In cost-optimal planning we aim to find a sequence of operators that achieve a set of goals
with minimum cost. Symbolic search with Binary Decision Diagrams (BDDs) performs …

Symbolic search has proven to be a competitive approach to cost-optimal planning, as it
compactly represents sets of states by symbolic data structures. While heuristics for symbolic …

Symbolic search with BDDs has shown remarkable performance for cost-optimal
deterministic planning by exploiting a succinct representation and exploration of state sets …

Symbolic search allows saving large amounts of memory compared to regular explicit-state
search algorithms. This is crucial in optimal settings, in which common search algorithms …

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 …

Many current state-of-the-art planners rely on forward heuristic search. The success of such
search typically depends on heuristic distance-to-the-goal estimates derived from the …

In this paper we propose refinements for optimal search with symbolic pattern databases in
deterministic statespace planning. As main memory is limited, external heuristic search is …

Abstract Symbolic PDBs and Merge-and-Shrink (M&S) are two approaches to derive
admissible heuristics for optimal planning. We present a combination of these techniques …

Factored planning decouples planning tasks into subsets (factors) of state variables. The
traditional focus is on handling complex cross-factor interactions. Departing from this, we …

Symbolic representations have attracted significant attention in optimal planning. Binary
Decision Diagrams (BDDs) form the basis for symbolic search algorithms. Closely related …