Model counting is the problem of computing the number of models that satisfy a given propositional theory. It has recently been applied to solving inference tasks in probabilistic logic programming, where the goal is to compute the probability of given queries being true provided a set of mutually independent random variables, a model (a logic program) and some evidence. The core of solving thi...

Multimodal grammars provide an expressive formalism for multimodal integration and understanding. However, handcrafted multimodal grammars can be brittle with respect to unexpected, erroneous, or disfluent inputs. Spoken language (speech-only) understanding systems have addressed this issue of lack of robustness of hand-crafted grammars by exploiting classification techniques to extract fillers...

In 3] a probabilistic space PCSP of processes is constructed uniformly from the standard CSP failures-divergences model. Here we identify the deterministic (probabilistic) elements of PCSP as the maximal elements in the reenement ordering. We extend the standard characterisation of deterministic processes in terms of of properties of their nite approximations, and formulate some analogues of th...

This paper presents a Banach space based approach towards a denotational semantics of a probabilistic constraint programming language. This language is based on the concurrent constraint programming paradigm, where randomness is introduced by means of a probabilis-tic choice construct. As a result, we obtain a declarative framework, in which randomised algorithms can be expressed and formalised...

This technical report describes the usage, syntax, semantics and core algorithms of the probabilistic inductive logic programming framework PrASP. PrASP is a research software which integrates non-monotonic reasoning based on Answer Set Programming (ASP), probabilistic inference and parameter learning. In contrast to traditional approaches to Probabilistic (Inductive) Logic Programming, our fra...

Probabilistic techniques are central to data analysis, but different approaches can be difficult to apply, combine, and compare. This paper introduces composable generative population models (CGPMs), a computational abstraction that extends directed graphical models and can be used to describe and compose a broad class of probabilistic data analysis techniques. Examples include hierarchical Bay...

Formal methods have long been employed to capture the dynamics of biological systems in terms of Continuous Time Markov Chains. The formal approach enables the use of elegant analysis tools such as model checking, but usually relies on a complete specification of the model of interest and cannot easily accommodate uncertain data. In contrast, data-driven modelling, based on machine learning tec...

Psychopharmacological studies have implicated the mesolimbic dopamine (DA) system in the mediation of cost/benefit evaluations about effort-related costs associated with larger rewards. However, the role of DA in risk-based decision making remains relatively unexplored. The present study investigated how systemic manipulations of DA transmission affect risky choice assessed with a probabilistic...