Reasoning In Intelligent Systems Research Articles

Visual analytics and intelligent reasoning for smart manufacturing defect detection and judgement: A meta-learning approach with knowledge graph embedding case-based reasoning

Visual inspection is an important procedure in manufacturing industries to guarantee product quality and reduce costs of poor quality. Several challenges exist for manufacturing inspection automation, including the false positive and false negative trade-off, autonomous decision-making, and inspection algorithm selection. This paper addresses these challenges by proposing a two-level visual analytics and intelligent reasoning system, where machine vision identifies and locates potential defects and convolutional neural network models further classify them into acceptable and defective features. Symbolic reasoning techniques are applied for autonomous decision making on part-level judgement to enhance the system reasoning capability, including part judgement (accept, reject, rework) and rework operation assignment. Furthermore, a case-based reasoning approach for meta-learning of inspection algorithm selection is proposed, which uses knowledge graph embedding (TransR embedding) to embed cases into a two-dimensional space for case similarity estimation and case retrieval. A case of smart assembly inspection is presented to validate the feasibility of the proposed system.

Research of intelligent reasoning system of Arabidopsis thaliana phenotype based on automated multi-task machine learning.

Traditional machine learning in plant phenotyping research requires the assistance of professional data scientists and domain experts to adjust the structure and hy-perparameters tuning of neural network models with much human intervention, making the model training and deployment ineffective. In this paper, the automated machine learning method is researched to construct a multi-task learning model for Arabidopsis thaliana genotype classification, leaf number, and leaf area regression tasks. The experimental results show that the genotype classification task's accuracy and recall achieved 98.78%, precision reached 98.83%, and classification F 1 value reached 98.79%, as well as the R 2 of leaf number regression task and leaf area regression task reached 0.9925 and 0.9997 respectively. The experimental results demonstrated that the multi-task automated machine learning model can combine the benefits of multi-task learning and automated machine learning, which achieved more bias information from related tasks and improved the overall classification and prediction effect. Additionally, the model can be created automatically and has a high degree of generalization for better phenotype reasoning. In addition, the trained model and system can be deployed on cloud platforms for convenient application.

Integrative decision support for accident emergency response by combining MFM and Go-Flow

Emergency response planning associated with written procedures play a key role in the process safety to ensure that accident can be properly managed. Although the extensive use of digital techniques have significantly reduced the accident risk by early warnings and operation guidance, the Fukushima nuclear disaster reminds us that the unexpected events may still exceed existing system capability to indicate appropriate responding measures. A novel digitalized approach is hence required for planning decision support. This article presents a joint utilizing of two modeling and analysis methodologies, which are complementary to each other to provide comprehensive insights for decision-making in the accident emergency response. Multilevel Flow Modeling (MFM) is applied to develop an intelligent reasoning system, which can harness plant’s qualitative functional knowledge to search available equipments and corresponding action sequences potentially being able to manage the accident. Go-Flow is a success-oriented reliability analysis method specially suitable for quantitative risk assessment in the situations involving configuration changes. The underlying configuration of each MFM-driven emergency response measure will update the understanding of goal achievement and modify the Go-Flow chart, by which plan’s effectiveness of risk reduction can be evaluated. Moreover, Go-Flow is also used to conduct the risk matrix assessment for identifying critical components, which can provide a reference for effective assignment of resources in the long-term accident management. This work is expected to strengthen operator’s ability of emergency response planning from perspective of both measure development and optimization. An accident scenario similar to what occurred in the Fukushima Daiichi nuclear power plant has been used to demonstrate the application of the propose approach.

A novel intelligent reasoning system to estimate energy consumption and optimize cutting parameters toward sustainable machining

As it is hard to estimate the energy consumption and to optimize the cutting parameters in different tool wear status, this paper presents a novel intelligent reasoning system for the milling process. The system consists of three parts including the improved case based reasoning (ICBR), the adaptive neural fuzzy inference system (ANFIS) and the vibration particle swarm optimization (VPSO) algorithm. The ICBR is used for providing accurate estimation of cutting power. The inputs of ICBR are cutting parameters and tool wear status, and the output is cutting power. In ICBR, the similar cases to the inputs are retrieved using K-nearest neighbor and artificial neural network (ANN) methods in the case retrieval stage. In the case reuse stage, the Gaussian fuzzy grey correlation model is proposed to estimate the cutting power based on the retrieved similar cases. The VPSO algorithm is proposed to establish ANN and ANFIS models. With the aid of ANFIS-VPSO method, the optimal cutting parameters can be obtained under different machining conditions. The experimental results have confirmed that the VPSO algorithm has better global optimization ability than PSO and DE algorithms. The cutting speed has the greatest influence on the cutting power and cutting vibration. The estimation accuracy of ICBR is up to 91.7%, which are better than that of standard CBR and other intelligent models. The optimal cutting parameters are verified with an optimization error less than 13.5% by experiment results. The intelligent reasoning system can reduce energy consumption, maintain machine tool stability and improve machining efficiency. As an important platform, this system can realize clean and intelligent production.

