Probabilistic Simple Temporal Networks (PSTN) represent scheduling problems under temporal uncertainty. Strong controllability (SC) of PSTNs involves finding a schedule to a PSTN that maximises the probability that all constraints are satisfied (robustness). Previous approaches to this problem assume independence of probabilistic durations, and approximate the risk by bounding it above using Boole’s inequality. This gives no guarantee of finding the schedule optimising robustness, and fails to consider correlations between probabilistic durations that frequently arise in practical applications. In this paper, we formally define the Correlated Simple Temporal Network (Corr-STN) which generalises the PSTN by removing the restriction of independence. We show that the problem of Corr-STN SC is convex for a large class of multivariate (log-concave) distributions. We then introduce an algorithm capable of finding optimal SC schedules to Corr-STNs, using the column generation method. Finally, we validate our approach on a number of Corr-STNs and find that our method offers more robust solutions when compared with prior approaches.

The I3A Camera Phone Image Quality (CPIQ) visual noise metric described is a core image quality attribute of the wider I3A CPIQ consumer orientated, camera image quality score. This paper describes the selection of a suitable noise metric, the adaptation of the chosen ISO 15739 visual noise protocol for the challenges posed by cell phone cameras and the mapping of the adapted protocol to subjective image quality loss using a published noise study. Via a simple study, visual noise metrics are shown to discriminate between different noise frequency shapes. The optical non-uniformities prevalent in cell phone cameras and higher noise levels pose significant challenges to the ISO 15739 visual noise protocol. The non-uniformities are addressed using a frequency based high pass filter. Secondly, the data clipping at high noise levels is avoided using a Johnson and Fairchild frequency based Luminance contrast sensitivity function (CSF). The final result is a visually based noise metric calibrated in Quality Loss Just Noticeable Differences (JND) using Aptina Imaging's subjectively calibrated image set.

Probabilistic Simple Temporal Networks (PSTN) are used to represent scheduling problems under uncertainty. In a temporal network that is Strongly Controllable (SC) there exists a concrete schedule that is robust to any uncertainty. We solve the problem of determining Chance Constrained PSTN SC as a Joint Chance Constrained optimisation problem via column generation, lifting the usual assumptions of independence and Boole's inequality typically leveraged in PSTN literature. Our approach offers on average a 10 times reduction in cost versus previous methods.

Automated planning is able to handle increasingly complex applications, but can produce unsatisfactory results when the goal and metric provided in its model does not match the actual expectation and preference of those using the tool. This can be ameliorated by including methods for explainable planning (XAIP), to reveal the reasons for the automated planner's decisions and to provide more in-depth interaction with the planner. In this paper we describe at a highlevel two recent pieces of work in XAIP. First, plan exploration through model restriction, in which contrastive questions are used to build a tree of solutions to a planning problem. Through a dialogue with the system the user better understands the underlying problem and the choices made by the automated planner. Second, strong controllability analysis of probabilistic temporal networks through solving a joint chance constrained optimisation problem. The result of the analysis is a Pareto optimal front that illustrates the trade-offs between costs and risk for a given plan. We also present a short discussion on the limitations of these methods and how they might be usefully combined.

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