Automated Event Recognition for Football Commentary Generation

The enjoyment of many games can be enhanced by in-game commentaries. In this paper, the authors focus on the automatic generation of commentaries for football games, using Championship Manager as a case study. The basis of this approach is a real-time mapping of game states to commentary concepts, such as "dangerous situation for team A". While in some cases it is feasible to provide such a mapping by hand-coding, in some cases it is not straight-forward because the meaning of the concepts cannot be easily formalized. In these cases, the authors propose to use inductive learning techniques that learn such a mapping from annotated game traces.

[1]  Thomas Rist,et al.  Rocco: A RoboCup Soccer Commentator System , 1998, RoboCup.

[2]  Damian Stolarski.  Conceptual Representation for a Soccer Commentary Generator , 2005, RoboCup.

[3]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[4]  David Gibson,et al.  International Journal of Gaming and Computer-Mediated Simulations , 2012 .

[5]  Hiroaki Kitano,et al.  RoboCup: A Challenge Problem for AI , 1997, AI Mag..

[6]  Kôiti Hasida,et al.  MIKE: an automatic commentary system for soccer , 1998, Proceedings International Conference on Multi Agent Systems (Cat. No.98EX160).

[7]  Kim Binsted,et al.  Character Design for Soccer Commentary , 1998, RoboCup.

[8]  Ian Witten,et al.  Data Mining , 2000 .

[9]  Thomas Rist,et al.  On the Simultaneous Interpretation of Real World Image Sequences and their Natural Language Description: The System Soccer , 1988, ECAI.

[10]  Stephen A. McGuire,et al.  Introductory Statistics , 2007, Technometrics.

[11]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[12]  Liva Ralaivola,et al.  Efficient learning of Naive Bayes classifiers under class-conditional classification noise , 2006, ICML.

[13]  Gerd Herzog,et al.  VIsual TRAnslator: Linking perceptions and natural language descriptions , 1994, Artificial Intelligence Review.

[14]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[15]  Andrea Corradini,et al.  Handbook of Research on Improving Learning and Motivation through Educational Games , 2011 .

[16]  Dennis Beck,et al.  Influence of Avatar Choice on Teacher Expectations and Perceptions of Student Success , 2012, Int. J. Gaming Comput. Mediat. Simulations.

[17]  Richard J. Qian,et al.  Detecting semantic events in soccer games: towards a complete solution , 2001, IEEE International Conference on Multimedia and Expo, 2001. ICME 2001..

[18]  Mohammad Reza Kangavari,et al.  Design and Implementation of Live Commentary System in Soccer Simulation Environment , 2004, RoboCup.

[19]  Nicolas Szilas,et al.  A Theoretical Background for Educational Video Games: Games, Signs, Knowledge , 2011 .

[20]  Thomas Rist,et al.  Multimedia Presentation of Interpreted Visual Data , 2007 .

[21]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[22]  Thomas H. Wonnacott,et al.  Introductory Statistics , 2007, Technometrics.

[23]  M. Grimshaw,et al.  Game Sound Technology and Player Interaction: Concepts and Developments , 2010 .

[24]  Belur V. Dasarathy,et al.  Nearest neighbor (NN) norms: NN pattern classification techniques , 1991 .

[25]  Paul McKevitt,et al.  Integration of Natural Language and Vision Processing , 1996, Springer Netherlands.

[26]  Gudula Retz-Schmidt,et al.  Recognizing intentions, interactions, and causes of plan failures , 1991, User Modeling and User-Adapted Interaction.

[27]  Kôiti Hasida,et al.  Reactive Content Selection in the Generation of Real-time Soccer Commentary , 1998, COLING-ACL.