ViPED: On-road vehicle passenger detection for autonomous vehicles

This paper is about detecting and counting the passengers of a tracking vehicle using on-car monocular vision. By having a model of nearby vehicle occupants, intelligent reasoning systems of autonomous cars will be provided with this additional knowledge needed in emergency situations such as those that many philosophers have recently raised. The on-road Vehicle PassengEr Detection (ViPED) system is based on the human perception model in terms of spatio-temporal reasoning, namely the slight movements of passenger shape silhouettes inside the cabin. The main challenges we face are the low light conditions of the cabin (no feature points), the subtle non-rigid motions of the occupants (possible artifactual transitions), and the puzzling discrimination problem of back or front seat occupants (lack of depth information inside the cabin). To overcome these challenges, we first track the detected car windshield and find the optimal affine warp. The registered windshield images are preprocessed in order to extract a feature matrix, which serves as input to a Convolutional Neural Network (CNN) for inferring the number and position of passengers. We demonstrate that our low-cost sensor system is able to detect in most cases successfully all the passengers in preceding moving vehicles at various distances and occupancies. Metrics and datasets are included for possible community future work on this new challenging task.

Resolution Method in Linguistic Propositional Logic

In the present paper, the resolution method for a linguistic propositional logic with truth value in a logical algebra - refined hedge algebra, is focused. The preliminaries of refined hedge algebra are given first. Then the syntax and semantic of linguistic propositional are defined. Finally, a resolution method which based on resolution principle in two-valued logic is established. Accordingly, the research in this paper will be helpful support for the application of intelligent reasoning system based on linguistic-valued logic which includes incomparable information.

A Formal Model for Metacognitive Reasoning in Intelligent Systems

This paper presents a formal model of metacognitive reasoning in intelligent systems (IS). The proposed model was named fM2 and uses predicate logic to represent a cycle of reasoning about failures generated in reasoning tasks in an IS. fM2 has mechanisms such as introspective monitoring and meta-level control to perform metacognitive reasoning. fM2 was implemented and validated on an intelligent tutoring system named FUNPRO. The performance metrics of FUNPRO indicate the capacity of fM2 to drastically decrease the reasoning failures produced in the recommendations of FUNPRO. Thus, this paper demonstrates the efficacy of fM2 as a valid tool to improve the performance of the reasoning processes of IS.

Applying Bayesian networks and belief propagation to error correction coding

Telecommunication standards utilise numerous different subsystems to improve the quality of voice and data communications. One of these subsystems is tasked with error detection and control within the transmitted streams. This is accomplished using numerous different error correction codes for various scenarios. This article investigates the use of a Bayesian network as a universal channel decoder for the two main branches of algebraic codes. This graph-based approach is visually intuitive and is found to produce similar results to the best performing decoders in use today. A graphical representation of a multi-path channel equaliser is incorporated into the Bayesian network, obtaining near optimal performance. References G. Colavolpe. On LDPC codes over channels with memory. IEEE T. Wirel. Commun., 5(7):1757–1766, 2006. doi:10.1109/TWC.2006.1673087. C. Douillard, M. Jezequel, C. Berrou, A. Picart, P. Didier, and A. Glavieux. Iterative correction of intersymbol interference: Turbo-equalization. Eur. T. Telecommun., 6(5):507–511, 1995. doi:10.1002/ett.4460060506. R. G. Gallager. Low-density parity-check codes. PhD thesis, Massachusetts Institute of Technology, 1963. http://www.rle.mit.edu/rgallager/documents/ldpc.pdf. R. J. McEliece, D. J. C. MacKay, and J.-F. Cheng. Turbo decoding as an instance of Pearl's ``belief propagation'' algorithm. IEEE J. Sel. Area. Comm., 16(2):140–152, 1998. doi:10.1109/49.661103 H. C. Myburgh, J. C Olivier, and A. J. van Zyl. Reduced complexity turbo equalization using a dynamic bayesian network. EURASIP J. Adv. Sig. Pr., 2012:136, 2012. doi:10.1186/1687-6180-2012-136. J. Pearl. Fusion, propagation, and structuring in belief networks. Artif. Intell., 29(3):241–288, 1986. doi:10.1016/0004-3702(86)90072-X. J. Pearl. Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann Publishers, 1988. J. G. Proakis and D. G. Manolakis. Digital communications McGraw-Hill New York, 1995. International Telecommunication Union. Requirements related to technical performance for IMT-Advanced radio interface(s). Technical report, http://www.itu.int/pub/R-REP-M.2134-2008, 2008. A. J. Viterbi. Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE T. Inform. Theory, 13(2):260–269, 1967. doi:10.1109/TIT.1967.1054010.

