elastic Response of Gas Bearing Clastic Formations: An Italian Case Study

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2488 ◽  
Author(s):  
Christian Coti ◽  
Vera Rocca ◽  
Quinto Sacchi
Keyword(s):  
Land Surface ◽  
A Priori ◽  
Fluid Pressure ◽  
Back Analysis ◽  
Elastic Response ◽  
A Priori Knowledge ◽  
Model Parameters ◽  
Elastic Model ◽  
Priori Knowledge

The research presented in this paper focuses on the analysis of land movements induced by underground gas storage operations in a depleted reservoir in Northern Italy with the aim of increasing the understanding of the deformation response of deep formations via a real case study. The a priori knowledge of the pseudo-elastic parameters showed a substantial discrepancy between static values from triaxial lab tests and dynamic values obtained via the interpretation of sonic data at wellbore scale. The discrepancy is not surprising for the formations under investigation: A thousand meters of a silty to shaly sequence intercalated with arenaceous banks above a reservoir formation, which is basically made up of sandstone intercalated with shale intervals and conglomerates. Information collected for over more than ten years of seasonal production/injection cycles (i.e., time and space evolution of the reservoir fluid pressure and of the induced land surface movements) was then combined in a 3D numerical geomechanical model to constrain and update the a priori knowledge on the pseudo elastic model parameters via a back analysis approach. The obtained calibrated model will then be used for reliable prediction of system safety analyses, for example in terms of induced ground movements.

Seismic results as a-priori knowledge for airborne TEM data inversion — A case study

2012 ◽  
Vol 80 ◽  
pp. 121-128 ◽  
Author(s):  
Thomas Burschil ◽  
Helga Wiederhold ◽  
Esben Auken
Keyword(s):  
A Priori ◽  
A Priori Knowledge ◽  
Data Inversion ◽  
Priori Knowledge

A Definition of A Priori Knowledge

1975 ◽  
Vol 6 (1) ◽  
pp. 1-8
Author(s):  
Michael E. Levin ◽  
Keyword(s):  
A Priori ◽  
A Priori Knowledge ◽  
Definition Of ◽  
Priori Knowledge

scholarly journals BosonSampling is far from uniform

2014 ◽  
Vol 14 (15&16) ◽  
pp. 1383-1423 ◽  
Author(s):  
Scott Aaronson ◽  
Alex Arkhipov
Keyword(s):  
Optical Networks ◽  
A Priori ◽  
Quantum Algorithm ◽  
A Priori Knowledge ◽  
Polynomial Size ◽  
Constant Bias ◽  
Variation Distance ◽  
Definition Of ◽  
Classical Computer ◽  
Priori Knowledge

BosonSampling, which we proposed three years ago, is a scheme for using linear-optical networks to solve sampling problems that appear to be intractable for a classical computer. \ In a recent manuscript, Gogolin et al.\ claimed that even an ideal BosonSampling device's output would be operationally indistinguishable\textquotedblright\ from a uniform random outcome, at least \textquotedblleft without detailed a priori knowledge; or at any rate, that telling the two apart might itself be a hard problem. We first answer these claims---explaining why the first is based on a definition of a priori knowledge such that, were it adopted, almost no quantum algorithm could be distinguished from a pure random-number source; while the second is neither new nor a practical obstacle to interesting BosonSampling experiments.However, we then go further, and address some interesting research questions inspired by Gogolin et al.'s arguments. We prove that, with high probability over a Haar-random matrix $A$, the BosonSampling distribution induced by $A$ is far from the uniform distribution in total variation distance. More surprisingly, and counter to Gogolin et al., we give an efficient algorithm that distinguishes these two distributions with constant bias. Finally, we offer three bonus results about BosonSampling. First, we report an observation of Fernando Brandao: that one can efficiently sample a distribution that has large entropy and that's indistinguishable from a BosonSampling distribution by any circuit of fixed polynomial size. Second, we show that BosonSampling distributions can be efficiently distinguished from uniform even with photon losses and for general initial states. Third, we offer the simplest known proof that Fermion Sampling is solvable in classical polynomial time, and we reuse techniques from our Boson Sampling analysis to characterize random FermionSampling distributions.

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