In this paper, we integrate, implement, and validate formation flying algorithms for large number of agents using probabilistic swarm guidance with inhomogeneous Markov chains and model predictive control with sequential convex programming. Using an inhomogeneous Markov chain, each agent determines its target position during each time step in a statistically independent manner while the swarm c...

Large real-world Probabilistic Temporal Planning (PTP) is a very challenging research field. A common approach is to model such problems as Markov Decision Problems (MDP) and use dynamic programming techniques. Yet, two major difficulties arise: 1dynamic programming does not scale with the number of tasks, and 2the probabilistic model may be uncertain, leading to the choice of unsafe policies. ...

We present a probability-sensitive confidentiality specification – a form of probabilistic noninterference – for a small multi-threaded programming language with dynamic thread creation. Probabilistic covert channels arise from a scheduler which is probabilistic. Since scheduling policy is typically outside the language specification for multithreaded languages, we describe how to generalise th...

Principal component analysis (PCA) is one of the most popular tools in multivariate exploratory data analysis. Its probabilistic version (PPCA) based on maximum likelihood procedure provides a manner to implement dimension reduction. Recently, bilinear PPCA (BPPCA) model, which assumes that noise terms follow matrix variate Gaussian distributions, has been introduced directly deal with two-dime...

Stochastic approximation methods for variational inference have recently gained popularity in the probabilistic programming community since these are amenable to automation and allow online, scalable, universal approximate Bayesian inference. Unfortunately, common Probabilistic Programming Languages (PPLs) with stochastic engines lack efficiency of message passing-based algorithms deterministic...

Automated planning is a major topic of research in artificial intelligence, and enjoys a long and distinguished history. The classical paradigm assumes a distinguished initial state, comprised of a set of facts, and is defined over a set of actions which change that state in one way or another. Planning in many real-world settings, however, is much more involved: an agent’s knowledge is almost ...