Author name cluster

David Klaska

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

2 author rows

IJCAI Conference 2022 Conference Paper

General Optimization Framework for Recurrent Reachability Objectives

David Klaska
Antonin Kucera
Vit Musil
Vojtech Rehak

We consider the mobile robot path planning problem for a class of recurrent reachability objectives. These objectives are parameterized by the expected time needed to visit one position from another, the expected square of this time, and also the frequency of moves between two neighboring locations. We design an efficient strategy synthesis algorithm for recurrent reachability objectives and demonstrate its functionality on non-trivial instances.

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UAI Conference 2022 Conference Paper

On-the-fly adaptation of patrolling strategies in changing environments

Tomás Brázdil
David Klaska
Antonín Kucera 0001
Vít Musil
Petr Novotný 0001
Vojtech Rehák

We consider the problem of efficient patrolling strategy adaptation in a changing environment where the topology of Defender’s moves and the importance of guarded targets change unpredictably. The Defender must instantly switch to a new strategy optimized for the new environment, not disrupting the ongoing patrolling task, and the new strategy must be computed promptly under all circumstances. Since strategy switching may cause unintended security risks compromising the achieved protection, our solution includes mechanisms for detecting and mitigating this problem. The efficiency of our framework is evaluated experimentally.

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UAI Conference 2021 Conference Paper

Regstar: efficient strategy synthesis for adversarial patrolling games

David Klaska
Antonín Kucera 0001
Vít Musil
Vojtech Rehák

We design a new efficient strategy synthesis method applicable to adversarial patrolling problems on graphs with arbitrary-length edges and possibly imperfect intrusion detection. The core ingredient is an efficient algorithm for computing the value and the gradient of a function assigning to every strategy its “protection” achieved. This allows for designing an efficient strategy improvement algorithm by differentiable programming and optimization techniques. Our method is the first one applicable to real-world patrolling graphs of reasonable sizes. It outperforms the state-of-the-art strategy synthesis algorithm by a margin.

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