Non Deterministic Logic Programs
  Non deterministic applications arise in many domains, including, stochastic
optimization, multi-objectives optimization, stochastic planning, contingent
stochastic planning, reinforcement learning, reinforcement learning in
partially observable Markov decision processes, and conditional planning. We
present a logic programming framework called non deterministic logic programs,
along with a declarative semantics and fixpoint semantics, to allow
representing and reasoning about inherently non deterministic real-world
applications. The language of non deterministic logic programs framework is
extended with non-monotonic negation, and two alternative semantics are
defined: the stable non deterministic model semantics and the well-founded non
deterministic model semantics as well as their relationship is studied. These
semantics subsume the deterministic stable model semantics and the
deterministic well-founded semantics of deterministic normal logic programs,
and they reduce to the semantics of deterministic definite logic programs
without negation. We show the application of the non deterministic logic
programs framework to a conditional planning problem.
