  Imperfect recall games represent dynamic interactions where players forget
previously known information, such as a history of played actions. The
importance of imperfect recall games stems from allowing a concise
representation of strategies compared to perfect recall games where players
remember all information. However, most of the algorithmic results are negative
for imperfect recall games -- a Nash equilibrium~(NE) does not have to exist
and computing a best response or a maxmin strategy is NP-hard. We focus on a
subclass of imperfect recall games, called A-loss recall games, where a best
response can be found in polynomial time. We derive novel properties of A-loss
recall games, including (1) a sufficient and necessary condition for the
existence of NE in A-loss recall games, (2) example where both NE and maxmin
require irrational numbers for rational input, and (3) NP-hardness of problems
related to finding maxmin strategies and existence of a NE strategy.
