Artificial Intelligence Paradigm Research Articles

Hybrid Functional-Neural Approach for Surface Reconstruction

This paper introduces a new hybrid functional-neural approach for surface reconstruction. Our approach is based on the combination of two powerful artificial intelligence paradigms: on one hand, we apply the popular Kohonen neural network to address the data parameterization problem. On the other hand, we introduce a new functional network, called NURBS functional network, whose topology is aimed at reproducing faithfully the functional structure of the NURBS surfaces. These neural and functional networks are applied in an iterative fashion for further surface refinement. The hybridization of these two networks provides us with a powerful computational approach to obtain a NURBS fitting surface to a set of irregularly sampled noisy data points within a prescribed error threshold. The method has been applied to two illustrative examples. The experimental results confirm the good performance of our approach.

Fuzzy Concurrent Object Oriented Expert System for Fault Diagnosis in 8085 Microprocessor Based System Board

With the acceptance of artificial intelligence paradigm, a number of successful artificial intelligence systems were created. Fault diagnosis in microprocessor based boards needs lot of empirical knowledge and expertise and is a true artificial intelligence problem. Research on fault diagnosis in microprocessor based system boards using new fuzzy-object oriented approach is presented in this paper. There are many uncertain situations observed during fault diagnosis. These uncertain situations were handled using fuzzy mathematics properties. Fuzzy inference mechanism is demonstrated using one case study. Some typical faults in 8085 microprocessor board and diagnostic procedures used is presented in this paper.

An application of case-based reasoning with machine learning for forensic autopsy

Case-based reasoning (CBR) is one of the matured paradigms of artificial intelligence for problem solving. CBR has been applied in many areas in the commercial sector to assist daily operations. However, CBR is relatively new in the field of forensic science. Even though forensic personnel have consciously used past experiences in solving new cases, the idea of applying machine intelligence to support decision-making in forensics is still in its infancy and poses a great challenge. This paper highlights the limitation of the methods used in forensics compared with a CBR method in the analysis of forensic evidences. The design and development of an Intelligent Forensic Autopsy Report System (I-AuReSys) basing on a CBR method along with the experimental results are presented. Our system is able to extract features by using an information extraction (IE) technique from the existing autopsy reports; then the system analyzes the case similarities by coupling the CBR technique with a Naïve Bayes learner for feature-weights learning; and finally it produces an outcome recommendation. Our experimental results reveal that the CBR method with the implementation of a learner is indeed a viable alternative method to the forensic methods with practical advantages.

Design, implementation and validation of AI-inspired information systems

While there is an emerging and always-growing interest for novel paradigms appeared recently (e.g., social networks,Cloud computing,NoSQL databases, Big Data, and so forth), Artificial Intelligence (AI) always plays a critical role in next-generation Information Systems. Indeed, as technology and paradigms pervade our life, there is a challenging need for smarter and more sophisticated Information Systems, for instance using innovative methodologies like crowdsourcing. As a consequence, it is natural to foresee the advancement of a novel class of Information Systems, which we call as AI-Inspired Information Systems. Basically, these are Information Systems which incorporate in their critical layers (i.e., design, implementation, validation) AI methodologies, yet extending their roots to classical foundations, with, indeed, exciting innovations. From this main evidence, there emerges a great interest in methodologies that can improve the variegate aspects of Information Systems life-cycle, with special emphasis on the modeling, representation, querying, retrieval, reasoning, mining and end-user phases. On the other hand, AI-inspired approaches tend to be computationally expensive, so that complexity analysis of proposed solutions must be rigorously evaluated and reasonable execution bounds should be derived. As a consequence, it naturally follows that designing, implementing and validating AI-Inspired Information Systems not only conveys in the need for elegant and scalable approaches, but also these approaches need to be theoretically-sound and exposing bounded complexities. Along this line, this special issue on “Design, Implementation and Validation of AIInspired Information Systems” of Journal of Intelligent Information Systems focuses on latest research results and open research challenges on the problem of designing, implementing and validating Information Systems, based on AI paradigms, according to principles and guidelines provided above. With the aim of adequately fulfilling both theoretical and practical issues of the investigated research context, this special issue contains five papers, which have gone through two rigorous review rounds before being accepted for the final inclu-

Adaptive Delivery of Trainings Using Ontologies and Case-Based Reasoning

The delivery of trainings to diversified and constantly changing audiences is expensive and time consuming. We propose a computational approach addressing this issue by providing an adaptive training delivery framework. The approach relies on case-based reasoning (CBR) as a problem-solving method whereby cases are used rather than a prohibitive number of rules to store knowledge, i.e., experience. CBR is indeed accepted as one of the mainstream paradigms in artificial intelligence since it represents both knowledge and reasons about it. This choice is further motivated by the fact that the process of adaptation to different audiences is built on the traces left by previous learning tasks and practices that can be stored and automatically retrieved. Moreover, to address the crucial and pending issue of case indexing in CBR, we use ontologies to model and index the learning objects that represent the trainings core, thus reducing the retrieval process and improving search. Substantially, we develop an adaptation algorithm responsible for the required corrective actions in the adaptive delivery of trainings destined to diversified and heterogeneous learners.

