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Proceedings ArticleDOI

MIKE: an automatic commentary system for soccer

03 Jul 1998-pp 285-292
TL;DR: This paper describes MIKE, an automatic commentary system for the game of soccer that interprets this domain with six soccer analysis modules that run concurrently within a role-sharing framework and discusses how to control the interaction between them.
Abstract: 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.
Citations
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Book
10 Mar 2014
TL;DR: Plan recognition, activity recognition, and intent recognition together combine and unify techniques from user modeling, machine vision, intelligent user interfaces, human/computer interaction, autonomous and multi-agent systems, natural language understanding, and machine learning.
Abstract: Plan recognition, activity recognition, and intent recognition together combine and unify techniques from user modeling, machine vision, intelligent user interfaces, human/computer interaction, autonomous and multi-agent systems, natural language understanding, and machine learning. Plan, Activity, and Intent Recognition explains the crucial role of these techniques in a wide variety of applications including: personal agent assistants, computer and network security, opponent modeling in games and simulation systems, coordination in robots and software agents, web e-commerce and collaborative filtering, dialog modeling, video surveillance, smart homes In this book, follow the history of this research area and witness exciting new developments in the field made possible by improved sensors, increased computational power, and new application areas. Combines basic theory on algorithms for plan/activity recognition along with results from recent workshops and seminars Explains how to interpret and recognize plans and activities from sensor data Provides valuable background knowledge and assembles key concepts into one guide for researchers or students studying these disciplines

111 citations

Journal ArticleDOI
TL;DR: Three systems that generate real-time natural language commentary on the RoboCup simulation league are presented, and their similarities, differences, and directions for the future discussed.
Abstract: � Three systems that generate real-time natural language commentary on the RoboCup simulation league are presented, and their similarities, differences, and directions for the future discussed. Although they emphasize different aspects of the commentary problem, all three systems take simulator data as input and generate appropriate, expressive, spoken commentary in real time.

63 citations


Cites background from "MIKE: an automatic commentary syste..."

  • ...One of our contributions is to demonstrate that a collection of concurrently running analysis modules can be used to follow and interpret the actions of multiple agents (Tanaka-Ishii et al. 1998)....

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Journal ArticleDOI
TL;DR: The current Soccer Server and the champion CMUnited soccer-playing agents are described and the ongoing development of FUSS is described, a new, flexible simulation environment for multiagent research in a variety of multiagent domains.
Abstract: The RoboCup Soccer Server and associated client code is a growing body of software infrastructure that enables a wide variety of multiagent systems research. The Soccer Server is a multiagent environment that supports 22 independent agents interacting in a complex, real-time environment. AI researchers have been using the Soccer Server to pursue research in a wide variety of areas, including real-time multiagent planning, real-time communication methods, collaborative sensing, and multiagent learning. This article describes the current Soccer Server and the champion CMUnited soccer-playing agents, both of which are publically available and used by a growing research community. It also describes the ongoing development of FUSS, a new, flexible simulation environment for multiagent research in a variety of multiagent domains.

62 citations


Cites background from "MIKE: an automatic commentary syste..."

  • ...In addition, some groups are building commentary systems that describe matches dynamically in natural language (Andr e et al., 1998; Tanaka-Ishii et al., 1998)....

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Book ChapterDOI
01 Jan 1999
TL;DR: First results concerning the realization of a fully automated RoboCup commentator will be presented and step-by-step even more advanced capabilities are to be added with future versions of the initial Rocco prototype.
Abstract: With the attempt to enable robots to play soccer games, the RoboCup challenge poses a demanding standard problem for AI and intelligent robotics research. The rich domain of robot soccer, however, provides a further option for the investigation of a second class of intelligent systems which are capable of understanding and describing complex time-varying scenes. Such automatic commentator systems offer an interesting research perspective for additional integration of natural language and intelligent multimedia technologies. In this paper, first results concerning the realization of a fully automated RoboCup commentator will be presented. The system called Rocco is currently able to generate TV-style live reports for arbitrary matches of the RoboCup simulator league. Based upon our generic approach towards multimedia reporting systems, step-by-step even more advanced capabilities are to be added with future versions of the initial Rocco prototype.

52 citations


Cites methods from "MIKE: an automatic commentary syste..."

  • ...Similar to the initial Rocco version, the systemMike (Multi-agent Interactions Knowledgeably Explained) described in [12] is designed to produce simultaneous spoken commentary for matches from the RoboCup simulator league....

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Proceedings ArticleDOI
10 Aug 1998
TL;DR: It is described how a principle of maximizing the total gain of importance scores during a game can be used to incorporate content selection into the surface generation module, thus accounting for issues such as interruption and abbreviation.
Abstract: MIKE is an automatic commentary system that generates a commentary of a simulated soccer game in English, French, or Japanese.One of the major technical challenges involved in live sports commentary is the reactive selection of content to describe complex, rapidly unfolding situation. To address this challenge, MIKE employs importance scores that intuitively capture the amount of information communicated to the audience. We describe how a principle of maximizing the total gain of importance scores during a game can be used to incorporate content selection into the surface generation module, thus accounting for issues such as interruption and abbreviation.Sample commentaries produced by MIKE are presented and used to evaluate different methods for content selection and generation in terms of efficiency of communication.

