Depth-Bounded Epistemic Planning
This addresses computational efficiency in epistemic planning for AI systems, though it is incremental as it builds on dynamic epistemic logic with a bounded-depth twist.
The authors tackled the problem of epistemic planning by introducing a depth-bounded reasoning approach that limits agents to reasoning about knowledge up to a specified modal depth, resulting in an algorithm that runs in (b+1)-EXPTIME and shows effective performance improvements over the EFP 2.0 planner in benchmarks.
We propose a novel algorithm for epistemic planning based on dynamic epistemic logic (DEL). The novelty is that we limit the depth of reasoning of the planning agent to an upper bound b, meaning that the planning agent can only reason about higher-order knowledge to at most (modal) depth b. We then compute a plan requiring the lowest reasoning depth by iteratively incrementing the value of b. The algorithm relies at its core on a new type of "canonical" b-bisimulation contraction that guarantees unique minimal models by construction. This yields smaller states wrt. standard bisimulation contractions, and enables to efficiently check for visited states. We show soundness and completeness of our planning algorithm, under suitable bounds on reasoning depth, and that, for a bound b, it runs in (b+1)-EXPTIME. We implement the algorithm in a novel epistemic planner, DAEDALUS, and compare it to the EFP 2.0 planner on several benchmarks from the literature, showing effective performance improvements.