Strong Stackelberg Equilibrium (SSE) is a fundamental solution concept in game theory in which one player commits to a strategy, while the other player observes this commitment and plays a best response. We present a new algorithm for computing SSE for two-player extensive-form general-sum games with imperfect information (EFGs) where computing SSE is an NP-hard problem. Our algorithm is based on a correlated version of SSE, known as Stackelberg Extensive-Form Correlated Equilibrium (SEFCE). Our contribution is therefore twofold: (1) we give the first linear program for computing SEFCE in EFGs without chance, (2) we repeatedly solve and modify this linear program in a systematic search until we arrive to SSE. Our new algorithm outperforms the best previous algorithms by several orders of magnitude.

Problems of domain-independent multiagent planning for cooperative agents in deterministic environments can be tackled by a well-known initiator--participants scheme from classical multiagent negotiation protocols. In this work, we use the approach to describe a multiagent extension of the Generate-And-Test principle distributively searching for a coordinated multiagent plan. The generate part uses a novel plan quality estimation technique based on metrics borrowed from the field of diverse planning. The test part builds upon planning with landmarks by compilation to classical planning. Finally, the proposed multiagent planning approach was experimentally analyzed on one newly designed domain and one classical benchmark domain. The results show what combination of plan quality estimation and diversity metrics provide the best planning efficiency