This paper presents a mu-calculus-based modal logic for describing properties of reactive probabilistic labeled transition systems (RPLTSs) and develops a modelchecking algorithm for determining whether or not states in finite-state RPLTSs satisfy formulas in the logic. The logic is based on the distinction between (probabilistic) “systems” and (nonprobabilistic) “observations”: using the modal...

This paper provides a Hoare-style logic for quantum computation. While the usual Hoare logic helps us to verify classical deterministic programs, our logic supports quantum probabilistic programs. Our target programming language is QPL defined by Selinger, and our logic is an extension of the probabilistic Hoare-style logic defined by den Hartog. In this paper, we demonstrate how the quantum Ho...

Hybrid probabilistic programs framework [5] is a variation of probabilistic annotated logic programming approach, which allows the user to explicitly encode the available knowledge about the dependency among the events in the program. In this paper, we extend the language of hybrid probabilistic programs by allowing disjunctive composition functions to be associated with heads of clauses and ch...

This paper presents KReator, a versatile integrated development environment for probabilistic inductive logic programming currently under development. The area of probabilistic inductive logic programming (or statistical relational learning) aims at applying probabilistic methods of inference and learning in relational or first-order representations of knowledge. In the past ten years the commu...

Uncertain information is being taken into account in an increasing number of application fields. In the meantime, abduction has been proved a powerful tool for handling hypothetical reasoning and incomplete knowledge. Probabilistic logical models are suitable framework to handle uncertain information, last decade many probabilistic languages have proposed, as well inference learning systems the...