Chapter

RoboCup-99: Robot Soccer World Cup III

Volume 1856 of the series Lecture Notes in Computer Science pp 85-102

Date:

Automated Assistants to Aid Humans in Understanding Team Behaviors

  • Taylor RainesAffiliated withInformation Sciences Institute and Computer Science Department, University of Southern California
  • , Milind TambeAffiliated withInformation Sciences Institute and Computer Science Department, University of Southern California
  • , Stacy MarsellaAffiliated withInformation Sciences Institute and Computer Science Department, University of Southern California

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Abstract

Multi-agent teamwork is critical in a large number of agent applications, including training, education, virtual enterprises and collective robotics. Tools that can help humans analyze, evaluate, and understand team behaviors are becoming increasingly important as well. We have taken a step towards building such a tool by creating an automated analyst agent called ISAAC for post-hoc, off-line agent-team analysis. ISAAC’s novelty stems from a key design constraint that arises in team analysis: multiple types of models of team behavior are necessary to analyze different granularities of team events, including agent actions, interactions, and global performance. These heterogeneous team models are automatically acquired via machine learning over teams’ external behavior traces, where the specific learning techniques are tailored to the particular model learned. Additionally, ISAAC employs multiple presentation techniques that can aid human understanding of the analyses. This paper presents ISAAC’s general conceptual framework, motivating its design, as well as its concrete application in the domain of RoboCup soccer. In the RoboCup domain, ISAAC was used prior to and during the RoboCup’99 tournament, and was awarded the RoboCup scientific challenge award.