scholarly journals Formal verification of neural agents in non-deterministic environments

2021 ◽  
Vol 36 (1) ◽  
Author(s):  
Michael E. Akintunde ◽  
Elena Botoeva ◽  
Panagiotis Kouvaros ◽  
Alessio Lomuscio
Keyword(s):  
Neural Networks ◽  
Parallel Algorithms ◽  
Formal Verification ◽  
Experimental Results ◽  
Use Case ◽  
Feed Forward ◽  
Verification Problem ◽  
Bounded Fragment

AbstractWe introduce a model for agent-environment systems where the agents are implemented via feed-forward ReLU neural networks and the environment is non-deterministic. We study the verification problem of such systems against CTL properties. We show that verifying these systems against reachability properties is undecidable. We introduce a bounded fragment of CTL, show its usefulness in identifying shallow bugs in the system, and prove that the verification problem against specifications in bounded CTL is in coNExpTime and PSpace-hard. We introduce sequential and parallel algorithms for MILP-based verification of agent-environment systems, present an implementation, and report the experimental results obtained against a variant of the VerticalCAS use-case and the frozen lake scenario.

scholarly journals Verification of RNN-Based Neural Agent-Environment Systems

2019 ◽  
Vol 33 ◽  
pp. 6006-6013 ◽  
Author(s):  
Michael E. Akintunde ◽  
Andreea Kevorchian ◽  
Alessio Lomuscio ◽  
Edoardo Pirovano
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Recurrent Neural Network ◽  
Experimental Results ◽  
Feed Forward ◽  
Execution Traces ◽  
Verification Problem ◽  
Complete Procedure ◽  
Feed Forward Neural Networks

We introduce agent-environment systems where the agent is stateful and executing a ReLU recurrent neural network. We define and study their verification problem by providing equivalences of recurrent and feed-forward neural networks on bounded execution traces. We give a sound and complete procedure for their verification against properties specified in a simplified version of LTL on bounded executions. We present an implementation and discuss the experimental results obtained.

EXPLOITING SYMMETRIES FOR TESTING EQUIVALENCE VERIFICATION IN THE SPI CALCULUS

2006 ◽  
Vol 17 (04) ◽  
pp. 815-832
Author(s):  
IVAN CIBRARIO BERTOLOTTI ◽  
LUCA DURANTE ◽  
RICCARDO SISTO ◽  
ADRIANO VALENZANO
Keyword(s):  
Formal Verification ◽  
Cryptographic Protocols ◽  
Transition System ◽  
Experimental Results ◽  
Verification Problem ◽  
Security Properties ◽  
Universal Quantification ◽  
Equivalence Verification ◽  
Testing Equivalence ◽  
Labelled Transition System

Testing equivalence is a quite powerful way of expressing security properties of cryptographic protocols, but its formal verification is a difficult task, because it is based on universal quantification over contexts. A technique based on state exploration to address this verification problem has previously been presented; it relies on an environment-sensitive labelled transition system (ES-LTS) and on symbolic term representation. This paper shows that such a technique can be enhanced by exploiting symmetries found in the ES-LTS structure. Experimental results show that the proposed enhancement can substantially reduce the size of the ES-LTS and that the technique as a whole compares favorably with respect to related work.

Mining discriminative patches for script identification in natural scene images

2021 ◽  
Vol 40 (1) ◽  
pp. 551-563
Author(s):  
Liqiong Lu ◽  
Dong Wu ◽  
Ziwei Tang ◽  
Yaohua Yi ◽  
Faliang Huang
Keyword(s):  
Neural Networks ◽  
Experimental Results ◽  
The Other ◽  
Natural Scene ◽  
Fixed Size ◽  
Script Identification ◽  
Aspect Ratios ◽  
Novel Approach ◽  
Public Datasets ◽  
Natural Scene Images

This paper focuses on script identification in natural scene images. Traditional CNNs (Convolution Neural Networks) cannot solve this problem perfectly for two reasons: one is the arbitrary aspect ratios of scene images which bring much difficulty to traditional CNNs with a fixed size image as the input. And the other is that some scripts with minor differences are easily confused because they share a subset of characters with the same shapes. We propose a novel approach combing Score CNN, Attention CNN and patches. Attention CNN is utilized to determine whether a patch is a discriminative patch and calculate the contribution weight of the discriminative patch to script identification of the whole image. Score CNN uses a discriminative patch as input and predict the score of each script type. Firstly patches with the same size are extracted from the scene images. Secondly these patches are used as inputs to Score CNN and Attention CNN to train two patch-level classifiers. Finally, the results of multiple discriminative patches extracted from the same image via the above two classifiers are fused to obtain the script type of this image. Using patches with the same size as inputs to CNN can avoid the problems caused by arbitrary aspect ratios of scene images. The trained classifiers can mine discriminative patches to accurately identify some confusing scripts. The experimental results show the good performance of our approach on four public datasets.

An Evolutionary Approach to the Use of Neural Networks in the Segmentation of Handwritten Numerals

1997 ◽  
Vol 11 (05) ◽  
pp. 717-734 ◽  
Author(s):  
J. M. Westall ◽  
M. S. Narasimha
Keyword(s):  
Neural Networks ◽  
Production System ◽  
Background Noise ◽  
Evolutionary Approach ◽  
Feed Forward ◽  
Feed Forward Neural Networks ◽  
Decision Functions

Neural networks are now widely and successfully used in the recognition of handwritten numerals. Despite their wide use in recognition, neural networks have not seen widespread use in segmentation. Segmentation can be extremely difficult in the presence of connected numerals, fragmented numerals, and background noise, and its failure is a principal cause of rejected and incorrectly read documents. Therefore, strategies leading to the successful application of neural technologies to segmentation are likely to yield important performance benefits. In this paper we identify problems that have impeded the use of neural networks in segmentation and describe an evolutionary approach to applying neural networks in segmentation. Our approach, based upon the use of monotonic fuzzy valued decision functions computed by feed-forward neural networks, has been successfully employed in a production system.

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