Analyzing the Interaction between Knowledge and Social Commitments in Multi-Agent Systems

Both knowledge and social commitments in Multi-Agent Systems (MASs) have long been under research independently, especially for agent communication. Plenty of work has been carried out to define their semantics. However, in concrete applications such as business settings and web-based applications, agents should reason about their knowledge and their social commitments at the same time, particularly when they are engaged in conversations. In fact, studying the interaction between knowledge and social commitments is still in its beginnings. Therefore, in this thesis, we aim to provide a practical and formal framework that analyzes the interaction between knowledge and communicative social commitments in MASs from the semantics, model checking, complexity, soundness and completeness perspectives. To investigate such an interaction, we, first, combine CTLK (an extension of computation Tree Logic (CTL) with modality for reasoning about knowledge) and CTLC (an extension of CTL with modalities for reasoning about commitments and their fulfillments) in one new logic named CTLKC. By so doing, we identify some paradoxes in the new logic showing that simply combining current versions of commitment and knowledge logics results in a language of logic that violates some fundamental intuitions. Consequently, we propose CTLKC+, a new consistent logic of knowledge and commitments that fixes the identified paradoxes and allows us to reason about social commitments and knowledge simultaneously in a consistent manner. Second, we use correspondence theory for modal logics to prove the soundness and completeness of CTLKC+. To do so, we develop a set of reasoning postulates in CTLKC+ and correspond them to certain classes of frames. The existence of such correspondence allows us to prove that the logic generated by any subset of these postulates is sound and complete, with respect to the models that are based on the corresponding frames. Third, we address the problem of model checking CTLKC+ by transforming it to the problem of model checking GCTL∗ (a generalized version of Extended Computation Tree Logic (CTL∗) with action formulas) and ARCTL (the combination of CTL with action formulas) in order to respectively use the CWB-NC automata-based model checker and the extended NuSMV symbolic model checker. Moreover, we prove that the transformation techniques are sound. Fourth, we analyze the complexity of the proposed model checking techniques. The results of this analysis reveal that the complexity of our transformation procedures is PSPACE-complete for local concurrent programs with respect to the size of these programs and the length of the formula being checked. From the time perspective, we prove that the complexity of the proposed approaches is P-complete with regard to the size of the model and length of the formula. Finally, we implement our model checking approaches and report some experimental results by verifying the well-known NetBell payment protocol against some desirable properties

Automatic Verification of Communicative Commitments using Reduction

In spite of the fact that modeling and verification of the Multi-Agent Systems (MASs) have been since long under study, there are several related challenges that should still be addressed. In effect, several frameworks have been established for modeling and verifying the MASs with regard to communicative commitments. A bulky volume of research has been conducted for defining semantics of these systems. Though, formal verification of these systems is still unresolved research problem. Within this context, this paper presents the CTLcom that reforms the CTLC, i.e., the temporal logic of the commitments, so as to enable reasoning about the commitments and fulfillment.  Moreover, the paper introduces a fully-automated method for verification of the logic by means of trimming down the problem of a model that checks the CTLcom to a problem of a model that checks the GCTL*, which is a generalized version of the CTL* with action formulae. By so doing, we take advantage of the CWB-NC automata-based model checker as a tool for verification. Lastly, this paper presents a case study drawn from the business field, that is, the NetBill protocol, illustrates its implementation, and discusses the associated experimental results in order to illustrate the efficiency and effectiveness of the suggested technique.   Keywords: Multi-Agent Systems, Model Checking, Communicative commitment's, Reduction

A Holistic Model for Recognition of Handwritten Arabic Text Based on the Local Binary Pattern Technique

In this paper, we introduce a multi-stage offline holistic handwritten Arabic text recognition model using the Local Binary Pattern (LBP) technique and two machine-learning approaches; Support Vector Machines (SVM) and Artificial Neural Network (ANN). In this model, the LBP method is utilized for extracting the global text features without text segmentation. The suggested model was tested and utilized on version II of the IFN/ENIT database applying the polynomial, linear, and Gaussian SVM and ANN classifiers. Performance of the ANN was assessed using the Levenberg-Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG) training methods. The classification outputs of the herein suggested model were compared and verified with the results obtained from two benchmark Arabic text recognition models (ATRSs) that are based on the Discrete Cosine Transform (DCT) and Principal Component Analysis (PCA) methods using various normalization sizes of images of Arabic text. The classification outcomes of the suggested model are promising and better than the outcomes of the examined benchmarks models. The best classification accuracies of the suggested model (97.46% and 94.92%) are obtained using the polynomial SVM classifier and the BR ANN training methods, respectively

