With the growing importance of multiagent teamwork, tools that can help humans analyze, evaluate, and understand team behaviors are also becoming increasingly important. To this end, we are creating isaac, a team analyst agent for post hoc, offline agent-team analysis. With the growing importance of teamwork, there is now a critical need for tools to help humans analyze, evaluate, and understand team behaviors. Indeed, in multiagent domains with tens or even hundreds of agents in teams, agent interactions are often highly complex and dynamic, making it difficult for human developers to analyze agent-team behaviors. The problem is further exacerbated in environments where agents are developed by different developers, where even the intended interactions are unpredictable.
With the growing importance of multiagent team-work, tools that can help humans analyze, evaluate, and understand team behaviors are also becoming increasingly important. To this end, we are creating isaac, a team analyst agent for post hoc, offline 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 by machine learning over teams' external behavior traces, where the specific learning techniques are tailored to the particular model learned. Additionally, ISAAC uses multiple presentation techniques that can aid human understanding of the analyses. This article presents ISAAC'S general conceptual framework and its application in the RoboCup soccer domain, where ISAAC was awarded the RoboCup Scientific Challenge Award.
Today, we live in a time characterized by rapid technology transformation, and resulting social, political, and economic disruption. In its wake, few institutions have remained untouched. The results can be dislocation, upheaval, opportunity, and inequality. Change is in the air. We are living in interesting times.
Almost every other day, either one of my colleague, college friend or an online contact from LinkedIn/Twitter will ask me "I have been reading a lot of hype around artificial intelligence and machine learning, I tried to read some of the articles and watched some videos but I really don't know where to start. Can you help or share something?". It is difficult to give a structured answer. It is totally crazy to learn everything about artificial intelligence. This field is so wide that it is easy to hit a roadblock because you started learning it in the wrong way (difficult way) without assessing your readiness.