The following sections present many different MAS techniques that have been previously published. They present an extensive, but not exhaustive, list of work in the field. Despite the youth of the field, space does not permit exhaustive coverage. Instead, the work mentioned is intended to illustrate the techniques that exist to deal with the issues that arise in the various multiagent scenarios. When possible, ML approaches are emphasized.
In increasing order of complexity, the three multiagent scenarios considered are: homogeneous non-communicating agents, heterogeneous non-communicating agents, and heterogeneous communicating agents. For each of these scenarios, the research issues that arise, the techniques that deal with them, and additional ML opportunities are presented. The issues may appear across scenarios, but they are presented and discussed in the least complex scenario to which they apply.
In addition to the existing learning approaches described in the sections entitled ``Issues and Techniques'', there are several previously unexplored learning opportunities that apply in each of the multiagent scenarios. For each scenario, a few promising opportunities for ML researchers are presented.
Many existing ML techniques can be directly applied in multiagent scenarios by delimiting a part of the domain that only involves a single agent. However multiagent learning is more concerned with learning issues that arise because of the multiagent aspect of a given domain. As described by Weiß, multiagent learning is ``learning that is done by several agents and that becomes possible only because several agents are present'' . This type of learning is emphasized in the sections entitled ``Further Learning Opportunities.''
For the purpose of illustration, each scenario is accompanied by a suitable instantiation of the Predator/Prey or ``Pursuit'' domain.