12,646 research outputs found
An Answer Set Programming-based Implementation of Epistemic Probabilistic Event Calculus
We describe a general procedure for translating Epistemic Probabilistic Event Calculus (EPEC) action language domains into Answer Set Programs (ASP), and show how the Python-driven features of the ASP solver Clingo can be used to provide efficient computation in this probabilistic setting. EPEC supports probabilistic, epistemic reasoning in domains containing narratives that include both an agent’s own action executions and environmentally triggered events. Some of the agent’s actions may be belief-conditioned, and some may be imperfect sensing actions that alter the strengths of previously held beliefs. We show that our ASP implementation can be used to provide query answers that fully correspond to EPEC’s own declarative, Bayesian-inspired semantics
Making Random Choices Invisible to the Scheduler
When dealing with process calculi and automata which express both
nondeterministic and probabilistic behavior, it is customary to introduce the
notion of scheduler to solve the nondeterminism. It has been observed that for
certain applications, notably those in security, the scheduler needs to be
restricted so not to reveal the outcome of the protocol's random choices, or
otherwise the model of adversary would be too strong even for ``obviously
correct'' protocols. We propose a process-algebraic framework in which the
control on the scheduler can be specified in syntactic terms, and we show how
to apply it to solve the problem mentioned above. We also consider the
definition of (probabilistic) may and must preorders, and we show that they are
precongruences with respect to the restricted schedulers. Furthermore, we show
that all the operators of the language, except replication, distribute over
probabilistic summation, which is a useful property for verification
- …