  Domain-general model-based planners often derive their generality by
constructing search heuristics through the relaxation or abstraction of
symbolic world models. We illustrate how abstract interpretation can serve as a
unifying framework for these abstraction-based heuristics, extending the reach
of heuristic search to richer world models that make use of more complex
datatypes and functions (e.g. sets, geometry), and even models with uncertainty
and probabilistic effects. These heuristics can also be integrated with
learning, allowing agents to jumpstart planning in novel world models via
abstraction-derived information that is later refined by experience. This
suggests that abstract interpretation can play a key role in building universal
reasoning systems.
