Decision-Theoretic Planning with non-Markovian Rewards

A decision process in which rewards depend on history rather than merely on the current state is called a decision process with non-Markovian rewards (NMRDP). In decision-theoretic planning, where many desirable behaviours are more naturally expressed as properties of execution sequences rather than as properties of states, NMRDPs form a more natural model than the commonly adopted fully Markovian decision process (MDP) model. While the more tractable solution methods developed for MDPs do not directly apply in the presence of non-Markovian rewards, a number of solution methods for NMRDPs have been proposed in the literature. These all exploit a compact specification of the non-Markovian reward function in temporal logic, to automatically translate the NMRDP into an equivalent MDP which is solved using efficient MDP solution methods. This paper presents NMRDPP (Non-Markovian Reward Decision Process Planner), a software platform for the development and experimentation of methods for decision-theoretic planning with non-Markovian rewards. The current version of NMRDPP implements, under a single interface, a family of methods based on existing as well as new approaches which we describe in detail. These include dynamic programming, heuristic search, and structured methods. Using NMRDPP, we compare the methods and identify certain problem features that affect their performance. NMRDPPs treatment of non-Markovian rewards is inspired by the treatment of domain-specific search control knowledge in the TLPlan planner, which it incorporates as a special case. In the First International Probabilistic Planning Competition, NMRDPP was able to compete and perform well in both the domain-independent and hand-coded tracks, using search control knowledge in the latter

The European Photon Imaging Camera on XMM-Newton: The MOS Cameras

The EPIC focal plane imaging spectrometers on XMM-Newton use CCDs to record the images and spectra of celestial X-ray sources focused by the three X-ray mirrors. There is one camera at the focus of each mirror; two of the cameras contain seven MOS CCDs, while the third uses twelve PN CCDs, defining a circular field of view of 30 arcmin diameter in each case. The CCDs were specially developed for EPIC, and combine high quality imaging with spectral resolution close to the Fano limit. A filter wheel carrying three kinds of X-ray transparent light blocking filter, a fully closed, and a fully open position, is fitted to each EPIC instrument. The CCDs are cooled passively and are under full closed loop thermal control. A radio-active source is fitted for internal calibration. Data are processed on-board to save telemetry by removing cosmic ray tracks, and generating X-ray event files; a variety of different instrument modes are available to increase the dynamic range of the instrument and to enable fast timing. The instruments were calibrated using laboratory X-ray beams, and synchrotron generated monochromatic X-ray beams before launch; in-orbit calibration makes use of a variety of celestial X-ray targets. The current calibration is better than 10% over the entire energy range of 0.2 to 10 keV. All three instruments survived launch and are performing nominally in orbit. In particular full field-of-view coverage is available, all electronic modes work, and the energy resolution is close to pre-launch values. Radiation damage is well within pre-launch predictions and does not yet impact on the energy resolution. The scientific results from EPIC amply fulfil pre-launch expectations.Comment: 9 pages, 11 figures, accepted for publication in the A&A Special Issue on XMM-Newto

Statistical learning techniques applied to epidemiology: a simulated case-control comparison study with logistic regression

<p>Abstract</p> <p>Background</p> <p>When investigating covariate interactions and group associations with standard regression analyses, the relationship between the response variable and exposure may be difficult to characterize. When the relationship is nonlinear, linear modeling techniques do not capture the nonlinear information content. Statistical learning (SL) techniques with kernels are capable of addressing nonlinear problems without making parametric assumptions. However, these techniques do not produce findings relevant for epidemiologic interpretations. A simulated case-control study was used to contrast the information embedding characteristics and separation boundaries produced by a specific SL technique with logistic regression (LR) modeling representing a parametric approach. The SL technique was comprised of a kernel mapping in combination with a perceptron neural network. Because the LR model has an important epidemiologic interpretation, the SL method was modified to produce the analogous interpretation and generate odds ratios for comparison.</p> <p>Results</p> <p>The SL approach is capable of generating odds ratios for main effects and risk factor interactions that better capture nonlinear relationships between exposure variables and outcome in comparison with LR.</p> <p>Conclusions</p> <p>The integration of SL methods in epidemiology may improve both the understanding and interpretation of complex exposure/disease relationships.</p

“Working the System”—British American Tobacco's Influence on the European Union Treaty and Its Implications for Policy: An Analysis of Internal Tobacco Industry Documents

Katherine Smith and colleagues investigate the ways in which British American Tobacco influenced the European Union Treaty so that new EU policies advance the interests of major corporations, including those that produce products damaging to health