How to make dynamic decisions with circuits? @ TPM 2022

We just presented a workshop paper on our ongoing work on dynamic decision making under uncertainty with circuits at the 5th Workshop on Tractable Probabilistic Modelling. Results are still preliminary, and the code will be made available soon. Stay tuned for future developments!

A fundamental problem tackled by artificial intelligence is decision making under uncertainty in dynamic environments. For example, a robot may need to autonomously reason on where to move (decision) at each time step (dynamic) while maximising the expected utility of the performed actions, and taking into account the inherent noisiness of the world (uncertainty). Decision circuits have been shown to be a useful modelling tool in such settings, with the caveat that they do not treat time as a first-class citizen. We repair this omission by introducing dynamic decision circuits (DDCs). More specifically, we show how to obtain DDCs from dynamic decision-theoretic Bayesian networks via knowledge compilation and how to perform inference in DDCs using the algebraic model counting framework - a generalisation of weighted model counting.