Counterexample-guided abstraction refinement (CEGAR) is a method for incrementally
computing abstractions of transition systems. We propose a CEGAR algorithm for computing …

The merge-and-shrink framework has been introduced as a general approach for defining
abstractions of large state spaces arising in domain-independent planning and related …

The merge-and-shrink framework provides a general basis for the computation of
abstraction heuristics for factored transition systems. Recent experimental and theoretical …

Symbolic bidirectional uniform-cost search is a prominent technique for cost-optimal
planning. Thus, the question whether it can be further improved by making use of heuristic …

The merge-and-shrink framework is a powerful tool to construct state space abstractions
based on factored representations. One of its core applications in classical planning is the …

In the context of heuristic search within automated planning, abstraction heuristics map the
problem into an abstract instance and use the optimal solution cost in the abstract state …

The performance of domain-independent planning systems heavily depends on how the
planning task has been modeled. This makes task reformulation an important tool to get rid …

The merge-and-shrink framework is a powerful tool for constructing state-of-the-art
admissible heuristics in classical planning. Recent work has begun generalizing the …

Classical planning is the problem of finding a sequence of deterministic actions in a state
space that lead from an initial state to a state satisfying some goal condition. The dominant …

Abstraction heuristics are a state-of-the-art technique to solve classical planning problems
optimally. A common approach is to precompute many small abstractions and combine them …