As classical planning is known to be computationally hard, no single planner is expected to
work well across many planning domains. One solution to this problem is to use online …

Automated planning is one of the foundational areas of AI. Since no single planner can work
well for all tasks and domains, portfolio-based techniques have become increasingly …

We study multiagent epistemic planning with a simple epistemic logic whose language is a
restriction of that of standard epistemic logic. Its formulas are boolean combinations of …

We present a new algorithm called A*+ BFHS for solving problems with unit-cost operators
where A* and IDA* fail due to memory limitations and/or the existence of many distinct paths …

Pattern databases (PDBs) are memory-based abstraction heuristics that are constructed
prior to the planning process which, if expressed symbolically, yield a very efficient …

Breadth-first heuristic search (BFHS) is a classic algorithm for optimally solving heuristic
search and planning problems. BFHS is slower than A* but requires less memory. However …

Abstract Fast Downward Merge-and-Shrink uses the optimized, efficient implementation of
the merge-and-shrink framework available in the Fast Downward planning system. We …

The difficulty of task planning for robotic agents arises from the stochastic nature of their
environment and the high cost of a failure during execution meaning frequent replanning is …

Automated planning is one of the foundational areas of AI. Since a single planner unlikely
works well for all tasks and domains, portfolio-based techniques become increasingly …

Heuristic search and planning model real-world problems as graphs, where each node
represents a unique state or configuration of the problem, and each edge represents an …