Behavior Acquisition and Classification: A Case Study in Robotic Soccer

Increasingly in domains with multiple intelligent agents, each agent must be able to identify what the other agents are doing. This is especially important when there are adversarial agents inferring with the accomplishment of goals. Once identified, the agents can then respond to recent strategies and adapt to improve performance. This research works under the hypothesis that fast and useful adaptation can be done by analogy to previous observations. We introduce methods to extract similarities in temporal observations of the world. First, past observations are organized into a set of behavior classes. By analyzing similarities, the current adversary can be classified into this set of behavior classes. The agents can then employ the most effective strategy against that behavior group. The test domain for this research is the Soccer Server System (Noda et al. 1998) as used in the Robot World Cup Initiative (Kitano et al. 1997). The server provides a realistic simulation of a soccer game. Distributed software agents interact in a complex, noisy, inaccessible environment. The software was developed based on the champion CMUnited99 agent team (Stone, Riley, & Veloso 2000). The raw data of the simulation consists of locations of players and the ball over time. The data is first broken into windows of fixed size. For each window, several features are extracted. Each feature extractor watches for a particular type of event (such as an opponent’s pass or an opponent’s shot). Upon observing an event of the right type, the feature extractor records where on the field, but not when in the window the event occurred. The recordings of all the games at RoboCup-98 and RoboCup-99 were used as the data sets. A behavior class is created for each team in the competitions. The teams are first observed on a fraction of the games they played. Then, for each type of feature, the data from each window is averaged together to create a “target configuration” for that feature type. In other words, a behavior class consists of a set of examples for what each feature extractor should return if the current opponent is in that class. After creating these behavior classes, the goal is to correctly identify which teams were playing based on these observations. In order to perform any classification, there must be a notion of similarity between the target configuration and what was actually observed. A novel similarity metric was developed that takes in account spatial localities of topological differences. Classification was performed in two ways. First with a standard nearest-neighbor approach and then by training a decision tree with the similarities to all of the target configurations as the feature set.