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Declarative programming for agent applications

AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS 2011年第2期23卷 224-272页

作者： Lloyd, J. W. Ng, K. S. Australian Natl Univ Coll Engn & Comp Sci Canberra ACT Australia

This paper introduces the execution model of a declarative programming language intended for agent applications. Features supported by the language include functional and logic programming idioms, higher-order functions, modal computation, probabilistic computation, and some theorem-proving capabilities. The need for these features is motivated and examples are given to illustrate the central ideas.

关键词： Declarative programming Multi-agent systems Multi-modal logic Higher-order logic Modal programming probabilistic programming

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UNCERTAIN<T>: ABSTRACTIONS FOR UNCERTAIN HARDWARE AND SOFTWARE

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IEEE MICRO 2015年第3期35卷 132-143页

作者： Bornholt, James Mytkowicz, Todd McKinley, Kathryn S. Univ Washington Dept Comp Sci & Engn Seattle WA 98195 USA

BUILDING CORRECT, EFFICIENT SYSTEMS THAT REASON ABOUT THE APPROXIMATIONS PRODUCED BY SENSORS, MACHINE LEARNING, BIG DATA, HUMANS, AND APPROXIMATE HARDWARE AND SOFTWARE REQUIRES NEW STANDARDS AND ABSTRACTIONS. THE UNCERTAIN < T > SOFTWARE ABSTRACTION AIMS TO TACKLE THESE PERVASIVE CORRECTNESS, OPTIMIZATION, AND PROGRAMMABILITY PROBLEMS AND GUIDE HARDWARE AND SOFTWARE DESIGNERS IN PRODUCING ESTIMATES.

关键词： Big Data Hardware Software Codesign Learning Artificial Intelligence Uncertain Hardware Uncertain Software Sensors Machine Learning Big Data Software Abstraction Programmability Problems Global Positioning System Uncertainty Energy Efficiency Sensors programming Bayes Methods probabilistic programming Uncertainty Estimates Energy Efficiency programming Models Approximation probabilistic programming

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Scenic: a language for scenario specification and data generation

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MACHINE LEARNING 2023年第10期112卷 3805-3849页

作者： Fremont, Daniel J. Kim, Edward Dreossi, Tommaso Ghosh, Shromona Yue, Xiangyu Sangiovanni-Vincentelli, Alberto L. Seshia, Sanjit A. Univ Calif Santa Cruz Santa Cruz CA 95064 USA Univ Calif Berkeley Berkeley CA 94720 USA Insitro San Francisco CA USA Waymo LLC Mountain View CA USA

We propose a new probabilistic programming language for the design and analysis of cyber-physical systems, especially those based on machine learning. We consider several problems arising in the design process, including training a system to be robust to rare events, testing its performance under different conditions, and debugging failures. We show how a probabilistic programming language can help address these problems by specifying distributions encoding interesting types of inputs, then sampling these to generate specialized training and test data. More generally, such languages can be used to write environment models, an essential prerequisite to any formal analysis. In this paper, we focus on systems such as autonomous cars and robots, whose environment at any point in time is a scene, a configuration of physical objects and agents. We design a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time. Scenic combines concise, readable syntax for spatiotemporal relationships with the ability to declaratively impose hard and soft constraints over the scenario. We develop specialized techniques for sampling from the resulting distribution, taking advantage of the structure provided by Scenic's domain-specific syntax. Finally, we apply Scenic in multiple case studies for training, testing, and debugging neural networks for perception both as standalone components and within the context of a full cyber-physical system.

关键词： Scenario description language Synthetic data Deep learning probabilistic programming Debugging Automatic test generation Simulation D 2 5 D 3 2 I 2 6 I 2 9

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The asymptotic value-to-capacity ratio for the multi-class stochastic knapsack problem

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 1997年第3期103卷 584-594页

作者： Lee, TE Oh, GT UNIV SUWON DEPT IND ENGNKYUNGGI DO 445743SOUTH KOREA

We consider a stochastic knapsack problem that packs multiple classes of random items. The pairs of profit and resource requirement for items of the same class are independent and identically distributed. However, such pairs for different classes of items are independent but not necessarily identically distributed. We investigate convergence and the asymptotic value of the ratio of the knapsack value function to the capacity when the capacity increases. We consider two growth models for the multi-class stochastic knapsack problem, one where the number of available items grows proportionally to the capacity and the other one where the available items are sampled until their total resource requirement does not exceed the capacity. By extending the results of Meanti et al. on a single class of random items, we show that for each growth model, the ratio converges almost surely to a computable constant. For special cases where resource requirements or profit coefficients are deterministic, we present simple interpretations on the asymptotic values. Finally, we present an exponential order upperbound on the tail probability of the ratio. (C) 1997 Elsevier Science B.V.

