Multi-agent teamwork is critical in a large number of agent applications, including training, education, virtual enterprises and collective robotics. T(x)ls 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 Roboup'99 tournament, and was awarded the Roboup scientific challenge award.
展开▼
