Abstract Interpretation for Generalized Heuristic Search in Model-Based Planning
This work addresses the challenge of building universal reasoning systems for AI planning, though it appears incremental as it extends existing abstraction-based methods.
The paper tackles the problem of extending heuristic search in model-based planning to richer world models with complex datatypes and uncertainty by proposing abstract interpretation as a unifying framework, resulting in enhanced generality and integration with learning for jumpstarting planning in novel environments.
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.