Intrusion Detection System Using Artificial Immune System and New Multi Core Technology

Intrusion detection systems (IDS) are nowadays very important for every IT company which is concerned with security and sensitive systems. A perfect IDS has still not been found and it stays a hot and challenging area in computer security research. Recently a new approach started to make its way to intrusion detection, namely the immune system. It has a lot of interesting features we would like to find in IDS. A new artificial intelligence paradigm was new compared to neural networks or fuzzy logic, but it is very promising for different areas in computer science. By abstractly comparing the way an intrusion detection system and the human immune system work, it is noticed some similarities. Within this context it is normal to use as much similarities as possible to improve IDS and to see how to implement the different features; this is where the artificial immune systems paradigm will help. This paper employs a modification method on the artificial immune system which known as LISYS (Light weight Immune System) through the using of new technology of Multi Core processor, the modification was running an individual instance of LISYS on each core of the processor which increase the number of detectors used by each node that used as an AIS node in the network, the experimental results shows that the detection efficiency was well improved.

Integrated Traffic and Communication Performance Evaluation of an Intelligent Vehicle Infrastructure Integration (VII) System for Online Travel-Time Prediction

This paper presents a framework for online highway travel-time prediction using traffic measurements that are likely to be available from vehicle infrastructure integration (VII) systems, in which vehicle and infrastructure devices communicate to improve mobility and safety. In the proposed intelligent VII system, two artificial intelligence (AI) paradigms, i.e., artificial neural networks (ANNs) and support vector regression (SVR), are used to determine future travel time based on such information as the current travel time and VII-enabled vehicles' flow and density. The development and performance evaluation of the VII-ANN and VII-SVR frameworks, in both the traffic and communications domains, were conducted using an integrated simulation platform for a highway network in Greenville, SC. In particular, the simulation platform allows for implementing traffic surveillance and management methods in the traffic simulator PARAMICS and for evaluating different communication protocols and network parameters in the communication network simulator, Network Simulator version 2 (ns-2). This paper's findings reveal that the designed communications system can support the travel-time prediction functionality. The findings also demonstrate that the travel-time prediction accuracy of the VII-AI framework was superior to a baseline instantaneous travel-time prediction algorithm, with the VII-SVR model slightly outperforming the VII-ANN model. Moreover, the VII-AI framework was shown to perform reasonably well during nonrecurrent congestion scenarios, which have traditionally challenged sensor-based highway travel-time prediction methods.

Book Reviews

At a time when smartphones, smart-homes, smart-cities, wearables, factories, etc., are becoming increasingly omnipresent, shall we also expect technological progress in artificial intelligence (AI) to result in the emergence of smart-governments and nations? The field of AI, and more broadly the development of artificial cognitive machines, is making breathtaking advances. A significant increase in computing power is enabling the rapid adoption of a new paradigm of AI, whereby cognitive machines are no longer programmed line by line, instruction by instruction, but instead are now capable of learning autonomously, thereby continuously developing themselves. The new paradigm is unleashing extraordinary progress in a wide range of applications, from healthcare to transportation and even the justice system; at the same time, these new forms of intelligence are making decisions in complex ways that escape the limits of human comprehension.The technological transformation driven by artificial cognitive machines is already beginning to have far reaching consequences, some unintended, at a scale and pace which exceeds even that of the first and second industrial revolutions. This paper aims to discuss these implications, both with an ontological and epistemological perspective, to assess the potential challenges for the systemic viability of democratic societies: a fundamental change in the nature of economic wealth creation, which could raise significant social tensions, combined with the paradoxical reduction in the effectiveness of human communication. It then explores the potential role of artificial cognitive machines to address its own challenges, introducing the idea of a future brain for public governance, understanding brain as an emergent property resulting from the interaction of human agents and AI systems created to address the priorities of social organizations.

