ATVA 2025
Mon 27 - Fri 31 October 2025 Bengaluru, India

This program is tentative and subject to change.

Wed 29 Oct 2025 11:30 - 12:00 at R102 - Games Chair(s): Sayan Mukherjee

This paper presents (permissive) Quantitative Strategy Templates (QaSTels) to succinctly represent infinitely many winning strategies in two-player energy and mean-payoff games. This transfers the recently introduced concept of Permissive (qualitative) Strategy Templates (PeSTels) for $\omega$-regular games to games with quantitative objectives. We provide the theoretical and algorithmic foundations of (i) QaSTel synthesis, and (ii) their (incremental) combination with PeSTels for games with mixed quantitative and qualitative objectives. Using a prototype implementation of our synthesis algorithms, we demonstrate empirically that QaSTels extend the advantageous properties of strategy templates over single winning strategies – known from PeSTels – to games with (additional) quantitative objectives. This includes (i) the enhanced robustness of strategies due to their runtime-adaptability, and (ii) the compositionality of templates w.r.t.\ incrementally arriving objectives. We use control-inspired examples to illustrate these superior properties of QaSTels for CPS design."

This program is tentative and subject to change.

Wed 29 Oct

Displayed time zone: Chennai, Kolkata, Mumbai, New Delhi change

11:00 - 12:30
GamesATVA Papers at R102
Chair(s): Sayan Mukherjee Univ Rennes, Inria, CNRS, IRISA, France
11:00
30m
Paper
Energy Games with Weight Uncertainty
ATVA Papers
Orna Kupferman Hebrew University, Naama Shamash Halevy The Hebrew University
11:30
30m
Paper
Quantitative Strategy Templates
ATVA Papers
Ashwani Anand Max Planck Institute for Software Systems, Satya Prakash Nayak Max Planck Institute for Software Systems (MPI-SWS), Ritam Raha University of Antwerp, Antwerp, Belgium, Irmak Saglam Max Planck Institute for Software Systems (MPI-SWS), Anne-Kathrin Schmuck Max Planck Institute for Software Systems
12:00
30m
Paper
Widest Path Games and Maximality Inheritance in Bounded Value Iteration for Stochastic Games
ATVA Papers
Kittiphon Phalakarn National Institute of Informatics, Yun Chen Tsai National Institute of Informatics, Japan, Ichiro Hasuo National Institute of Informatics, Japan