← Search

Martin Schmid

6 accepted papers

2026

Massively Parallel Proof-Number Search for Impartial Games and Beyond

AAAI 2026technical

Proof-Number Search is a best-first search algorithm with many successful applications, especially in game solving. As large-scale computing clusters become increasingly accessible, parallelization is a natural way to accelerate computation. However, existing parallel versions of Proof-Number Search

Cited by 0SourcePDFScholar
2024

Learning Not to Regret

AAAI 2024technical

The literature on game-theoretic equilibrium finding predominantly focuses on single games or their repeated play. Nevertheless, numerous real-world scenarios feature playing a game sampled from a distribution of similar, but not identical games, such as playing poker with different public cards or…

2023

Rethinking Formal Models of Partially Observable Multiagent Decision Making (Extended Abstract)

IJCAI 2023poster

Multiagent decision-making in partially observable environments is usually modelled as either an extensive-form game (EFG) in game theory or a partially observable stochastic game (POSG) in multiagent reinforcement learning (MARL). One issue with the current situation is that while most practical pr…

Cited by 0SourcePDFScholar
2022

Approximate Exploitability: Learning a Best Response

IJCAI 2022poster

Researchers have shown that neural networks are vulnerable to adversarial examples and subtle environment changes. The resulting errors can look like blunders to humans, eroding trust in these agents. In prior games research, agent evaluation often focused on the in-practice game outcomes. Such eva…

Cited by 0SourcePDFScholar
2021

Solving Common-Payoff Games with Approximate Policy Iteration

AAAI 2021technical

For artificially intelligent learning systems to have widespread applicability in real-world settings, it is important that they be able to operate decentrally. Unfortunately, decentralized control is difficult---computing even an epsilon-optimal joint policy is a NEXP complete problem. Nevertheless…