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 …

Plan recognition, activity recognition, and intent recognition together combine and unify
techniques from user modeling, machine vision, intelligent user interfaces, human/computer …

Whenever people move through their environments they do not move randomly. Instead,
they usually follow specific trajectories or motion patterns corresponding to their intentions …

The goal of zero-shot human-AI coordination is to develop an agent that can collaborate with
humans without relying on human data. Prevailing two-stage population-based methods …

Opponent modeling is the ability to use prior knowledge and observations in order to predict
the behavior of an opponent. This survey presents a comprehensive overview of existing …

Designing coordinated robot behaviors in uncertain, dynamic, real-time, adversarial
environments, such as in robot soccer, is very challenging. In this work we present a case …

Real-world AI systems have been recently deployed which can automatically analyze the
plan and tactics of tennis players. As the game-state is updated regularly at short intervals …

Agents in dynamic environments have to deal with world representations that change over
time. In order to allow agents to act autonomously and to make their decisions on a solid …

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 …

Although in theory opponent modeling can be useful in any adversarial domain, in practice it
is both difficult to do accurately and to use effectively to improve game play. In this paper, we …