Trick Costs for  \(\alpha \mu \) and New Relatives

In this paper we present a player for incomplete information card games with tricks scored by points or eyes. We factorize existing algorithms into a template that captures the main ingredients of such game trees. We then analyze three different algorithms, and the impact of the information given during the algorithm on the decisions it makes. We extend these algorithms to work with cost instead of winning vectors, and illustrate their effectiveness in finding good cards. We develop a new algorithm that tries to respect known information.