  Agent programming is mostly a symbolic discipline and, as such, draws little
benefits from probabilistic areas as machine learning and graphical models.
However, the greatest objective of agent research is the achievement of
autonomy in dynamical and complex environments --- a goal that implies
embracing uncertainty and therefore the entailed representations, algorithms
and techniques. This paper proposes an innovative and conflict free two layer
approach to agent programming that uses already established methods and tools
from both symbolic and probabilistic artificial intelligence. Moreover, this
framework is illustrated by means of a widely used agent programming example,
GoldMiners.
