  This paper deals with chain graphs under the alternative
Andersson-Madigan-Perlman (AMP) interpretation. In particular, we present a
constraint based algorithm for learning an AMP chain graph a given probability
distribution is faithful to. We also show that the extension of Meek's
conjecture to AMP chain graphs does not hold, which compromises the development
of efficient and correct score+search learning algorithms under assumptions
weaker than faithfulness.
