Computational Results for Extensive-Form Adversarial Team Games

TL;DR

This paper presents the first computational analysis of extensive-form adversarial team games, exploring different communication scenarios, their inefficiencies, computational complexities, and algorithms for equilibrium finding.

Contribution

It introduces the first computational study of these games, defines solution concepts, analyzes inefficiency due to communication limits, and provides algorithms for equilibrium computation.

Findings

Inefficiency can be arbitrarily large with limited communication.

Exact algorithms are developed for each communication scenario.

Empirical evaluation shows scalability and communication impact.

Abstract

We provide, to the best of our knowledge, the first computational study of extensive-form adversarial team games. These games are sequential, zero-sum games in which a team of players, sharing the same utility function, faces an adversary. We define three different scenarios according to the communication capabilities of the team. In the first, the teammates can communicate and correlate their actions both before and during the play. In the second, they can only communicate before the play. In the third, no communication is possible at all. We define the most suitable solution concepts, and we study the inefficiency caused by partial or null communication, showing that the inefficiency can be arbitrarily large in the size of the game tree. Furthermore, we study the computational complexity of the equilibrium-finding problem in the three scenarios mentioned above, and we provide, for each of the three scenarios, an exact algorithm. Finally, we empirically evaluate the scalability of the algorithms in random games and the inefficiency caused by partial or null communication.