Increasing the Numeric Expressiveness of the Planning Domain Definition Language

The technology of artificial intelligence (AI) planning is being adopted across many different disciplines. This has resulted in the wider use of the Planning Domain Definition Language (PDDL), where it is being used to model planning problems of different natures. One such area where AI planning is particularly attractive is engineering, where the optimisation problems are mathematically rich. The example used throughout this paper is the optimisation (minimisation) of machine tool measurement uncertainty. This planning problem highlights the limits of PDDL's numerical expressiveness in the absence of the square root function. A workaround method using the Babylonian algorithm is then evaluated before the extension of PDDL to include more mathematics functions is discussed

NTFS Permissions Explorer

Administrating and monitoring NTFS permissions can be somewhat of a cumbersome and convoluted task. In today’s modern data rich world there has never been a more important time to ensure that your data is secured against unwanted access. This software-based solution has been produced to aid user understand of the current implemented permissions and identify possible problems

Quintessence reconstructed: new constraints and tracker viability

We update and extend our previous work reconstructing the potential of a quintessence field from current observational data. We extend the cosmological data set to include new supernova data, plus information from the cosmic microwave background and from baryon acoustic oscillations. We extend the modeling by considering Padé approximant expansions as well as Taylor series, and by using observations to assess the viability of the tracker hypothesis. We find that parameter constraints have improved by a factor of 2, with a strengthening of the preference of the cosmological constant over evolving quintessence models. Present data show some signs, though inconclusive, of favoring tracker models over nontracker models under our assumptions

Representing the Process of Machine Tool Calibration in First-order Logic

Machine tool calibration requires a wide range of measurement techniques that can be carried out in many different sequences. Planning a machine tool calibration is typically performed by a subject expert with a great understanding of International standards and industrial best-practice guides. However, it is often the case that the planned sequence of measurements is not the optimal. Therefore, in an attempt to improve the process, intelligent computing methods can be designed for plan suggestion. As a starting point, this paper presents a way of converting expert knowledge into first-order logic that can be expressed in the PROLOG language. It then shows how queries can be executed against the logic to construct a knowledge-base of all the different measurements that can be performed during machine tool calibration

Constraining the dark fluid

Cosmological observations are normally fit under the assumption that the dark sector can be decomposed into dark matter and dark energy components. However, as long as the probes remain purely gravitational, there is no unique decomposition and observations can only constrain a single dark fluid; this is known as the dark degeneracy. We use observations to directly constrain this dark fluid in a model-independent way, demonstrating in particular that the data cannot be fit by a dark fluid with a single constant equation of state. Parameterizing the dark fluid equation of state by a variety of polynomials in the scale factor $a$, we use current kinematical data to constrain the parameters. While the simplest interpretation of the dark fluid remains that it is comprised of separate dark matter and cosmological constant contributions, our results cover other model types including unified dark energy/matter scenarios.Comment: 5 pages, 5 figures incorporated. Updated to new observational data including SHOES determination of H0; new citations adde

Unified dark energy and dark matter from a scalar field different from quintessence

We explore unification of dark matter and dark energy in a theory containing a scalar field of non-Lagrangian type, obtained by direct insertion of a kinetic term into the energy-momentum tensor. This scalar is different from quintessence, having an equation of state between -1 and 0 and a zero sound speed in its rest frame. We solve the equations of motion for an exponential potential via a rewriting as an autonomous system, and demonstrate the observational viability of the scenario, for sufficiently small exponential potential parameter \lambda, by comparison to a compilation of kinematical cosmological data.Comment: 10 pages RevTeX4 with 5 figures incorporate

Models for Human Navigation and Optimal Path Planning Using Level Set Methods and Hamilton-Jacobi Equations

We present several models for different physical scenarios which are centered around human movement or optimal path planning, and use partial differential equations and concepts from control theory. The first model is a game-theoretic model for environmental crime which tracks criminals' movement using the level set method, and improves upon previous continuous models by removing overly restrictive assumptions of symmetry. Next, we design a method for determining optimal hiking paths in mountainous regions using an anisotropic level set equation. After this, we present a model for optimal human navigation with uncertainty which is rooted in dynamic programming and stochastic optimal control theory. Lastly, we consider optimal path planning for simple, self-driving cars in the Hamilton-Jacobi formulation. We improve upon previous models which simplify the car to a point mass, and present a reasonably general upwind, sweeping scheme to solve the relevant Hamilton-Jacobi equation

Reconstructing thawing quintessence with multiple datasets

In this work we model the quintessence potential in a Taylor series expansion, up to second order, around the present-day value of the scalar field. The field is evolved in a thawing regime assuming zero initial velocity. We use the latest data from the Planck satellite, baryonic acoustic oscillations observations from the Sloan Digital Sky Survey, and Supernovae luminosity distance information from Union2.1 to constrain our models parameters, and also include perturbation growth data from the WiggleZ, BOSS and the 6dF surveys. The supernova data provide the strongest individual constraint on the potential parameters. We show that the growth data performance is competitive with the other datasets in constraining the dark energy parameters we introduce. We also conclude that the combined constraints we obtain for our model parameters, when compared to previous works of nearly a decade ago, have shown only modest improvement, even with new growth of structure data added to previously-existent types of data.Comment: 9 pages, 4 figures and 1 table. Version 2 with minor changes to match Physical Review D accepted versio

Model selection in cosmology

Model selection aims to determine which theoretical models are most plausible given some data, without necessarily considering preferred values of model parameters. A common model selection question is to ask when new data require introduction of an additional parameter, describing a newly discovered physical effect. We review model selection statistics, then focus on the Bayesian evidence, which implements Bayesian analysis at the level of models rather than parameters. We describe our CosmoNest code, the first computationally efficient implementation of Bayesian model selection in a cosmological context. We apply it to recent WMAP satellite data, examining the need for a perturbation spectral index differing from the scaleinvariant (Harrison–Zel'dovich) case

The WMAP normalization of inflationary cosmologies

We use the three-year WMAP observations to determine the normalization of the matter power spectrum in inflationary cosmologies. In this context, the quantity of interest is not the normalization marginalized over all parameters, but rather the normalization as a function of the inflationary parameters n and r with marginalization over the remaining cosmological parameters. We compute this normalization and provide an accurate fitting function. The statistical uncertainty in the normalization is 3 percent, roughly half that achieved by COBE. We use the k-l relation for the standard cosmological model to identify the pivot scale for the WMAP normalization. We also quote the inflationary energy scale corresponding to the WMAP normalization.Comment: 4 pages RevTex4 with two figure