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 never simply a set of facts that are true, and actions that the agent intends to execute never operate the way they are supposed to. Thus, probabilistic planning attempts to incorporate stochastic models directly into the planning process. In this article, we briefly report on probabilistic planning through the lens of probabilistic programming: a programming paradigm that aims to ease the specification of structured probability distributions. In particular, we provide an overview of the features of two systems, HYPE and ALLEGRO, which emphasise different strengths of probabilistic programming that are particularly useful for complex modelling issues raised in probabilistic planning.
Monte-Carlo tree search is drawing great interest in the domain of planning under uncertainty, particularly when little or no domain knowledge is available. One of the central problems is the trade-off between exploration and exploitation. In this paper we present a novel Bayesian mixture modelling and inference based Thompson sampling approach to addressing this dilemma. The proposed Dirichlet-NormalGamma MCTS (DNG-MCTS) algorithm represents the uncertainty of the accumulated reward for actions in the MCTS search tree as a mixture of Normal distributions and inferences on it in Bayesian settings by choosing conjugate priors in the form of combinations of Dirichlet and NormalGamma distributions. Thompson sampling is used to select the best action at each decision node. Experimental results show that our proposed algorithm has achieved the state-of-the-art comparing with popular UCT algorithm in the context of online planning for general Markov decision processes.
State spaces in classical planning domains are usually quite large and can easily be extended to larger unmanageable sizes that do often exceed the capacity of many solvers. In this context, heuristic planning provides a powerful mean for solving rather difficult instances. However, it has been empirically observed that the performance significantly drops in the most difficult domains where the heuristic function induces large plateaus or unrecognized dead-ends. Therefore, a large effort has been invested in improving heuristic functions while only a few contributions have been made to the selection of the search algorithm. Thus, we review the choice of the search algorithm and show that some improvements are still feasible for various kinds of domains, especially the harder ones. In order to do so, we add diversity to the heuristic search in the hope that severe plateaus and/or dead-ends will be avoided or that shorter plans will be found. Also, with the aim of making the reporting of results more clear, a technique based on cumulative distribution functions is advocated.
Data scientists have hundreds of probability distributions from which to choose. Data science, whatever it may be, remains a big deal. "A data scientist is better at statistics than any software engineer," you may overhear a pundit say, at your local tech get-togethers and hackathons. The applied mathematicians have their revenge, because statistics hasn't been this talked-about since the roaring 20s. They have their own legitimizing Venn diagram of which people don't make fun. Suddenly it's you, the engineer, left out of the chat about confidence intervals instead of tutting at the analysts who have never heard of the Apache Bikeshed project for distributed comment formatting.
Although most scheduling problems are NP-hard, domain specific techniques perform well in practice but are quite expensive to construct. In adaptive problem-solving solving, domain specific knowledge is acquired automatically for a general problem solver with a flexible control architecture. In this approach, a learning system explores a space of possible heuristic methods for one well-suited to the eccentricities of the given domain and problem distribution. In this article, we discuss an application of the approach to scheduling satellite communications. Using problem distributions based on actual mission requirements, our approach identifies strategies that not only decrease the amount of CPU time required to produce schedules, but also increase the percentage of problems that are solvable within computational resource limitations.