Autonomous AI systems need complex computational techniques for planning and
performing actions. Planning and acting require significant deliberation because an …

Current heuristic estimators for classical domain-independent planning are usually based
on one of four ideas: delete relaxations, critical paths, abstractions, and, most recently …

This paper investigates symbolic heuristic search with BDDs for solving domain-
independent action planning problems cost-optimally. By distributing the impact of operators …

Many areas of computer science require answering questions about reachability in
compactly described discrete transition systems. Answering such questions effectively …

Heuristic search is a leading approach to domain-independent planning. For cost-optimal
planning, however, existing admissible heuristics are generally too weak to effectively guide …

Operator cost partitioning is a well-known technique to make admissible heuristics additive
by distributing the operator costs among individual heuristics. Planning tasks are usually …

This paper studies novel subgoaling relaxations for automated planning with propositional
and numeric state variables. Subgoaling relaxations address one source of complexity of the …

People often give instructions whose meaning is ambiguous without further context,
expecting that their actions or goals will disambiguate their intentions. How can we build …

Quantitative formal models capture probabilistic behaviour, real-time aspects, or general
continuous dynamics. A number of tools support their automatic analysis with respect to …

We empirically examine several ways of exploiting the information of multiple heuristics in a
satisficing best-first search algorithm, comparing their performance in terms of coverage …