49 citations

References
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Journal ArticleDOI
TL;DR: The characteristics of the speech problem in particular, the special kinds of problem-solving uncertainty in that domain, the structure of the Hearsay-II system developed to cope with that uncertainty, and the relationship between Hearsey-II's structure and those of other speech-understanding systems are discussed.
Abstract: The Hearsay-II system, developed during the DARPA-sponsored five-year speech-understanding research program, represents both a specific solution to the speech-understanding problem and a general framework for coordinating independent processes to achieve cooperative problem-solving behavior. As a computational problem, speech understanding reflects a large number of intrinsically interesting issues. Spoken sounds are achieved by a long chain of successive transformations, from intentions, through semantic and syntactic structuring, to the eventually resulting audible acoustic waves. As a consequence, interpreting speech means effectively inverting these transformations to recover the speaker's intention from the sound. At each step in the interpretive process, ambiguity and uncertainty arise. The Hearsay-II problem-solving framework reconstructs an intention from hypothetical interpretations formulated at various levels of abstraction. In addition, it allocates limited processing resources first to the most promising incremental actions. The final configuration of the Hearsay-II system comprises problem-solving components to generate and evaluate speech hypotheses, and a focus-of-control mechanism to identify potential actions of greatest value. Many of these specific procedures reveal novel approaches to speech problems. Most important, the system successfully integrates and coordinates all of these independent activities to resolve uncertainty and control combinatorics. Several adaptations of the Hearsay-II framework have already been undertaken in other problem domains, and it is anticipated that this trend will continue; many future systems necessarily will integrate diverse sources of knowledge to solve complex problems cooperatively. Discussed in this paper are the characteristics of the speech problem in particular, the special kinds of problem-solving uncertainty in that domain, the structure of the Hearsay-II system developed to cope with that uncertainty, and the relationship between Hearsay-II's structure and those of other speech-understanding systems. The paper is intended for the general computer science audience and presupposes no speech or artificial intelligence background.

1,422 citations

Book ChapterDOI
01 Jan 1998
TL;DR: RoboCup Challenge as mentioned in this paper is a set of challenges for intelligent agent researchers using a friendly competition in a dynamic, real-time, multi-agent domain, which includes learning of individual agents and teams, team planning and execution in service of teamwork, and opponent modeling.
Abstract: RoboCup Challenge offers a set of challenges for intelligent agent researchers using a friendly competition in a dynamic, real-time, multi-agent domain. While RoboCup in general envisions longer range challenges over the next few decades, RoboCup Challenge presents three specific challenges for the next two years: (i) learning of individual agents and teams; (ii) multi-agent team planning and plan-execution in service of teamwork; and (iii) opponent modeling. RoboCup Challenge provides a novel opportunity for machine learning, planning, and multi-agent researchers — it not only supplies a concrete domain to evalute their techniques, but also challenges researchers to evolve these techniques to face key constraints fundamental to this domain: real-time, uncertainty, and teamwork.

315 citations

Journal ArticleDOI
TL;DR: The potential of Soccer Server is demonstrated by reporting an experiment that uses the system to compare the performance of a neural network architecture and a decision tree algorithm at learning the selection of soccer play plans.
Abstract: This article describes Soccer Server, a simulator of the game of soccer designed as a benchmark for evaluating multiagent systems and cooperative algorithms. In real life, successful soccer teams require many qualities, such as basic ball control skills, the ability to carry out strategies, and teamwork. We believe that simulating such behaviors is a significant challenge for computer science, artificial intelligence, and robotics technologies. It is to promote the development of such technologies, and to help define a new standard problem for research, that we have developed Soccer Server. We demonstrate the potential of Soccer Server by reporting an experiment that uses the system to compare the performance of a neural network architecture and a decision tree algorithm at learning the selection of soccer play plans. Other researchers using Soccer Server to investigate the nature of cooperative behavior in a multiagent environment will have the chance to assess their progress at RoboCup-97, an internatio...

248 citations

Proceedings Article
23 Aug 1997
TL;DR: This paper presents three specific challenges for the next two years of RoboCup Challenge: learning of individual agents and teams; multi-agent team planning and plan-execution in service of teamwork; and opponent modeling.
Abstract: RoboCup Challenge offers a set of challenges for intelligent agent researchers using a friendly competition in a dynamic, real-time, multiagent domain. While RoboCup in general envisions longer range challenges over the next few decades, RoboCup Challenge presents three specific challenges for the next two years: (i) learning of individual agents and teams; (ii) multi-agent team planning and plan-execution in service of teamwork; and (iii) opponent modeling. RoboCup Challenge provides a novel opportunity for machine learning, planning, and multi-agent researchers it not only supplies a concrete domain to evaluate their techniques, but also challenges researchers to evolve these techniques to face key constraints fundamental to this domain: real-time, uncertainty, and teamwork.

100 citations

Proceedings ArticleDOI
16 Sep 1996
TL;DR: A motion analysis system of soccer games is presented to evaluate the teamwork quantitatively based on the movement of all the players in a game and two new features; minimum moving time pattern andinant region are proposed.
Abstract: We present a motion analysis system of soccer games. The purpose of this system is to evaluate the teamwork quantitatively based on the movement of all the players in a game. Space management and cooperative movement by the players are two major factors for teamwork evaluation. To quantify them from motion images, we propose two new features; "minimum moving time pattern" and "dominant region". From experiments using actual game scenes, it is suggested that the proposed system can be a new tool for supporting to evaluate the teamwork.

95 citations