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A SURROGATE FOR LINEAR-PROGRAMS WITH RANDOM REQUIREMENTS

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 1988年第3期34卷 399-402页

作者： GROVE, MA UNIV OREGON DEPT ECONEUGENEOR 97403 USA

An approach to linear programs with random requirements is suggested. The procedure involves choosing actions which minimize the expected value of a certain loss function. These actions are then taken as goals, and optimal values of the decision variables are found by solving a simple linear goal programming problem. [ABSTRACT FROM AUTHOR]

关键词： goal programming probabilistic programming

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Authoring Human Simulators via probabilistic Functional Reactive Program Synthesis 22

Authoring Human Simulators via Probabilistic Functional Reac...

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17th Annual ACM/IEEE International Conference on Human-Robot Interaction (HRI)

作者： Chung, Michael Jae-Yoon Cakmak, Maya Univ Washington Comp Sci & Engn Seattle WA 98195 USA

ISBN: (数字)9781665407311

ISBN: (纸本)9781665407311

One of the core challenges in creating interactive behaviors for social robots is testing. Programs implementing the interactive behaviors require real humans to test and this requirement makes testing of the programs extremely expensive. To address this problem, human-robot interaction researchers in the past proposed using human simulators. However, human simulators are tedious to set up and context-dependent and therefore are not widely used in practice. We propose a program synthesis approach to building human simulators for the purpose of testing interactive robot programs. Our key ideas are (1) representing human simulators as probabilistic functional reactive programming programs and (2) using probabilistic inference for synthesizing human simulator programs. Programmers then will be able to build human simulators by providing interaction traces between a robot and a human or two humans which they can later use to test interactive robot programs and improve or tweak as needed.

关键词： probabilistic programming program synthesis end-user programming social robots

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Surprisal, Liking, and Musical Affect 7th

Surprisal, Liking, and Musical Affect

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7th Biennial International Conference for Mathematics and Computation in Music (MCM)

作者： Fram, Noah R. CCRMA 660 Lomita Dr Stanford CA 94305 USA

ISBN: (纸本)9783030213923;9783030213916

Formulation and processing of expectation has long been viewed as an essential component of the emotional, psychological, and neurological response to musical events. There are multiple theories of musical expectation, ranging from a broad association between expectation violation and musical affect to precise descriptions of neurocognitive networks that contribute to the perception of surprising stimuli. In this paper, we propose a probabilistic model of musical expectation that relies on the recursive updating of listeners' conditional predictions of future events in the musical stream. This model is defined in terms of cross-entropy, or information content given a prior model. A probabilistic program implementing some aspects of this model with melodies from Bach chorales is shown to support the hypothesized connection between the evolution of surprisal through a piece and affective arousal, indexed by the spread of possible deviations from the expected play count.

关键词： Affect Entropy Music Perception probabilistic programming Surprisal

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Understanding Human Generated Decision Data 1

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10th Annual International symposium on Business Modeling and Software Design (BMSD)

作者： Silvander, Johan Blekinge Inst Technol Software Engn Res Lab Sweden Karlskrona Sweden

ISBN: (数字)9783030523060

ISBN: (纸本)9783030523060;9783030523053

In order to design intent-driven systems, the understanding of how the data is generated is essential. Without the understanding of the data generation process, it is not possible to use interventions, and counterfactuals. Interventions, and counterfactuals, are useful tools in order to achieve an artificial intelligence which can improve the system itself. We will create an understanding, and a model, of how data about decisions are generated, as well as used, by human decision makers. The research data were collected with the help of focus group interviews, and questionnaires. The models were built and evaluated with the help of, bayesian statistics, probability programming, and discussions with the practitioners. When we are combining, probabilistic programming models, extended machine learning algorithms, and data science processes, into a directed acyclic graph, we can mimic the process of human generated decision data. We believe the usage of a directed acyclic graph, to combine the functions and models, is a good base for mimic human generated decision data. Our next step is to evaluate if flow-based programming can be used as a framework for realization of components, useful in intent-driven systems.

