Probabilistic Planning by Probabilistic Programming

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. Among other things, with these systems, one can instantiate planning problems with growing and shrinking state spaces, discrete and continuous probability distributions, and nonunique prior distributions in a first-order setting.