Konstantin M. Seiler

2 accepted papers

2024

Multi-Horizon Multi-Agent Planning Using Decentralised Monte Carlo Tree Search

Konstantin M. Seiler, Felix H. Kong, Robert Fitch

RA-L 2024

We propose multi-horizon Monte Carlo tree search (MH-MCTS), the first framework for integrated hierarchical multi-horizon, multi-agent planning based on Monte Carlo tree search (MCTS). The method employs multiple simultaneous MCTS optimisations for each planning level within each agent, which are de

Cited by 5 Source ↗Scholar ↗

2015

An online and approximate solver for POMDPs with continuous action space

Konstantin M. Seiler, Hanna Kurniawati, Surya P. N. Singh

ICRA 2015poster

For agile, accurate autonomous robotics, it is desirable to plan motion in the presence of uncertainty. The Partially Observable Markov Decision Process (POMDP) provides a principled framework for this. Despite the tremendous advances of POMDP-based planning, most can only solve problems with a smal…

Cited by 98 Source ↗Scholar ↗