关键词： Human decisions Bayesian statistics probabilistic programming

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PRAGMATIC APPROACHES TO OPTIMIZATION WITH RANDOM YIELD COEFFICIENTS

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FOREST SCIENCE 1995年第3期41卷 501-512页

作者： HOF, J BEVERS, M PICKENS, J MICHIGAN TECHNOL UNIV HOUGHTONMI 49931

This paper discusses practical methods for handling normally distributed random technical (yield) coefficients in linear programs that optimize natural resource allocation and scheduling, These methods are practical in the sense that they are applicable to large-scale real world models and do not require nonlinear solution methods. The paper begins with a description and demonstration of postoptimization approaches that are applicable to large, linear problems, and then explores methods for reducing overall risk through land allocation diversification, A central theme of the paper is the importance of providing some sort of allowance for uncertainty when presenting optimization results, which promotes a more realistic view of the problem by analysts and decision makers alike.

关键词： LINEAR programming CHANCE CONSTRAINTS probabilistic programming

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Controlling Procedural Modeling Programs with Stochastically-Ordered Sequential Monte Carlo

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ACM TRANSACTIONS ON GRAPHICS 2015年第4期34卷 1-11页

作者： Ritchie, Daniel Mildenhall, Ben Goodman, Noah D. Hanrahan, Pat Stanford Univ Stanford CA 94305 USA

We present a method for controlling the output of procedural modeling programs using Sequential Monte Carlo (SMC). Previous probabilistic methods for controlling procedural models use Markov Chain Monte Carlo (MCMC), which receives control feedback only for completely-generated models. In contrast, SMC receives feedback incrementally on incomplete models, allowing it to reallocate computational resources and converge quickly. To handle the many possible sequentializations of a structured, recursive procedural modeling program, we develop and prove the correctness of a new SMC variant, Stochastically-Ordered Sequential Monte Carlo (SOSMC). We implement SOSMC for general-purpose programs using a new programming primitive: the stochastic future. Finally, we show that SOSMC reliably generates high-quality outputs for a variety of programs and control scoring functions. For small computational budgets, SOSMC's outputs often score nearly twice as high as those of MCMC or normal SMC.

关键词： Procedural Modeling Directable Randomness probabilistic programming Sequential Monte Carlo

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Bayesian protein superposition using Hamiltonian Monte Carlo 20

Bayesian protein superposition using Hamiltonian Monte Carlo

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20th IEEE International Conference on Bioinformatics and Bioengineering (BIBE)

作者： Moreta, Lys Sanz Al-Sibahi, Ahmad Salim Hamelryck, Thomas Univ Copenhagen Dept Comp Sci Copenhagen Denmark Univ Copenhagen Sect Computat & RNA Biol Bioinformat Ctr Copenhagen Denmark

ISBN: (纸本)9781728195742

Optimally superimposing protein structures is essential to study their structure, function, dynamics and evolution. We present THESEUS NUTS (No U-Turn Sampler), a Bayesian version of the THESEUS model [1]-[3] which relies on maximum likelihood estimation. The probabilistic model interprets each protein as a rotated and translated noisy observation of a latent mean structure. Unlike conventional methods [4], THESEUS takes into account the differences in correlations between the atoms in the structure. This paper extends the previous THESEUS MAP (Maximum A Posteriori) model, [5] to full Bayesian inference by making use of the iterative NUTS [6], a Hamiltonian Monte Carlo method. The model delivers consistent results and is computationally efficient thanks to its implementation in the probabilistic programming language NumpPyro [7], [8] which in turn relies upon JAX [9], a system for high-performance machine learning.

关键词： protein superposition Bayesian modelling probabilistic programming NUTS Hamiltonian Monte Carlo protein structure superposition

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Exact and Efficient Bayesian Inference for Privacy Risk Quantification 21st

Exact and Efficient Bayesian Inference for Privacy Risk Quan...

