Most cooperative games are tackled by creating a team of agents who are optimised for each other and the problem. Creating an agent who can play in a variety of teams without any foreknowledge of its partner is a different challenge. These AI systems could useful for human-AI interaction as different people bring a lot of variance into the system. The training method synchronous K-level reasoning best response (SyKLRBR) tries to tackle this problem by creating agents based on grounded information. This research tested the potential of SyKLRBR in human-AI research evaluating the performance of its agent in the cooperative game Overcooked. The agents were able to obtain consistent scores against several unseen strategies, suggesting that SyKLRBR is able to create robust agents. Where SyKLRBR shows potential for medium cooperative settings this paper also discusses its great weakness for highly cooperative problems.