Recent findings suggest that humans deploy cognitive mechanism of physics simulation engines to simulate the objects. We propose a framework for bots probabilistic programming tools interacting with intuitive environments. The employs in way infer about moves performed by an agent setting governed Newtonian laws motion. However, methods programs can be slow such due their need generate many sam...

Probabilistic Programming Languages (PPLs) allow users to encode statistical inference problems and automatically apply an algorithm solve them. Popular algorithms for PPLs, such as sequential Monte Carlo (SMC) Markov chain (MCMC), are built around checkpoints -- relevant events the during execution of a probabilistic program. Deciding location is, in current not done optimally. To this problem...

This paper presents ProbCompCert, a compiler for subset of the Stan probabilistic programming language (PPL), in which several key passes have been formally verified using Coq proof assistant. Because nature PPLs, bugs their compilers can be difficult to detect and fix, making verification an interesting possibility. However, proving correctness PPL compilation requires new techniques because c...

This paper presents PFLP, a library for probabilistic programming in the functional logic programming language Curry. It demonstrates how the concepts of a functional logic programming language support the implementation of a library for probabilistic programming. In fact, the paradigms of functional logic and probabilistic programming are closely connected. That is, we can apply techniques fro...

In this paper, we focus on the combination of probabilistic logic programming with the principle of maximum entropy. We start by deening probabilistic queries to probabilistic logic programs and their answer substitutions under maximum entropy. We then present an ef-cient linear programming characterization for the problem of deciding whether a probabilistic logic program is satissable. Finally...

In this paper, we focus on the combination of probabilistic logic programming with the principle of maximum entropy. We start by deening probabilistic queries to probabilistic logic programs and their answer substitutions under maximum entropy. We then present an eecient linear programming characterization for the problem of deciding whether a probabilistic logic program is satissable. Finally,...

We present a probabilistic logic programming framework that allows the representation of conditional probabilities. While conditional probabilities are the most commonly used method for representing uncertainty in probabilistic expert systems, they have been largely neglected by work in quantitative logic programming. We de-ne a xpoint theory, declarative semantics, and proof procedure for the ...

Inference is the key bottleneck in probabilistic programming. Often, the main advantages of probabilistic programming – simplicity, modularity, ease-of-use, etc. – are dwarfed by the complexity and intractability of inference. In fact, one of the main reasons for the scarcity/absence of large applications and real-world systems that are based in large part on probabilistic programming languages...