Semantic Annotation and Case-Based Retrieval of Fixture Design Case

In order to develop an intelligent Case-based Reasoning system and meet the need of knowledge reuse of fixture design process, ontology technology is integrated in CBR system, Fixture design domain ontology is constructed, and concepts and relations are defined and represented. Semantic annotation of fixture design case is achieved. The similarity estimation approach is presented to resolve the problem of semantic retrieval of fixture design case. Finally, prototype system based on proposed method is introduced and the information retrieval approach is proved efficiently.

Application of Flexible Combine-Clamp in Digital Rapid Production for Aircraft Tube

Aiming at the existing problem of plane tube welding jig manufacturing, many systems developed such as standard components parameterized modeling system, computer intelligent reasoning system, standard components replacement system, accuracy checking system and standard components management system, and also replace the traditional standard samples by three-dimensional model of tube, then realize the digital rapid design and manufacture of combined welding jig.

Rough Mereology as a Tool for Knowledge Discovery and Reasoning in Intelligent Systems: A Survey

In this work, we present an account of our recent results on applications of rough mereology to problems of 1) knowledge granulation; 2) granular preprocessing in knowledge discovery by means of decision rules; 3) spatial reasoning in multi-agent systems in exemplary case of intelligent mobile robotics.

The synthesis of cognitive procedures and the problem of induction

The JSM-method of automated hypotheses generation realizing the automated plausible reasoning in Intelligent Systems is described. It is shown that the method represents specially organized interaction of induction, analogy and abduction. The foundations of exact epistemology for JSM- Intelligent Systems are formulated.

Belief Propagation: An Asymptotically Optimal Algorithm for the Random Assignment Problem

The random assignment problem asks for the minimum-cost perfect matching in the complete n × n bipartite graph 𝒦nn with i.i.d. edge weights, say uniform on [0, 1]. In a remarkable work by Aldous [Aldous, D. 2001. The ζ(2) limit in the random assignment problem. RSA 18 381–418], the optimal cost was shown to converge to ζ(2) as n → ∞, as conjectured by Mézard and Parisi [Mézard, M., G. Parisi. 1987. On the solution of the random link matching problem. J. Phys. 48 1451–1459] through the so-called cavity method. The latter also suggested a nonrigorous decentralized strategy for finding the optimum, which turned out to be an instance of the belief propagation (BP) heuristic discussed by Pearl [Pearl, J. 1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco]. In this paper we use the objective method to analyze the performance of BP as the size of the underlying graph becomes large. Specifically, we establish that the dynamic of BP on 𝒦nn converges in distribution as n → ∞ to an appropriately defined dynamic on the Poisson weighted infinite tree, and we then prove correlation decay for this limiting dynamic. As a consequence, we obtain that BP finds an asymptotically correct assignment in O(n2) time only. This contrasts with both the worst-case upper bound for convergence of BP derived by Bayati et al. [Bayati, M., D. Shah, M. Sharma. 2008. Max-product for maximum weight matching: Convergence, correctness, and LP duality. IEEE Trans. Inform. Theory 54(3) 1241–1251.] and the best-known computational cost of Θ(n3) achieved by Edmonds and Karp's algorithm [Edmonds, J., R. Karp. 1972. Theoretical improvements in algorithmic efficiency for network flow problems. J. ACM 19 248–264].

