24,294 research outputs found
A Description Logic Framework for Commonsense Conceptual Combination Integrating Typicality, Probabilities and Cognitive Heuristics
We propose a nonmonotonic Description Logic of typicality able to account for
the phenomenon of concept combination of prototypical concepts. The proposed
logic relies on the logic of typicality ALC TR, whose semantics is based on the
notion of rational closure, as well as on the distributed semantics of
probabilistic Description Logics, and is equipped with a cognitive heuristic
used by humans for concept composition. We first extend the logic of typicality
ALC TR by typicality inclusions whose intuitive meaning is that "there is
probability p about the fact that typical Cs are Ds". As in the distributed
semantics, we define different scenarios containing only some typicality
inclusions, each one having a suitable probability. We then focus on those
scenarios whose probabilities belong to a given and fixed range, and we exploit
such scenarios in order to ascribe typical properties to a concept C obtained
as the combination of two prototypical concepts. We also show that reasoning in
the proposed Description Logic is EXPTIME-complete as for the underlying ALC.Comment: 39 pages, 3 figure
Bisimulation, Logic and Reachability Analysis for Markovian Systems
In the recent years, there have been a large amount of investigations on safety verification of uncertain continuous systems. In engineering and applied mathematics, this verification is called stochastic reachability analysis, while in computer science this is called probabilistic model checking
(PMC). In the context of this work, we consider the two terms interchangeable. It is worthy to note that PMC has been mostly considered for discrete systems. Therefore, there is an issue of improving the application of computer science techniques in the formal verification of continuous stochastic systems.
We present a new probabilistic logic of model theoretic nature. The terms of this logic express reachability properties and the logic formulas express statistical properties of terms.
Moreover, we show that this logic characterizes a bisimulation relation for continuous time continuous space Markov processes. For this logic we define a new semantics using state space symmetries. This is a recent concept that was successfully used in model checking. Using this semantics, we prove a full abstraction result. Furthermore, we prove a result that can be used in model checking, namely that the bisimulation preserves the probabilities of the reachable sets
Coalgebraic Semantics for Probabilistic Logic Programming
Probabilistic logic programming is increasingly important in artificial
intelligence and related fields as a formalism to reason about uncertainty. It
generalises logic programming with the possibility of annotating clauses with
probabilities. This paper proposes a coalgebraic semantics on probabilistic
logic programming. Programs are modelled as coalgebras for a certain functor F,
and two semantics are given in terms of cofree coalgebras. First, the
F-coalgebra yields a semantics in terms of derivation trees. Second, by
embedding F into another type G, as cofree G-coalgebra we obtain a `possible
worlds' interpretation of programs, from which one may recover the usual
distribution semantics of probabilistic logic programming. Furthermore, we show
that a similar approach can be used to provide a coalgebraic semantics to
weighted logic programming
Coalgebraic semantics for probabilistic logic programming
Probabilistic logic programming is increasingly important in artificial intelligence and related fields as a formalism to reason about uncertainty. It generalises logic programming with the possibility of annotating clauses with probabilities. This paper proposes a coalgebraic semantics on probabilistic logic programming. Programs are modelled as coalgebras for a certain functor F, and two semantics are given in terms of cofree coalgebras. First, the cofree F-coalgebra yields a semantics in terms of derivation trees. Second, by embedding F into another type G, as cofree G-coalgebra we obtain a 'possible worlds' interpretation of programs, from which one may recover the usual distribution semantics of probabilistic logic programming. Furthermore, we show that a similar approach can be used to provide a coalgebraic semantics to weighted logic programming
A Coalgebraic Perspective on Probabilistic Logic Programming
Probabilistic logic programming is increasingly important in artificial intelligence and related fields as a formalism to reason about uncertainty. It generalises logic programming with the possibility of annotating clauses with probabilities. This paper proposes a coalgebraic perspective on probabilistic logic programming. Programs are modelled as coalgebras for a certain functor F, and two semantics are given in terms of cofree coalgebras. First, the cofree F-coalgebra yields a semantics in terms of derivation trees. Second, by embedding F into another type G, as cofree G-coalgebra we obtain a "possible worlds" interpretation of programs, from which one may recover the usual distribution semantics of probabilistic logic programming
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