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IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

作者： Shamsi, Seyed Mahdi Farina, Gian Pietro Gaboardi, Marco Napp, Nils Univ Buffalo Dept Comp Sci & Engn Buffalo NY 14260 USA

ISBN: (纸本)9781728164229

We present a robotic development framework called ROSPPL, which can accomplish many of the essential probabilistic tasks that comprise modern autonomous systems and is based on a general purpose probabilistic programming language (PPL). Benefiting from ROS integration, a short PPL program in our framework is capable of controlling a robotic system, estimating its current state online, as well as automatically calibrating parameters and detecting errors, simply through probabilistic model and policy specification. The advantage of our approach lies in its generality which makes it useful for quickly designing and prototyping of new robots. By directly modeling the interconnection of random variables, decoupled from the inference engine, our design benefits from robustness, re-usability, upgradability, and ease of specification. In this paper, we use a SDV as an example of a complex autonomous system, to show how different sub-components of such system could be implemented using a probabilistic programming language, in a way that the system is capable of reasoning about itself. Our set of use-cases include localization, mapping, fault detection, calibration, and planning.

关键词： probabilistic programming languages Robotics SLAM Planning Error Detection Calibration

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probabilistic programming languages for Modeling Autonomous Systems

Probabilistic Programming Languages for Modeling Autonomous ...

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International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

作者： Seyed Mahdi Shamsi Gian Pietro Farina Marco Gaboardi Nils Napp University at Buffalo Buffalo NY

ISBN: (数字)9781728164229

ISBN: (纸本)9781728164236

关键词： probabilistic programming languages Robotics SLAM Planning Error Detection Calibration Error detection Computer programming languages supersonic low altitude missile robotics Autonomous Systems new robot Probability Calibration Planning probabilistic Model PPL gene PRIORITY PROBLEM LIST Specifications

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Compiling Universal probabilistic programming languages with Efficient Parallel Sequential Monte Carlo Inference 1

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31st European Symposium on programming (ESOP) Held as Part of the 25th European Joint Conferences on Theory and Practice of Software (ETAPS)

作者： Lunden, Daniel Ohman, Joey Kudlicka, Jan Senderov, Viktor Ronquist, Fredrik Broman, David KTH Royal Inst Technol EECS & Digital Futures Stockholm Sweden AI Sweden Stockholm Sweden BI Norwegian Business Sch Dept Data Sci & Analyt Oslo Norway Swedish Museum Nat Hist Dept Bioinformat & Genet Stockholm Sweden Stockholm Univ Dept Zool Stockholm Sweden

ISBN: (数字)9783030993368

ISBN: (纸本)9783030993368;9783030993351

probabilistic programming languages (PPLs) allow users to encode arbitrary inference problems, and PPL implementations provide general-purpose automatic inference for these problems. However, constructing inference implementations that are efficient enough is challenging for many real-world problems. Often, this is due to PPLs not fully exploiting available parallelization and optimization opportunities. For example, handling probabilistic checkpoints in PPLs through continuation-passing style transformations or non-preemptive multitasking-as is done in many popular PPLs-often disallows compilation to low-level languages required for high-performance platforms such as GPUs. To solve the checkpoint problem, we introduce the concept of PPL control-flow graphs (PCFGs)-a simple and efficient approach to checkpoints in low-level languages. We use this approach to implement RootPPL: a low-level PPL built on CUDA and C++ with OpenMP, providing highly efficient and massively parallel SMC inference. We also introduce a general method of compiling universal high-level PPLs to PCFGs and illustrate its application when compiling Miking CorePPL-a high-level universal PPL-to RootPPL. The approach is the first to compile a universal PPL to GPUs with SMC inference. We evaluate RootPPL and the CorePPL compiler through a set of real-world experiments in the domains of phylogenetics and epidemiology, demonstrating up to 6x speedups over state-of-the-art PPLs implementing SMC inference.

关键词： probabilistic programming languages Compilers Sequential Monte Carlo GPU Compilation

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Modelling Recursion and probabilistic Choice in Guarded Type Theory

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PROCEEDINGS OF THE ACM ON programming languages-PACMPL 2025年第POPL期9卷 1417-1445页

作者： Stassen, Philipp Mogelberg, Rasmus ejlers Zwart, Maaike annebet Aguirre, Alejandro Birkedal, Lars Aarhus Univ Aarhus Denmark IT Univ Copenhagen Copenhagen Denmark

Constructive type theory combines logic and programming in one language. This is useful both for reasoning about programs written in type theory, as well as for reasoning about other programming languages inside type theory. It is well-known that it is challenging to extend these applications to languages with recursion and computational effects such as probabilistic choice, because these features are not easily represented in constructive type theory. We show how to define and reason about FPC circle plus, a programming language with probabilistic choice and recursive types, in guarded type theory. We use higher inductive types to represent finite distributions and guarded recursion to model recursion. We define both operational and denotational semantics of FPC circle plus, as well as a relation between the two. The relation can be used to prove adequacy, but we also show how to use it to reason about programs up to contextual equivalence.

关键词： probabilistic programming languages Type Theory Guarded Recursion Recursive Types

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probabilistic (logic) programming concepts

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MACHINE LEARNING 2015年第1期100卷 5-47页

作者： De Raedt, Luc Kimmig, Angelika Katholieke Univ Leuven Dept Comp Sci B-3001 Heverlee Belgium

A multitude of different probabilistic programming languages exists today, all extending a traditional programming language with primitives to support modeling of complex, structured probability distributions. Each of these languages employs its own probabilistic primitives, and comes with a particular syntax, semantics and inference procedure. This makes it hard to understand the underlying programming concepts and appreciate the differences between the different languages. To obtain a better understanding of probabilistic programming, we identify a number of core programming concepts underlying the primitives used by various probabilistic languages, discuss the execution mechanisms that they require and use these to position and survey state-of-the-art probabilistic languages and their implementation. While doing so, we focus on probabilistic extensions of logic programming languages such as Prolog, which have been considered for over 20 years.