关键词： probabilistic programming stochastic knapsack problem multiple classes

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Chance constrained optimization of process systems under uncertainty: I. Strict monotonicity

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COMPUTERS & CHEMICAL ENGINEERING 2009年第10期33卷 1568-1583页

作者： Arellano-Garcia, Harvey Wozny, Guenter Berlin Inst Technol Chair Proc Dynam & Operat D-10623 Berlin Germany

An approach for chance constrained programming of large-scale nonlinear dynamic systems is presented. The stochastic property of the uncertainties is explicitly considered in the problem formulation in which some input and state constraints are to be complied with predefined probability levels. The method considers a nonlinear relation between the uncertain input and the constrained variables. It also involves efficient algorithms so as to compute the probabilities and, simultaneously, the gradients through integration by collocation in finite elements. The formulation of single or joint probability limits incorporates the issue of feasibility and the contemplation of trade-off between robustness and profitability regarding the objective function values. The approach is relevant to all cases when uncertainty can be described by any kind of joint correlated multivariate distribution function. Thus, chance constrained programming is a promising technique in solving optimization problems under uncertainty in system engineering. The potential and the efficiency of the presented systematic methodology, which assumes a strict monotonic relationship between the uncertain input and the uncertain constrained output, are illustrated with application to a reactive batch distillation processes under uncertainty. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Uncertainty Chance constraints Dynamic optimization probabilistic programming Batch distillation

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probabilistically Programmed STT-MRAM

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IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS 2012年第1期2卷 42-51页

作者： Wu, Wenqing Zhu, Xiaochun Kang, Seung Yuen, Kendrick Gilmore, Rob Qualcomm Inc Corp R&D Div San Diego CA 92121 USA Qualcomm Inc QCT Div San Diego CA 92121 USA

Novel memory programming methods and corresponding memory structures are presented in this paper. Unlike conventional memory programming, this programming technique does not require deterministic switching of memory elements. This technique explicitly exploits the probabilistic switching characteristics of memory elements such as spin-transfer torque magnetic tunnel junction (STT-MTJ) to reduce programming power and delay. This technique also allows multilevel cell (MLC) spin-transfer torque magnetoresistive random access memory (STT-MRAM) to be fabricated with existing STT-MTJ fabrication processes, thus making high capacity STT-MRAM chips readily achievable. The optimal STT-MTJ switching probabilities are given in this paper for reaching minimum programming delay, power, and iteration. Moreover, this paper proves, by applying probabilistic programming to existing STT-MTJs, both programming delay and power can be reduced to levels beyond the reach of conventional deterministic programming. Furthermore arbitrarily small programming bit error rate (BER) can be accomplished in theory using probabilistic programming without much penalty on average programming delay and power. On the contrary, deterministic programming always presents finite programming BER, which is expensive to reduce in terms of programming power and delay. The MLC capability of STT-MTJ clusters has also been confirmed using fabricated STT-MTJ devices. The major circuitries for implementing probabilistically programmed MLC STT-MRAM are also presented in this paper.

关键词： Arbitrarily small programming bit error rate (BER) high capacity spin-transfer torque magnetoresistive random access memory (STT-MRAM) chips low power STT-MRAM multilevel cell (MLC) memory probabilistic programming stochastic switching

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Determination of optimal path under approach and exit constraints

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 1999年第2期117卷 310-325页

作者： Khan, WA Hayhurst, DR Cannings, C GIK Inst Engn Sci & Technol NWFP Fac Mech Engn Topi 23460 Pakistan Univ Manchester Inst Sci & Technol Dept Mech Engn Manchester M60 1QD Lancs England Univ Sheffield Sch Math & Stat Probabil & Stat Sect Sheffield S10 2UN S Yorkshire England

To economize machining process used in component manufacturing number of procedures an used. Typical parameters which are optimized are feed rate, spindle speed, depth of cut, machining time etc. Almost no consideration is given to non-productive machining time, which is an important parameter on modern computer numerical control machine tools. Its importance is further augmented in the area of numerically controlled cutting where surface area to thickness ratio is high. The problem is formulated as a large scale traveling salesman problem. The cases of symmetric, asymmetric and symmetric asymmetric TSP in two dimensions are presented. The stochastic search procedure simulated annealing algorithm is used to solve these instances of TSP. The perturbation scheme is modified for asymmetric graph and mixed symmetric asymmetric graph TSPs. An investigation is also carried out for empirically finding a suitable value of acceptance probability for random topology of nodes. Effect of problem size and node distribution on the convergence is also monitored. Solution of symmetric, asymmetric and mixed symmetric asymmetric TSPs are provided. This solution allows the optimization of non-productive movement thus reducing the machine tool resident time and the power consumption. The solution is also applicable to a number of other areas such as multi axes production machinery, pick and place technology, and quality control machines. (C) 1999 Elsevier Science B.V. All rights reserved.

