Using learned action models in execution monitoring

Planners reason with abstracted models of the behaviours they use to construct plans. When plans are turned into the instructions that drive an executive, the real behaviours interacting with the unpredictable uncertainties of the environment can lead to failure. One of the challenges for intelligent autonomy is to recognise when the actual execution of a behaviour has diverged so far from the expected behaviour that it can be considered to be a failure. In this paper we present further developments of the work described in (Fox et al. 2006), where models of behaviours were learned as Hidden Markov Models. Execution of behaviours is monitored by tracking the most likely trajectory through such a learned model, while possible failures in execution are identified as deviations from common patterns of trajectories within the learned models. We present results for our experiments with a model learned for a robot behaviour

On the identification of normal modes of oscillation from observations of the solar periphery

The decomposition of solar oscillations into their constituent normal modes requires a knowledge of both the spatial and temporal variation of the perturbation to the Sun's surface. The task is especially difficult when only limited spatial information is available. Observations of the limb darkening function, for example, are probably sensitive to too large a number of modes to permit most of the modes to be identified in a power spectrum of measurements at only a few points on the limb, unless the results are combined with other data. A procedure was considered by which the contributions from quite small groups of modes to spatially well resolved data obtained at any instant can be extracted from the remaining modes. Combining these results with frequency information then permits the modes to be identified, at least if their frequencies are low enough to ensure that modes of high degree do not contribute substantially to the signal

Antiferromagnetic Alignment and Relaxation Rate of Gd Spins in the High Temperature Superconductor GdBa_2Cu_3O_(7-delta)

The complex surface impedance of a number of GdBa$_2$Cu$_3$O$_{7-\delta}$ single crystals has been measured at 10, 15 and 21 GHz using a cavity perturbation technique. At low temperatures a marked increase in the effective penetration depth and surface resistance is observed associated with the paramagnetic and antiferromagnetic alignment of the Gd spins. The effective penetration depth has a sharp change in slope at the N\'eel temperature, $T_N$, and the surface resistance peaks at a frequency dependent temperature below 3K. The observed temperature and frequency dependence can be described by a model which assumes a negligibly small interaction between the Gd spins and the electrons in the superconducting state, with a frequency dependent magnetic susceptibility and a Gd spin relaxation time $\tau_s$ being a strong function of temperature. Above $T_N$, $\tau_s$ has a component varying as $1 / (T - T_N)$, while below $T_N$ it increases $\sim T^{-5}$.Comment: 4 Pages, 4 Figures. Submitted to Phys. Rev.

Probing Solar Convection

In the solar convection zone acoustic waves are scattered by turbulent sound speed fluctuations. In this paper the scattering of waves by convective cells is treated using Rytov's technique. Particular care is taken to include diffraction effects which are important especially for high-degree modes that are confined to the surface layers of the Sun. The scattering leads to damping of the waves and causes a phase shift. Damping manifests itself in the width of the spectral peak of p-mode eigenfrequencies. The contribution of scattering to the line widths is estimated and the sensitivity of the results on the assumed spectrum of the turbulence is studied. Finally the theoretical predictions are compared with recently measured line widths of high-degree modes.Comment: 26 pages, 7 figures, accepted by MNRA