  We consider a cooperative multi-agent system consisting of a team of agents
with decentralized information. Our focus is on the design of symmetric (i.e.
identical) strategies for the agents in order to optimize a finite horizon team
objective. We start with a general information structure and then consider some
special cases. The constraint of using symmetric strategies introduces new
features and complications in the team problem. For example, we show in a
simple example that randomized symmetric strategies may outperform
deterministic symmetric strategies. We also discuss why some of the known
approaches for reducing agents' private information in teams may not work under
the constraint of symmetric strategies. We then adopt the common information
approach for our problem and modify it to accommodate the use of symmetric
strategies. This results in a common information based dynamic program where
each step involves minimization over a single function from the space of an
agent's private information to the space of probability distributions over
actions. We present specialized models where private information can be reduced
using simple dynamic program based arguments.

Optimization and Control;Multiagent Systems