Search Results for author: Viliam Lisý

Found 12 papers, 6 papers with code

DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker

1 code implementation6 Jan 2017 Matej Moravčík, Martin Schmid, Neil Burch, Viliam Lisý, Dustin Morrill, Nolan Bard, Trevor Davis, Kevin Waugh, Michael Johanson, Michael Bowling

Poker is the quintessential game of imperfect information, and a longstanding challenge problem in artificial intelligence.

Game of Poker

Classification with Costly Features using Deep Reinforcement Learning

1 code implementation20 Nov 2017 Jaromír Janisch, Tomáš Pevný, Viliam Lisý

We study a classification problem where each feature can be acquired for a cost and the goal is to optimize a trade-off between the expected classification error and the feature cost.

Classification Classification with Costly Features +5

Value Functions for Depth-Limited Solving in Zero-Sum Imperfect-Information Games

no code implementations31 May 2019 Vojtěch Kovařík, Dominik Seitz, Viliam Lisý, Jan Rudolf, Shuo Sun, Karel Ha

We provide a formal definition of depth-limited games together with an accessible and rigorous explanation of the underlying concepts, both of which were previously missing in imperfect-information games.

counterfactual

Rethinking Formal Models of Partially Observable Multiagent Decision Making

no code implementations26 Jun 2019 Vojtěch Kovařík, Martin Schmid, Neil Burch, Michael Bowling, Viliam Lisý

A second issue is that while EFGs have recently seen significant algorithmic progress, their classical formalization is unsuitable for efficient presentation of the underlying ideas, such as those around decomposition.

counterfactual Decision Making +1

Classification with Costly Features as a Sequential Decision-Making Problem

2 code implementations5 Sep 2019 Jaromír Janisch, Tomáš Pevný, Viliam Lisý

This work focuses on a specific classification problem, where the information about a sample is not readily available, but has to be acquired for a cost, and there is a per-sample budget.

Classification Classification with Costly Features +4

Classification with Costly Features in Hierarchical Deep Sets

1 code implementation20 Nov 2019 Jaromír Janisch, Tomáš Pevný, Viliam Lisý

In this work, we extend an existing deep reinforcement learning-based algorithm with hierarchical deep sets and hierarchical softmax, so that it can directly process this data.

Classification Classification with Costly Features +4

Symbolic Relational Deep Reinforcement Learning based on Graph Neural Networks and Autoregressive Policy Decomposition

1 code implementation25 Sep 2020 Jaromír Janisch, Tomáš Pevný, Viliam Lisý

We focus on reinforcement learning (RL) in relational problems that are naturally defined in terms of objects, their relations, and object-centric actions.

reinforcement-learning Reinforcement Learning (RL) +1

Complexity and Algorithms for Exploiting Quantal Opponents in Large Two-Player Games

no code implementations30 Sep 2020 David Milec, Jakub Černý, Viliam Lisý, Bo An

This paper aims to analyze and propose scalable algorithms for computing effective and robust strategies against a quantal opponent in normal-form and extensive-form games.

counterfactual

Revisiting Game Representations: The Hidden Costs of Efficiency in Sequential Decision-making Algorithms

no code implementations20 Dec 2021 Vojtěch Kovařík, David Milec, Michal Šustr, Dominik Seitz, Viliam Lisý

Recent advancements in algorithms for sequential decision-making under imperfect information have shown remarkable success in large games such as limit- and no-limit poker.

counterfactual Decision Making

Explaining Classifiers Trained on Raw Hierarchical Multiple-Instance Data

no code implementations4 Aug 2022 Tomáš Pevný, Viliam Lisý, Branislav Bošanský, Petr Somol, Michal Pěchouček

Learning from raw data input, thus limiting the need for feature engineering, is a component of many successful applications of machine learning methods in various domains.

Feature Engineering Multiple Instance Learning

NASimEmu: Network Attack Simulator & Emulator for Training Agents Generalizing to Novel Scenarios

2 code implementations26 May 2023 Jaromír Janisch, Tomáš Pevný, Viliam Lisý

Current frameworks for training offensive penetration testing agents with deep reinforcement learning struggle to produce agents that perform well in real-world scenarios, due to the reality gap in simulation-based frameworks and the lack of scalability in emulation-based frameworks.

Generation of Games for Opponent Model Differentiation

no code implementations28 Nov 2023 David Milec, Viliam Lisý, Christopher Kiekintveld

Attackers in the real world are predominantly human actors, and the protection methods often incorporate opponent models to improve the performance when facing humans.

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