  This paper introduces the Fusemate probabilistic logic programming system.
Fusemate's inference engine comprises a grounding component and a variable
elimination method for probabilistic inference. Fusemate differs from most
other systems by grounding the program in a bottom-up way instead of the common
top-down way. While bottom-up grounding is attractive for a number of reasons,
e.g., for dynamically creating distributions of varying support sizes, it makes
it harder to control the amount of ground clauses generated. We address this
problem by interleaving grounding with a query-guided relevance test which
prunes rules whose bodies are inconsistent with the query. We present our
method in detail and demonstrate it with examples that involve "time", such as
(hidden) Markov models. Our experiments demonstrate competitive or better
performance compared to a state-of-the art probabilistic logic programming
system, in particular for high branching problems.
