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 …

As autonomous agents proliferate in the real world, both in software and robotic settings,
they will increasingly need to band together for cooperative activities with previously …

Reinforcement learning is the area of machine learning concerned with learning which
actions to execute in an unknown environment in order to maximize cumulative reward. As …

We focus on a robotic domain in which a human acts both as a teacher and a collaborator to
a mobile robot. First, we present an approach that allows a robot to learn task …

The CMUnited-99 simulator team became the 1999 RoboCup simulator league champion
by winning all 8 of its games, outscoring opponents by a combined score of 110-0 …

Abstract Soccer Simulation 2D League is one of the major leagues of RoboCup
competitions. In a Soccer Simulation 2D (SS2D) game, two teams of 11 players and one …

This paper reviews the proposed opponent modeling algorithms within the soccer simulation
domain. RoboCup soccer simulation 2D is a rich multi agent environment where opponent …

This paper draws correlations between several challenges and opportunities within the area
of team sports analytics and key research areas within multiagent systems (MAS). We …

We describe Gliders2d, a base code release for Gliders, a soccer simulation team which
won the RoboCup Soccer 2D Simulation League in 2016. We trace six evolutionary steps …

In soccer, like in other collective sports, although players try to hide their strategy, it is always
possible, with a careful analysis, to detect it and to construct a model that characterizes their …