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21st International Workshop on Software Engineering and Formal Methods (SEFM)

作者： Ronneberg, Rasmus C. Pardo, Raul Wasowski, Andrzej Karlsruhe Inst Technol Karlsruhe Germany It Univ Copenhagen Copenhagen Denmark

ISBN: (纸本)9783031471148;9783031471155

Data analysis has high value both for commercial and research purposes. However, disclosing analysis results may pose severe privacy risk to individuals. Privug is a method to quantify privacy risks of data analytics programs by analyzing their source code. The method uses probability distributions to model attacker knowledge and Bayesian inference to update said knowledge based on observable outputs. Currently, Privug uses Markov ChainMonte Carlo (MCMC) to perform inference, which is a flexible but approximate solution. This paper presents an exact Bayesian inference engine based on multivariate Gaussian distributions to accurately and efficiently quantify privacy risks. The inference engine is implemented for a subset of Python programs that can be modeled as multivariate Gaussian models. We evaluate the method by analyzing privacy risks in programs to release public statistics. The evaluation shows that our method accurately and efficiently analyzes privacy risks, and outperforms existing methods. Furthermore, we demonstrate the use of our engine to analyze the effect of differential privacy in public statistics.

关键词： Privacy risk analysis Bayesian inference probabilistic programming

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A report on the Workshop Software Engineering for the Uncertain World 2020

A report on the Workshop Software Engineering for the Uncert...

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13th Innovations in Software Engineering Conference (ISEC)

作者： Kulkarni, Vinay TCS Res Res & Dev Pune Maharashtra India

ISBN: (纸本)9781450375948

Traditionally, software systems have been used to derive mechanical advantage through automation. The underlying assumptions being: objectives for the software system and the environment within which it will operate will remain largely unchanged;and the required information is available fully and with total certainty. Software development is then viewed as a refinement exercise from high-level human-understandable requirements to a deterministic machine-executable implementation. However, for a variety of reasons, these assumptions no longer hold. This calls for a new look at engineering software that's expected to deliver on the stated objectives in an everchanging environment characterized with partial information and inherent uncertainty. The workshop aims to brainstorm this emerging challenge of "Software Engineering for the Uncertain World".

关键词： Program specification and modeling languages System modeling and simulation Modeling of uncertainty probabilistic programming Learning-capable languages and software architectures Knowledge modeling and the integration of AI with Software Engineering Analysis and system verification in presence of uncertainty Adaptive systems

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A Model-Learner Pattern for Bayesian Reasoning

A Model-Learner Pattern for Bayesian Reasoning

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40th ACM SIGPLAN-SIGACT Symposium on Principles of programming Languages

作者： Gordon, Andrew D. Aizatulin, Mihhail Borgstrom, Johannes Claret, Guillaume Graepel, Thore Nori, Aditya V. Rajamani, Sriram K. Russo, Claudio Univ Edinburgh Edinburgh EH8 9YL Midlothian Scotland Open Univ Milton Keynes Bucks England Uppsala Univ Uppsala Sweden

ISBN: (纸本)9781450318327

A Bayesian model is based on a pair of probability distributions, known as the prior and sampling distributions. A wide range of fundamental machine learning tasks, including regression, classification, clustering, and many others, can all be seen as Bayesian models. We propose a new probabilistic programming abstraction, a typed Bayesian model, based on a pair of probabilistic expressions for the prior and sampling distributions. A sampler for a model is an algorithm to compute synthetic data from its sampling distribution, while a learner for a model is an algorithm for probabilistic inference on the model. Models, samplers, and learners form a generic programming pattern for model-based inference. They support the uniform expression of common tasks including model testing, and generic compositions such as mixture models, evidence-based model averaging, and mixtures of experts. A formal semantics supports reasoning about model equivalence and implementation correctness. By developing a series of examples and three learner implementations based on exact inference, factor graphs, and Markov chain Monte Carlo, we demonstrate the broad applicability of this new programming pattern.

关键词： Bayesian reasoning machine learning model-learner pattern probabilistic programming

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