Much research in artificial intelligence is concerned with the development of autonomous
agents that can interact effectively with other agents. An important aspect of such agents is …

The first part of a two-part series of papers provides a survey on recent advances in Deep
Reinforcement Learning (DRL) applications for solving partially observable Markov decision …

Poker is an interesting test-bed for artificial intelligence research. It is a game of imperfect
information, where multiple competing agents must deal with probabilistic knowledge, risk …

The game of poker has been identified as a beneficial domain for current AI research
because of the properties it possesses such as the need to deal with hidden information and …

We develop an algorithm for opponent modeling in large extensive-form games of imperfect
information. It works by observing the opponent's action frequencies and building an …

The rising tide of threats, from financial cybercrime to asymmetric military conflicts, demands
greater sophistication in tools and techniques of law enforcement, commercial and domestic …

The game of poker offers a clean well-defined domain in which to investigate some truly
fundamental issues in computing science, such as how to handle deliberate misinformation …

We consider the problem of learning an effective strategy online in a hidden information
game against an opponent with a changing strategy. We want to model and exploit the …

In this paper we apply a Monte-Carlo Tree Search implementation that is boosted with
domain knowledge to the game of poker. More specifically, we integrate an opponent model …

This article discusses two contributions to decision-making in complex partially observable
stochastic games. First, we apply two state-of-the-art search techniques that use Monte …