  ATNoSFERES is a Pittsburgh style Learning Classifier System (LCS) in which
the rules are represented as edges of an Augmented Transition Network.
Genotypes are strings of tokens of a stack-based language, whose execution
builds the labeled graph. The original ATNoSFERES, using a bitstring to
represent the language tokens, has been favorably compared in previous work to
several Michigan style LCSs architectures in the context of Non Markov
problems. Several modifications of ATNoSFERES are proposed here: the most
important one conceptually being a representational change: each token is now
represented by an integer, hence the genotype is a string of integers; several
other modifications of the underlying grammar language are also proposed. The
resulting ATNoSFERES-II is validated on several standard animat Non Markov
problems, on which it outperforms all previously published results in the LCS
literature. The reasons for these improvement are carefully analyzed, and some
assumptions are proposed on the underlying mechanisms in order to explain these
good results.
