In two-player finite-state stochastic games of partial observation on graphs, in every state of
the graph, the players simultaneously choose an action, and their joint actions determine a …

Markov Decision Processes (MDPs) model systems with uncertain transition dynamics.
Multiple-environment MDPs (MEMDPs) extend MDPs. They intuitively reflect finite sets of …

The model of probabilistic automata was introduced by Rabin in 1963. Ever since,
undecidability results were obtained for this model, showing that although simple, it is very …

Rich representation of knowledge and actions has been a goal that many AI researchers
pursue. Among all proposals, perhaps, the situation calculus by Reiter is the most widely …

We consider probabilistic automata on infinite words with acceptance defined by parity
conditions. We consider three qualitative decision problems:(i) the positive decision problem …

Probabilistic automata are an extension of nondeterministic finite automata in which
transitions are annotated with probabilities. Despite its simplicity, this model is very …

We consider distribution-based objectives for Markov Decision Processes (MDP). This class
of objectives gives rise to an interesting trade-off between full and partial information. As in …

We study alternating automata with qualitative semantics over infinite binary trees:
Alternation means that two opposing players construct a decoration of the input tree called a …

The emptiness and containment problems for probabilistic automata are natural quantitative
generalisations of the classical language emptiness and inclusion problems for Boolean …

In a recent paper, Belle and Levesque proposed a framework for a type of program called
belief programs, a probabilistic extension of GOLOG programs where every action and …