Formalization and analysis of the REST architecture from the process algebra perspective

Wu, Xi; Zhu, Huibiao

doi:10.1016/j.future.2015.09.007

Cited by 14 publications

(2 citation statements)

References 11 publications

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“…Formal modeling and reasoning has long been recognized to be superior in signifying the essence of things [13][14][15]. This is of great importance, since it enables an agent to make decisions according to their understanding of what has occurred and to reason about the underlying games like human beings, and also helps outsiders to analyze or predict what types of decisions the agent could or would make [16].…”

Section: Introductionmentioning

confidence: 99%

A Formal Representation for Intelligent Decision-Making in Games

Liu,

Zhang,

Zhang

et al. 2023

Mathematics

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The study of intelligent game-playing has gained tremendous attention in the past few decades. The recent development of artificial intelligence (AI) players (e.g., the Go player AlphaGo) has made intelligent game-playing even more prominent in both academia and industry. The performance of state-of-the-art AI players benefits greatly from machine learning techniques, based on which, players can make estimations and decisions even without understanding the games. Although AI machines show great superiority over humans in terms of data processing and complex computation, there remains a vast distance between artificial intelligence and human intelligence with respect to the abilities of context understanding and reasoning. In this paper, we explore the theoretical foundation of intelligent game-playing from a logical perspective. The proposed logic, by considering the computational limits in practical game-playing, drops the ideal assumptions in existing logics for the classical game model. We show that under logical framework, the basis of decision-making for agents in game scenarios can be formally represented and analyzed. Moreover, by characterizing the solutions of games, this logic is able to formalize players’ rational decision-making during practical game-playing.

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Section: Introductionmentioning

confidence: 99%

A Formal Representation for Intelligent Decision-Making in Games

Liu,

Zhang,

Zhang

et al. 2023

Mathematics

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“…Formal methods are based on mathematical logic. Generally, formal methods can be divided into formal specification and formal verification [16].…”

Section: Introductionmentioning

confidence: 99%

Formal Verification of a Hybrid Machine Learning-Based Fault Prediction Model in Internet of Things Applications

et al. 2020

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By increasing the complexity of the Internet of Things (IoT) applications, fault prediction become an important challenge in interactions between human, and smart devices. Fault prediction is one of the key factors to achieve better arranging the IoT applications. Most of the current research studies evaluated the fault prediction methods using simulation environments. However, formal verification of the correctness of a fault prediction method has not been reported yet. This paper presents a behavioral modeling and formal verification of a hybrid machine learning-based fault prediction model with Multi-Layer Perceptron (MLP) and Particle Swarm Optimization (PSO) algorithms. In particular, the PSO is used for feature selection. Then, the fault prediction is considered as a behavior to be verified formally. The fault prediction behavior is divided into two types of behaviors: dimension reduction behavior and prediction behavior. For each of the behaviors, one formal model is designed. The behavioral models designed are mapped into the Labeled Transition System (LTS). The Process Analysis Toolkit (PAT) model checker is employed to evaluate the behavioral models. The accuracy of the fault prediction method is done by some existing specifications such as deadlock-free and reachability properties in terms of linear temporal logic formulas. Also, the verification of the fault prediction behaviors is used to detect the defect metrics of information-centric IoT applications. Experimental results showed that our proposed verification method has minimum verification time and memory usage for evaluating critical specification rules than other research studies. INDEX TERMS Internet of Things applications, fault prediction, formal verification, process analysis toolkit, multi-layer perceptron, particle swarm optimization.

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CPN Model Based Standard Feature Verification Method for REST Service Architecture

Liu

Zhao

2019

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Formalization and analysis of the REST architecture from the process algebra perspective

Cited by 14 publications

References 11 publications

A Formal Representation for Intelligent Decision-Making in Games

A Formal Representation for Intelligent Decision-Making in Games

Formal Verification of a Hybrid Machine Learning-Based Fault Prediction Model in Internet of Things Applications

CPN Model Based Standard Feature Verification Method for REST Service Architecture

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