关键词： probabilistic programming languages probabilistic logic programming Statistical relational learning Inference in probabilistic languages

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probabilistic Incremental Program Evolution

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EVOLUTIONARY COMPUTATION 1997年第2期5卷 123-141页

作者： Salustowicz, Rafal Schmidhuber, Juergen IDSIA CH-6900 Lugano Switzerland

probabilistic incremental program evolution (PIPE) is a novel technique for automatic program synthesis. We combine probability vector coding of program instructions, population-based incremental learning, and tree-coded programs like those used in some variants of genetic programming (GP). PIPE iteratively generates successive populations of functional programs according to an adaptive probability distribution over all possible programs. Each iteration, it uses the best program to refine the distribution. Thus, it stochastically generates better and better programs. Since distribution refinements depend only on the best program of the current population, PIPE can evaluate program populations efficiently when the goal is to discover a program with minimal runtime. We compare PIPE to GP on a function regression problem and the 6-bit parity problem. We also use PIPE to solve tasks in partially observable mazes, where the best programs have minimal runtime.

关键词： probabilistic incremental program evolution probabilistic programming languages stochastic program search population-based incremental learning genetic programming partially observable environments

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Computable de Finetti measures

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ANNALS OF PURE AND APPLIED LOGIC 2012年第5期163卷 530-546页

作者： Freer, Cameron E. Roy, Daniel M. MIT Dept Math Cambridge MA 02139 USA MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02139 USA

We prove a computable version of the de Finetti theorem on exchangeable sequences of real random variables. As a consequence, exchangeable stochastic processes expressed in probabilistic functional programming languages can be automatically rewritten as procedures that do not modify non-local state. Along the way, we prove that a distribution on the unit interval is computable if and only if its moments are uniformly computable. (C) 2011 Elsevier B.V. All rights reserved.

关键词： The de Finetti theorem Exchangeability Computable probability theory probabilistic programming languages Mutation

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Symbolic Execution for Randomized Programs

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PROCEEDINGS OF THE ACM ON programming languages-PACMPL 2022年第OOPSLA期6卷 1583–1612页

作者： Susag, Zachary Lahiri, Sumit Hsu, Justin Roy, Subhajit Cornell Univ Ithaca NY 14853 USA IIT Kanpur Kanpur India

We propose a symbolic execution method for programs that can draw random samples. In contrast to existing work, our method can verify randomized programs with unknown inputs and can prove probabilistic properties that universally quantify over all possible inputs. Our technique augments standard symbolic execution with a new class of probabilistic symbolic variables, which represent the results of random draws, and computes symbolic expressions representing the probability of taking individual paths. We implement our method on top of the KLEE symbolic execution engine alongside multiple optimizations and use it to prove properties about probabilities and expected values for a range of challenging case studies written in C++, including Freivalds' algorithm, randomized quicksort, and a randomized property-testing algorithm for monotonicity. We evaluate our method against Psi, an exact probabilistic symbolic inference engine, and Storm, a probabilistic model checker, and show that our method significantly outperforms both tools.

关键词： probabilistic programming languages symbolic execution automated software verification

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Characterizations of semantic domains for randomized algorithms

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Japan Journal of Applied Mathematics 1989年第1期6卷 111-146页

作者： Yamada, Shinichi Department of Mathematics School of Science and Engineering Waseda University Shinjuku Tokyo 160 3-4-1 Ohkubo Japan Research and Advanced Technology Nihon Unisys Ltd. Tokyo 107 2-17-51 Akasaka Minato-ku Japan

Randomized algorithms, or probabilistic algorithms, extend the notion of algorithm by introducing input of random data and random choices in the process of computation. A new mathematical theory of the semantic domains for randomized algorithms, or randomized domains, is developed, which intrinsically extends Scott's domain theory for deterministic computation. Main results include the characterization theorems and practical axiom systems for randomized domains, and the construction of the universal reflexive randomized domain R_∞. The theory readily provides denotational semantics for a class of high level probabilistic programming languages and is also directly applicable to probabilistic logics, inductive inference, and theory of stochastic programming. © 1989 JJAM Publishing Committee.

关键词： denotational semantics probabilistic programming languages randomized algorithm randomized domains

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Computable Exchangeable Sequences Have Computable de Finetti Measures

Computable Exchangeable Sequences Have Computable de Finetti...

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5th Conference on Computability in Europe (CiE 2009)

作者： Freed, Cameron E. Roy, Daniel M. MIT Dept Math Cambridge MA 02139 USA MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02139 USA

ISBN: (纸本)9783642030727

We prove a uniformly computable version of de Finetti's theorem on exchangeable sequences of real random variables. In the process, we develop machinery for computably recovering a, distribution from its sequence of moments;which suffices to prove the theorem in the case of (almost sorely) continuous directing random measures. In the general case, we give a proof inspired by a randomized algorithm which succeeds with probability one. Finally, we show how, as a consequence of the main theorem, exchangeable stochastic processes in probabilistic functional programming languages can be rewritten as procedures that do not use mutation.

关键词： de Finetti exchangeability computable probability theory probabilistic programming languages mutation

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