Cooperative multi-agent planning (MAP) is a relatively recent research field that combines
technologies, algorithms, and techniques developed by the Artificial Intelligence Planning …

Current domain-independent, classical planners require symbolic models of the problem
domain and instance as input, resulting in a knowledge acquisition bottleneck. Meanwhile …

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

We introduce an efficient method for translating planning tasks specified in the standard
PDDL formalism into a concise grounded representation that uses finite-domain state …

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 …

When solving instances of problem domains that feature a large branching factor, A* may
generate a large number of nodes whose cost is greater than the cost of the optimal solution …

Cost-Optimal Planning with Landmarks Page 1 Introduction Admissible Landmark Heuristics
Admissible Heuristics with Action Landmarks Evaluations Discussion . . Cost-Optimal Planning …

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

This article surveys and gives historical accounts to the algorithmic essentials of directed
model checking, a promising bug-hunting technique to mitigate the state explosion problem …