Multiagent Systems is the subfield of AI that aims to provide both principles for construction of complex systems involving multiple agents and mechanisms for coordination of independent agents' behaviors. As of yet, there has been little work with Multiagent Systems that require real-time control in noisy, adversarial environments. Because of the inherent complexity of this type of Multiagent System, Machine Learning is an interesting and promising area to merge with Multiagent Systems. Machine learning has the potential to provide robust mechanisms that leverage upon experience to equip agents with a large spectrum of behaviors, ranging from effective individual performance in a team, to collaborative achievement of independently and jointly set high-level goals. Especially in domains that include independently designed agents with conflicting goals (adversaries), learning may allow agents to adapt to unforeseen behaviors on the parts of other agents.
Layered Learning is an approach to complex multiagent domains
that involves incorporating low-level learned behaviors into
higher-level behaviors [Stone and Veloso1997a]. Using simulated Robotic
Soccer (see Section 
) as an example of such a
domain, a Neural Network (NN) was used to learn a low-level individual
behavior (ball-interception), which was then incorporated into a basic
collaborative behavior (passing). The collaborative behavior was
learned via a Decision Tree (DT) [Stone and Veloso1997a].
This paper extends these basic learned behaviors into a full multiagent behavior that is capable of controlling agents throughout an entire game. This behavior makes control decisions based on the confidence factors associated with DT classifications--a novel approach. It also makes use of the ability to reason about action-execution time to eliminate options that would not have adequate time to be executed successfully. The newly created behavior is tested empirically in game situations.
The rest of the paper is organized as follows.
Section gives an overview of foundational
work in the Robotic Soccer domain. The new behavior, along
with explanations of how the DT is used for control and how the
agents reason about action-execution time, is presented in
Section . Extensive empirical results are reported in
Section , and Section concludes.