A Holistic Model for Recognition of Handwritten Arabic Text Based on the Local Binary Pattern Technique

In this paper, we introduce a multi-stage offline holistic handwritten Arabic text recognition model using the Local Binary Pattern (LBP) technique and two machine-learning approaches; Support Vector Machines (SVM) and Artificial Neural Network (ANN). In this model, the LBP method is utilized for extracting the global text features without text segmentation. The suggested model was tested and utilized on version II of the IFN/ENIT database applying the polynomial, linear, and Gaussian SVM and ANN classifiers. Performance of the ANN was assessed using the Levenberg-Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG) training methods. The classification outputs of the herein suggested model were compared and verified with the results obtained from two benchmark Arabic text recognition models (ATRSs) that are based on the Discrete Cosine Transform (DCT) and Principal Component Analysis (PCA) methods using various normalization sizes of images of Arabic text. The classification outcomes of the suggested model are promising and better than the outcomes of the examined benchmarks models. The best classification accuracies of the suggested model (97.46% and 94.92%) are obtained using the polynomial SVM classifier and the BR ANN training methods, respectively.</p

Reduction Model Checking for Multi-Agent Systems of Group Social Commitments

Innumerable industries now use multi-agent systems (MASs) in various contexts, including healthcare, security, and commercial deployments. It is challenging to select reliable business protocols for critically important safety-related systems (e.g., in healthcare). The verification and validation of business applications is increasingly explored concerning multi-agent systems’ group social commitments. This study explains a novel extended reduction verification method to model-check business applications’ critical specification rules using action restricted computation tree logic (ARCTL). In particular, we aim to conduct the verification process for the CTLGC logic using a reduction algorithm and show its effectiveness to handle MASs with huge models, thus, showing its importance and applicability in large real-world applications. To do so, we need to transform the CTLGC model to an ARCTL model and the CTLGC formulas into ARCTL formulas. Thus, the developed method was verified with the model-checker new symbolic model verifier (NuSMV), and it demonstrated effectiveness in the safety-critical specification rule support provision. The proposed method can verify up to 2.43462 × 1014 states MASs, which shows its effectiveness when applied to real-world applications

Multi-Resolution Discrete Cosine Transform Fusion Technique Face Recognition Model

This work presents a Multi-Resolution Discrete Cosine Transform (MDCT) fusion technique Fusion Feature-Level Face Recognition Model (FFLFRM) comprising face detection, feature extraction, feature fusion, and face classification. It detects core facial characteristics as well as local and global features utilizing Local Binary Pattern (LBP) and Principal Component Analysis (PCA) extraction. MDCT fusion technique was applied, followed by Artificial Neural Network (ANN) classification. Model testing used 10,000 faces derived from the Olivetti Research Laboratory (ORL) library. Model performance was evaluated in comparison with three state-of-the-art models depending on Frequency Partition (FP), Laplacian Pyramid (LP) and Covariance Intersection (CI) fusion techniques, in terms of image features (low-resolution issues and occlusion) and facial characteristics (pose, and expression per se and in relation to illumination). The MDCT-based model yielded promising recognition results, with a 97.70% accuracy demonstrating effectiveness and robustness for challenges. Furthermore, this work proved that the MDCT method used by the proposed FFLFRM is simpler, faster, and more accurate than the Discrete Fourier Transform (DFT), Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT). As well as that it is an effective method for facial real-life applications

Artificial Ecosystem-Based Optimization with Dwarf Mongoose Optimization for Feature Selection and Global Optimization Problems

Meta-Heuristic (MH) algorithms have recently proven successful in a broad range of applications because of their strong capabilities in picking the optimal features and removing redundant and irrelevant features. Artificial Ecosystem-based Optimization (AEO) shows extraordinary ability in the exploration stage and poor exploitation because of its stochastic nature. Dwarf Mongoose Optimization Algorithm (DMOA) is a recent MH algorithm showing a high exploitation capability. This paper proposes AEO-DMOA Feature Selection (FS) by integrating AEO and DMOA to develop an efficient FS algorithm with a better equilibrium between exploration and exploitation. The performance of the AEO-DMOA is investigated on seven datasets from different domains and a collection of twenty-eight global optimization functions, eighteen CEC2017, and ten CEC2019 benchmark functions. Comparative study and statistical analysis demonstrate that AEO-DMOA gives competitive results and is statistically significant compared to other popular MH approaches. The benchmark function results also indicate enhanced performance in high-dimensional search space.Funding Agencies|Linkoeping University</p