scholarly journals Inductive Synthesis for Probabilistic Programs Reaches New Horizons

2021 ◽  
pp. 191-209
Author(s):  
Roman Andriushchenko ◽  
Milan Češka ◽  
Sebastian Junges ◽  
Joost-Pieter Katoen
Keyword(s):  
Deductive Reasoning ◽  
Synthesis Process ◽  
Worst Case ◽  
New Horizons ◽  
The Family ◽  
Pruning Strategy ◽  
Finite State ◽  
Novel Method ◽  
Partially Observable ◽  
Probabilistic Programs

AbstractThis paper presents a novel method for the automated synthesis of probabilistic programs. The starting point is a program sketch representing a finite family of finite-state Markov chains with related but distinct topologies, and a reachability specification. The method builds on a novel inductive oracle that greedily generates counter-examples (CEs) for violating programs and uses them to prune the family. These CEs leverage the semantics of the family in the form of bounds on its best- and worst-case behaviour provided by a deductive oracle using an MDP abstraction. The method further monitors the performance of the synthesis and adaptively switches between inductive and deductive reasoning. Our experiments demonstrate that the novel CE construction provides a significantly faster and more effective pruning strategy leading to an accelerated synthesis process on a wide range of benchmarks. For challenging problems, such as the synthesis of decentralized partially-observable controllers, we reduce the run-time from a day to minutes.

scholarly journals Goal Recognition Design in Deterministic Environments

2019 ◽  
Vol 65 ◽  
pp. 209-269
Author(s):  
Sarah Keren ◽  
Avigdor Gal ◽  
Erez Karpas
Keyword(s):  
Computer Games ◽  
Empirical Evaluation ◽  
Recognition System ◽  
Model Quality ◽  
Worst Case ◽  
Pruning Strategy ◽  
Goal Recognition ◽  
Optimizing Model ◽  
Partially Observable ◽  
Human Robot Collaboration

Goal recognition design (GRD) facilitates understanding the goals of acting agents through the analysis and redesign of goal recognition models, thus offering a solution for assessing and minimizing the maximal progress of any agent in the model before goal recognition is guaranteed. In a nutshell, given a model of a domain and a set of possible goals, a solution to a GRD problem determines (1) the extent to which actions performed by an agent within the model reveal the agent’s objective; and (2) how best to modify the model so that the objective of an agent can be detected as early as possible. This approach is relevant to any domain in which rapid goal recognition is essential and the model design can be controlled. Applications include intrusion detection, assisted cognition, computer games, and human-robot collaboration. A GRD problem has two components: the analyzed goal recognition setting, and a design model specifying the possible ways the environment in which agents act can be modified so as to facilitate recognition. This work formulates a general framework for GRD in deterministic and partially observable environments, and offers a toolbox of solutions for evaluating and optimizing model quality for various settings. For the purpose of evaluation we suggest the worst case distinctiveness (WCD) measure, which represents the maximal cost of a path an agent may follow before its goal can be inferred by a goal recognition system. We offer novel compilations to classical planning for calculating WCD in settings where agents are bounded-suboptimal. We then suggest methods for minimizing WCD by searching for an optimal redesign strategy within the space of possible modifications, and using pruning to increase efficiency. We support our approach with an empirical evaluation that measures WCD in a variety of GRD settings and tests the efficiency of our compilation-based methods for computing it. We also examine the effectiveness of reducing WCD via redesign and the performance gain brought about by our proposed pruning strategy.

AN MPI PERFORMANCE MONITORING INTERFACE FOR CELL BASED COMPUTE NODES

2009 ◽  
Vol 19 (04) ◽  
pp. 535-552
Author(s):  
HIKMET DURSUN ◽  
KEVIN J. BARKER ◽  
DARREN J. KERBYSON ◽  
SCOTT PAKIN ◽  
RICHARD SEYMOUR ◽  
...  
Keyword(s):  
Performance Monitoring ◽  
Parallel Applications ◽  
Cell Processor ◽  
Worst Case ◽  
Application Performance ◽  
Cell Broadband Engine ◽  
The Family ◽  
Function Call ◽  
Local Store ◽  
And Function

