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• Albert-Ludwigs-Universität Freiburg
• Technische Fakultät
• Institut für Informatik

Tim Schulte – Publikationen

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2020

• Patrick Caspari, Robert Mattmüller und Tim Schulte.
  A Framework to Prove Strong Privacy in Multi-Agent Planning.
  In Proceedings of the 6th Workshop on Distributed and Multi-Agent Planning (DMAP 2020), S. 32-39. 2020.
  (PDF)
  

2019

• Tim Schulte und Bernhard Nebel.
  Trial-based Heuristic Tree-search for Distributed Multi-Agent Planning.
  In Proceedings of the Twelfth Annual Symposium on Combinatorial Search (SoCS 2019) (SoCS 2019). 2019.
  (Abstract einblenden) (Abstract ausblenden) (PDF)
  

  We present a novel search scheme for privacy-preserving multi-agent planning, inspired by UCT search. We compare the presented approach to classical multi-agent forward search and evaluate it based on benchmarks from the CoDMAP competition.

2018

• Tim Schulte.
  Stubborn Sets Pruning for Privacy Preserving Planning.
  In Proceedings of the Eleventh Annual Symposium on Combinatorial Search (SoCS 2018) (SoCS 2018). 2018.
  (Abstract einblenden) (Abstract ausblenden) (PDF)
  

  We adapt a partial-order reduction technique based on stubborn sets to the setting of privacy-preserving multi-agent planning. We prove that the presented approach preserves optimality and show experimentally that it can significantly improve search performance on some domains.

• Tim Schulte.
  Stubborn Sets Pruning for Privacy Preserving Planning.
  In Proceedings of the 5th Workshop on Distributed and Multi-Agent Planning (DMAP 2018) (DMAP 2018). 2018.
  (Abstract einblenden) (Abstract ausblenden) (PDF)
  

  We adapt a partial-order reduction technique based on stubborn sets to the setting of privacy-preserving multi-agent planning. We prove that the presented approach preserves optimality and show experimentally that it can significantly improve search performance on some domains.

2016

• Tim Schulte und Bernhard Nebel.
  Trial-based Heuristic Tree-search for Distributed Multi-Agent Planning.
  In Proceedings of the Ninth Annual Symposium on Combinatorial Search (SoCS 2016) (SoCS 2016). 2016.
  (Abstract einblenden) (Abstract ausblenden) (PDF)
  

  We present a novel search scheme for privacy-preserving multi-agent planning, inspired by UCT search. We compare the presented approach to classical multi-agent forward search and evaluate it based on benchmarks from the CoDMAP competition.

• Tim Schulte und Bernhard Nebel.
  Trial-based Heuristic Tree-search for Distributed Multi-Agent Planning.
  In Proceedings of the 4th Workshop on Distributed and Multi-Agent Planning (DMAP 2016) (DMAP 2016). 2016.
  (Abstract einblenden) (Abstract ausblenden) (PDF)
  

  We present a novel search scheme for privacy-preserving multi-agent planning. Inspired by UCT search, the scheme is based on growing an asynchronous search tree by running repeated trials through the tree. We describe key differences to classical multi-agent forward search, discuss theoretical properties of the presented approach, and evaluate it based on benchmarks from the CoDMAP competition.

2014

• Tim Schulte und Thomas Keller.
  Balancing Exploration and Exploitation in Classical Planning.
  In Proceedings of the Seventh Annual Symposium on Combinatorial Search (SoCS 2014) (SoCS 2014). 2014.
  (Abstract einblenden) (Abstract ausblenden) (PDF) (BIB)
  

  Successful heuristic search planners for satisficing planning like FF or LAMA are usually based on one or more best first search techniques. Recent research has led to planners like Arvand, Roamer or Probe, where novel techniques like Monte-Carlo Random Walks extend the traditional exploitation-focused best first search by an exploration component. The UCT algorithm balances these contradictory incentives and has shown tremendous success in related areas of sequential decision making but has never been applied to classical planning yet. We make up for this shortcoming by applying the Trial-based Heuristic Tree Search framework to classical planning. We show how to model the best first search techniques Weighted A* and Greedy Best First Search with only three ingredients: action selection, initialization and backup function. Then we use THTS to derive four versions of the UCT algorithm that differ in the used backup functions. The experimental evaluation shows that our main algorithm, GreedyUCT*, outperforms all other algorithms presented in this paper, both in terms of coverage and quality.

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