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