122 citations · 123 across the 5 of their papers we have counts for
4 papers · 1 filter
Optimize Planning Heuristics to Rank, not to Estimate Cost-to-Goal
Leah Chrestien, Tomás Pevný, Stefan Edelkamp +1
In imitation learning for planning, parameters of heuristic functions are optimized against a set of solved problem instances. This work revisits the necessary and sufficient condi…
Heuristic Search Planning with Deep Neural Networks using Imitation, Attention and Curriculum Learning
Leah Chrestien, Tomas Pevny, Antonin Komenda +1
Learning a well-informed heuristic function for hard task planning domains is an elusive problem. Although there are known neural network architectures to represent such heuristic…
Cooperative Multi-Agent Planning: A Survey
Alejandro Torreño, Eva Onaindia, Antonín Komenda +1
Cooperative multi-agent planning (MAP) is a relatively recent research field that combines technologies, algorithms and techniques developed by the Artificial Intelligence Planning…
Decentralized Multi-agent Plan Repair in Dynamic Environments
Antonín Komenda, Peter Novák, Michal Pěchouček
Achieving joint objectives by teams of cooperative planning agents requires significant coordination and communication efforts. For a single-agent system facing a plan failure in a…