关键词： combinatorial analysis probabilistic programming production routing traveling salesman

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A landscape and implementation framework for probabilistic rough sets using PROBLOG

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INFORMATION SCIENCES 2022年 593卷 546-576页

作者： Doherty, Patrick Szalas, Andrzej Jinan Univ Sch Intelligent Syst & Engn Zhuhai Campus Zhuhai Peoples R China Linkoping Univ Dept Comp & Informat Sci SE-58183 Linkoping Sweden Univ Warsaw Inst Informat Banacha 2 PL-02097 Warsaw Poland

Reasoning about uncertainty is one of the main cornerstones of Knowledge Representation. More recently, combining logic with probability has been of major interest. Rough set methods have been proposed for modeling incompleteness and imprecision based on indiscernibility and its generalizations and there is a large body of work in this direction. More recently, the classical theory has been generalized to include probabilistic rough set methods of which there are also a great variety of proposals. Pragmatic, easily accessible, and easy to use tools for specification and reasoning with this wide variety of methods is lacking. It is the purpose of this paper to fill in that gap where the focus will be on probabilistic rough set methods. A landscape of (probabilistic) rough set reasoning methods and the variety of choices involved in specifying them is surveyed first. While doing this, an abstract generalization of all the considered approaches is derived which subsumes each of the methods. One then shows how, via this generalization, one can specify and reason about any of these methods using PROBLOG, a popular and widely used probabilistic logic programming language based on PROBLOG. The paper also considers new techniques in this context such as the use of probabilistic target sets when defining rough sets and the use of partially specified base relations that are also probabilistic. Additionally, probabilistic approaches using tolerance spaces are proposed. The paper includes a rich set of examples and provides a framework based on a library of generic PROBLOG relations that make specification of any of these methods, straightforward, efficient and compact. Complete, ready to run PROBLOG code is included in the Appendix for all examples considered. (C) 2021 The Author(s). Published by Elsevier Inc.

关键词： probabilistic rough sets ProbLog probabilistic programming Knowledge representation Rule-based languages

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Tree-AMP: Compositional Inference with Tree Approximate Message Passing

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JOURNAL OF MACHINE LEARNING RESEARCH 2023年第1期24卷 1-89页

作者： Baker, Antoine Krzakala, Florent Aubin, Benjamin Zdeborova, Lenka PSL Univ Ecole Normale Super Lab Phys CNRS Paris France Univ Paris Saclay Inst Phys Theor CNRS CEA Saclay France

We introduce Tree-AMP, standing for Tree Approximate Message Passing, a python package for compositional inference in high-dimensional tree-structured models. The package provides a unifying framework to study several approximate message passing algorithms previously derived for a variety of machine learning tasks such as generalized linear models, inference in multi-layer networks, matrix factorization, and reconstruction using non separable penalties. For some models, the asymptotic performance of the algorithm can be theoretically predicted by the state evolution, and the measurements entropy estimated by the free entropy formalism. The implementation is modular by design: each module, which implements a factor, can be composed at will with other modules to solve complex inference tasks. The user only needs to declare the factor graph of the model: the inference algorithm, state evolution and entropy estimation are fully automated. The source code is publicly available at https://***/sphinxteam/tramp and the documentation at

关键词： probabilistic programming graphical models Bethe entropy state evolution expectation propagation

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DERIVING PROBABILITY DENSITY FUNCTIONS FROM probabilistic FUNCTIONAL PROGRAMS

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LOGICAL METHODS IN COMPUTER SCIENCE 2017年第2期13卷

作者： Bhat, Sooraj Borgstrom, Johannes Gordon, Andrew D. Russo, Claudio Georgia Inst Technol Atlanta GA 30332 USA Uppsala Univ Uppsala Sweden Microsoft Res Redmond WA USA Univ Edinburgh Edinburgh Midlothian Scotland

The probability density function of a probability distribution is a fundamental concept in probability theory and a key ingredient in various widely used machine learning methods. However, the necessary framework for compiling probabilistic functional programs to density functions has only recently been developed. In this work, we present a density compiler for a probabilistic language with failure and both discrete and continuous distributions, and provide a proof of its soundness. The compiler greatly reduces the development effort of domain experts, which we demonstrate by solving inference problems from various scientific applications, such as modelling the global carbon cycle, using a standard Markov chain Monte Carlo framework.

关键词： probability density function probabilistic programming Markov chain Monte Carlo

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