Development of an intelligent reasoning system to distinguish hunger states in Rainbow trout ( Oncorhynchus mykiss)

In aquaculture it is inherently difficult to monitor animals as they are underwater. Furthermore, feed costs account for 50–60% of production costs. Recently, telemetry technology has advanced such that physiology and behaviour of fish can be monitored in real time, providing the incentive for automated monitoring systems. As muscles become activated, they generate electric signals, known as electromyograms (EMG's). These EMG's can be measured in fish with the use of a radio-transmitter, an EMG tag. Previous studies have demonstrated collection of telemetric physiological and behavioural data on feeding fish; however, automation of the classifying process has not been attempted. The following paper details the use of supervised pattern recognition in successfully classifying (average success rates 85%) fed and fasted fish. In addition, the success rate of quadratic and support vector machine (SVM) classifiers, different feature sets and data sets is examined.

Diagnosis using a first-order stochastic language that learns

We have created a diagnostic/prognostic software tool for the analysis of complex systems, such as monitoring the ''running health'' of helicopter rotor systems. Although our software is not yet deployed for real-time in-flight diagnosis, we have successfully analyzed the data sets of actual helicopter rotor failures supplied to us by the US Navy. In this paper, we discuss both critical techniques supporting the design of our stochastic diagnostic system as well as issues related to its full deployment. We also present four examples of its use. Our diagnostic system, called DBAYES, is composed of a logic-based, first-order, and Turing-complete set of software tools for stochastic modeling. We use this language for modeling time-series data supplied by sensors on mechanical systems. The inference scheme for these software tools is based on a variant of Pearl's loopy belief propagation algorithm [Pearl, P. (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inference. San Francisco, CA: Morgan Kaufmann]. Our language contains variables that can capture general classes of situations, events, and relationships. A Turing-complete language is able to reason about potentially infinite classes and situations, similar to the analysis of dynamic Bayesian networks. Since the inference algorithm is based on a variant of loopy belief propagation, the language includes expectation maximization type learning of parameters in the modeled domain. In this paper we briefly present the theoretical foundations for our first-order stochastic language and then demonstrate time-series modeling and learning in the context of fault diagnosis.

Experience-grounded semantics: a theory for intelligent systems

An experience-grounded semantics is introduced for an intelligent reasoning system, which is adaptive, and works with insufficient knowledge and resources. According to this semantics, truth and meaning are defined with respect to the experience of the system – the truth value of a statement indicates the amount of available evidence, and the meaning of a term indicates its experienced relations with other terms. The major difference between experience-grounded semantics and model-theoretic semantics is that the former does not assume the sufficiency of knowledge and resources. This approach provides new ideas to the solution of some important problems in cognitive science.

The system reliability analyser tool

This paper describes the design and evaluation of a socio-technical design support system, the system reliability analyser (SRA). The tool is used to validate non-functional system requirements, such as system reliability. It employs a Bayesian belief network (BBN) model to assess system reliability (Pearl in Probabilistic reasoning in intelligent systems: networks of plausible information, 1988) based on a variety of high-level operational scenarios. The tool diagnoses problematic areas in future system models and assists in the identification of their causes. The evaluation of the tool demonstrated that it supported the task it was intended to do. The evaluation also identified usability problems in the current visualisations and illustrated their resolution.

Evolutionary approaches to fuzzy modelling for classification

An overview of the application of evolutionary computation to fuzzy knowledge discovery is presented. This is set in one of two contexts: overcoming the knowledge acquisition bottleneck in the development of intelligent reasoning systems, and in the data mining of databases where the aim is the discovery of new knowledge. The different strategies utilizing evolutionary algorithms for knowledge acquisition are abstracted from the work reviewed. The simplest strategy runs an evolutionary algorithm once, while the iterative rule learning approach runs several evolutionary algorithms in succession, with the output from each considered a partial solution. Ensembles are formed by combining several classifiers generated by evolutionary techniques, while co-evolution is often used for evolving rule bases and associated membership functions simultaneously. The associated strengths and limitations of these induction strategies are compared and discussed. Ways in which evolutionary techniques have been adapted to satisfy the common evaluation criteria of the induced knowledge—classification accuracy, comprehensibility and novelty value—are also considered. The review concludes by highlighting common limitations of the experimental methodology used and indicating ways of resolving them.

Editorial advances on uncertain reasoning in intelligent systems

Editorial advances on uncertain reasoning in intelligent systems

Intelligent Reasoning Systems Inc. secures funding for 'aggressive growth'

Intelligent Reasoning Systems Inc. secures funding for 'aggressive growth'