In this paper, we present a methodology for profiling parallel applications executing on the family of architectures commonly referred as the "Cell" processor. Specifically, we examine Cell-centric MPI programs on hybrid clusters containing multiple Opteron and IBM PowerXCell 8i processors per node such as those used in the petascale Roadrunner system. We analyze the performance of our approach on a PlayStation3 console based on Cell Broadband Engine—the CBE—as well as an IBM BladeCenter QS22 based on PowerXCell 8i. Our implementation incurs less than 0.5% overhead and 0.3 µs per profiler call for a typical molecular dynamics code on the Cell BE while efficiently utilizing the limited local store of the Cell's SPE cores. Our worst-case overhead analysis on the PowerXCell 8i costs 3.2 µs per profiler call while using only two 5 KiB buffers. We demonstrate the use of our profiler on a cluster of hybrid nodes running a suite of scientific applications. Our analyses of inter-SPE communication (across the entire cluster) and function call patterns provide valuable information that can be used to optimize application performance.

scholarly journals PAYNT: A Tool for Inductive Synthesis of Probabilistic Programs

2021 ◽  
pp. 856-869
Author(s):  
Roman Andriushchenko ◽  
Milan Češka ◽  
Sebastian Junges ◽  
Joost-Pieter Katoen ◽  
Šimon Stupinský
Keyword(s):  
Model Checking ◽  
Case Studies ◽  
Temporal Logic ◽  
Probabilistic Model ◽  
Finite Family ◽  
Probabilistic Model Checking ◽  
Inductive Synthesis ◽  
Finite State ◽  
Alternative Approaches ◽  
Probabilistic Programs

AbstractThis paper presents PAYNT, a tool to automatically synthesise probabilistic programs. PAYNT enables the synthesis of finite-state probabilistic programs from a program sketch representing a finite family of program candidates. A tight interaction between inductive oracle-guided methods with state-of-the-art probabilistic model checking is at the heart of PAYNT. These oracle-guided methods effectively reason about all possible candidates and synthesise programs that meet a given specification formulated as a conjunction of temporal logic constraints and possibly including an optimising objective. We demonstrate the performance and usefulness of PAYNT using several case studies from different application domains; e.g., we find the optimal randomized protocol for network stabilisation among 3M potential programs within minutes, whereas alternative approaches would need days to do so.

N-WAY COMPOSITION OF WEIGHTED FINITE-STATE TRANSDUCERS

2009 ◽  
Vol 20 (04) ◽  
pp. 613-627 ◽  
Author(s):  
CYRIL ALLAUZEN ◽  
MEHRYAR MOHRI
Keyword(s):  
Speech Synthesis ◽  
Linear Time ◽  
Worst Case ◽  
String Kernels ◽  
Processing Step ◽  
Worst Case Complexity ◽  
Finite State ◽  
Standard Composition ◽  
Weighted Finite State Transducers ◽  
Expected Complexity

Composition of weighted transducers is a fundamental algorithm used in many applications, including for computing complex edit-distances between automata, or string kernels in machine learning, or to combine different components of a speech recognition, speech synthesis, or information extraction system. We present a generalization of the composition of weighted transducers, n-way composition, which is dramatically faster in practice than the standard composition algorithm when combining more than two transducers. The worst-case complexity of our algorithm for composing three transducers T1, T2, and T3 resulting in T, is O(|T|Q min (d(T1)d(T3), d(T2)) + |T|E), where |·|Q denotes the number of states, |·|E the number of transitions, and d(·) the maximum out-degree. As in regular composition, the use of perfect hashing requires a pre-processing step with linear-time expected complexity in the size of the input transducers. In many cases, this approach significantly improves on the complexity of standard composition. Our algorithm also leads to a dramatically faster composition in practice. Furthermore, standard composition can be obtained as a special case of our algorithm. We report the results of several experiments demonstrating this improvement. These theoretical and empirical improvements significantly enhance performance in the applications already mentioned.

HFSM-Based Software Implementation of Haptic Interactions

2000 ◽  
Author(s):  
I. Chau ◽  
S. E. Salcudean ◽  
D. K. Pai
Keyword(s):  
Application Programming Interface ◽  
Software Implementation ◽  
Haptic Interaction ◽  
Dynamic Constraints ◽  
Haptic Interactions ◽  
Finite State ◽  
Application Programming ◽  
Novel Method ◽  
Three Degree Of Freedom ◽  
Programming Interface

Abstract We present a novel method to encapsulate and formalize haptic interaction in a compact systematic format using hierarchical finite state machines (HFSMs). HFSMs capture both reality-based and synthesized haptic interactions. The lowest level states in the hierarchy are impedances implemented by the haptic device. Transitions between states are governed by inequalities defining geometric and dynamic constraints. This model is compatible with other haptic rendering techniques and can be used as a low level application programming interface. We will describe the format and implementation and illustrate the approach with an example. Experimental results with a three-degree-of-freedom planar haptic interface are also presented.

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