Quality-diversity (QD) algorithms search for a set of good solutions which cover a space as
defined by behavior metrics. This simultaneous focus on quality and diversity with explicit …

Recent superhuman results in games have largely been achieved in a variety of zero-sum
settings, such as Go and Poker, in which agents need to compete against others. However …

Training a robust cooperative agent requires diverse partner agents. However, obtaining
those agents is difficult. Previous works aim to learn diverse behaviors by changing the state …

As a fundamental problem for Artificial Intelligence, multi-agent system (MAS) is making
rapid progress, mainly driven by multi-agent reinforcement learning (MARL) techniques …

Ad hoc teamwork requires an agent to cooperate with unknown teammates without prior
coordination. Many works propose to abstract teammate instances into high-level …

A Software Product Line (SPL) is a set of software products that are built from a variability
model. Real-world SPLs typically involve a vast number of valid products, making it …

Deep reinforcement learning has achieved superhuman performance in zero-sum games
such as Go and Poker in recent years. In the real world, however, many scenarios are non …

Designing agents that are able to achieve different play-styles while maintaining a
competitive level of play is a difficult task, especially for games for which the research …

Hanabi is a cooperative game that brings the problem of modeling other players to the
forefront. In this game, coordinated groups of players can leverage preestablished …

In this article, we present an approach to generate a team of general video game playing
agents with differentiated behaviors that can ultimately assist in the game development …