Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work.

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A Survey of Monte Carlo Tree Search Methods

Human-Robot Interaction (HRI) has recently received considerable attention in the academic community, in labs, in technology companies, and through the media. Because of this attention, it is desirable to present a survey of HRI to serve as a tutorial to people outside the field and to promote discussion of a unified vision of HRI within the field. The goal of this review is to present a unified treatment of HRI-related problems, to identify key themes, and discuss challenge problems that are likely to shape the field in the near future. Although the review follows a survey structure, the goal of presenting a coherent "story" of HRI means that there are necessarily some well-written, intriguing, and influential papers that are not referenced. Instead of trying to survey every paper, we describe the HRI story from multiple perspectives with an eye toward identifying themes that cross applications. The survey attempts to include papers that represent a fair cross section of the universities, government efforts, industry labs, and countries that contribute to HRI, and a cross section of the disciplines that contribute to the field, such as human, factors, robotics, cognitive psychology, and design.

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Human-Robot Interaction: A Survey

Adopt: asynchronous distributed constraint optimization with quality guarantees

The World Trade Center (WTC) rescue response provided an unfortunate opportunity to study the human-robot interactions (HRI) during a real unstaged rescue for the first time. A post-hoc analysis was performed on the data collected during the response, which resulted in 17 findings on the impact of the environment and conditions on the HRI: the skills displayed and needed by robots and humans, the details of the Urban Search and Rescue (USAR) task, the social informatics in the USAR domain, and what information is communicated at what time. The results of this work impact the field of robotics by providing a case study for HRI in USAR drawn from an unstaged USAR effort. Eleven recommendations are made based on the findings that impact the robotics, computer science, engineering, psychology, and rescue fields. These recommendations call for group organization and user confidence studies, more research into perceptual and assistive interfaces, and formal models of the state of the robot, state of the world, and information as to what has been observed.

Human-robot interactions during the robot-assisted urban search and rescue response at the World Trade Center

Disaster rescue is one of the most serious social issue which involves very large numbers of heterogeneous agents in the hostile environment. RoboCup-Rescue intends to promote research and development in this socially significant domain by creating a standard simulator and forum for researchers and practitioners. While the rescue domain intuitively appealing as large scale multi-agent domains, it has not yet given through analysis on its domain characteristics. In this paper, we present detailed analysis on the task domain and elucidate characteristics necessary for multi-agent systems for this domain.

RoboCup Rescue: search and rescue in large-scale disasters as a domain for autonomous agents research

This paper introduces the RoboCup-Rescue Simulation Project, a contribution to the disaster mitigation, search and rescue problem. A comprehensive urban disaster simulator is constructed on distributed computers. Heterogeneous intelligent agents such as fire fighters, victims and volunteers conduct search and rescue activities in this virtual disaster world. A real world interface integrates various sensor systems and controllers of infrastructures in the real cities with the virtual world. Real-time simulation is synchronized with actual disasters, computing complex relationship between various damage factors and agent behaviors. A mission-critical man-machine interface provides portability and robustness of disaster mitigation centers, and augmented-reality interfaces for rescue parties in real disasters. It also provides a virtual reality training function for the public. This diverse spectrum of RoboCup-Rescue contributes to the creation of the safer social system.

The RoboCup-Rescue project: a robotic approach to the disaster mitigation problem

This paper describes MIKE, an automatic commentary system for the game of soccer. Since soccer is played by teams, describing the course of a game calls for reasoning about multi-agent interactions. Also, events may occur at any point of the field at any time, making it difficult to fix viewpoints. MIKE interprets this domain with six soccer analysis modules that run concurrently within a role-sharing framework. We describe these analysis modules and also discuss how to control the interaction between them so that an explanation of a game emerges reactively from the system. We present and evaluate examples of the match commentaries produced by MIKE in English, Japanese and French.

MIKE: an automatic commentary system for soccer

In this paper we propose a Multi-Agent version of UCT Monte Carlo Go. We use the emergent behavior of a great number of simple agents to increase the quality of the Monte Carlo simulations, increasing the strength of the artificial player as a whole. Instead of one agent playing against itself, different agents play in the simulation phase of the algorithm, leading to a better exploration of the search space. We could significantly overcome Fuego, a top Computer Go software. Emergent behavior seems to be the next step of Computer Go development.

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Multi-agent Monte Carlo Go

RoboCup is an increasingly successful attempt to promote the full integration of AI and robotics research. The most prominent feature of RoboCup is that it provides the researchers with the opportunity to demonstrate their research results as a form of competition in a dynamically changing hostile environment, defined as the international standard game definition, in which the gamut of intelligent robotics research issues are naturally involved. In this article, first we show an overview of the RoboCup initiative. Currently, we have four kinds of leagues: the real small, middle, and four legged robots, and the simulation league. While the practical issues have been mainly attacked in the real robot leagues, the more strategic issues in multi-agent environments have been focused in the simulation league, such as teamwork among agents, agent modeling, and multi-agent learning which are argued in the rest of the paper. Finally, the future perspectives are given.

H. Matsubara

Papers

RoboCup Rescue: search and rescue in large-scale disasters as a domain for autonomous agents research

The RoboCup-Rescue project: a robotic approach to the disaster mitigation problem

MIKE: an automatic commentary system for soccer

Multi-agent Monte Carlo Go

RoboCup as a strategic initiative to advance technologies