Application of selected artificial intelligence methods in terms of transport and intelligent transport systems

In recent years, there has been increased interest among both transportation researchers and practitioners in exploring the feasibility of applying artificial intelligence (AI) paradigms to address some of the transport problems in order to improve the efficiency, safety, and environmental-compatibility of transportation systems. The contribution is to highlight the application of tools and methods in the field of artificial intelligence solutions to traffic problems, where using traditional solutions would be time consuming and more expensive. This article deals with the application of selected artificial intelligence methods in case of transport and intelligent transport systems.

An empirical study of modeling self-management capabilities in autonomic systems using case-based reasoning

Autonomic systems promise to inject self-managing capabilities in software systems. The major objectives of autonomic computing are to minimize human intervention and to enable a seamless self-adaptive behavior in the software systems. To achieve self-managing behavior, various methods have been exploited in past. Case-based reasoning (CBR) is a problem solving paradigm of artificial intelligence which exploits past experience, stored in the form of problem–solution pairs. We have applied CBR based modeling approach to achieve autonomicity in software systems. The proposed algorithms have been described and CBR implementation on externalization and internalization architectures of autonomic systems using two case studies RUBiS and Autonomic Forest Fire Application (AFFA) have been shown. The study highlights the effect of 10 different similarity measures, the role of adaptation and the effect of changing nearest neighborhood cardinality for a CBR solution cycle in autonomic managers. The results presented in this paper show that the proposed CBR based autonomic model exhibits 90–98% accuracy in diagnosing the problem and planning the solution.

Knowledge modeling through computational agents: application to surveillance systems

Abstract: In this work the concept of computational agent is located within the methodological framework of levels and domains of description of a calculus in the context of different usual paradigms in Artificial Intelligence (symbolic, situated, connectionist, and hybrid). Emphasis in the computable aspects of agent theory is put, leaving open the possibility to the incorporation of other aspects that are still pure cognitive nomenclature without any computational counterpart of equivalent semantic richness. The ideas presented are shown as currently being implemented on semi‐automatic surveillance systems. A case study of a mobile robot application for the detection and following of humans is described.

RT-MOVICAB-IDS: Addressing real-time intrusion detection

This study presents a novel Hybrid Intelligent Intrusion Detection System (IDS) known as RT-MOVICAB-IDS that incorporates temporal control. One of its main goals is to facilitate real-time Intrusion Detection, as accurate and swift responses are crucial in this field, especially if automatic abortion mechanisms are running. The formulation of this hybrid IDS combines Artificial Neural Networks (ANN) and Case-Based Reasoning (CBR) within a Multi-Agent System (MAS) to detect intrusions in dynamic computer networks. Temporal restrictions are imposed on this IDS, in order to perform real/execution time processing and assure system response predictability. Therefore, a dynamic real-time multi-agent architecture for IDS is proposed in this study, allowing the addition of predictable agents (both reactive and deliberative). In particular, two of the deliberative agents deployed in this system incorporate temporal-bounded CBR. This upgraded CBR is based on an anytime approximation, which allows the adaptation of this Artificial Intelligence paradigm to real-time requirements. Experimental results using real data sets are presented which validate the performance of this novel hybrid IDS.

Interconnection between biological abnormalities in borderline personality disorder: Use of the Bayesian networks model

There is agreement in that strengthening the sets of neurobiological data would reinforce the diagnostic objectivity of many psychiatric entities. This article attempts to use this approach in borderline personality disorder (BPD). Assuming that most of the biological findings in BPD reflect common underlying pathophysiological processes we hypothesized that most of the data involved in the findings would be statistically interconnected and interdependent, indicating biological consistency for this diagnosis. Prospectively obtained data on scalp and sleep electroencephalography (EEG), clinical neurologic soft signs, the dexamethasone suppression and thyrotropin-releasing hormone stimulation tests of 20 consecutive BPD patients were used to generate a Bayesian network model, an artificial intelligence paradigm that visually illustrates eventual associations (or inter-dependencies) between otherwise seemingly unrelated variables. The Bayesian network model identified relationships among most of the variables. EEG and TSH were the variables that influence most of the others, especially sleep parameters. Neurological soft signs were linked with EEG, TSH, and sleep parameters. The results suggest the possibility of using objective neurobiological variables to strengthen the validity of future diagnostic criteria and nosological characterization of BPD.

Brain-mind machinery: brain-inspired computing and mind opening

Brain and mind continue to be a topic of enormous scientific interest. With the recent advances in measuring instruments such as two-photon laser scanning microscopy and fMRI, the neuronal connectivity and circuitry of how the brain's various regions are hierarchically interconnected and organized are better understood now than ever before. By reverse engineering the brain, computer scientists hope to build cognitively intelligent systems that will revolutionize the artificial intelligence paradigm. Brain-Mind Machinery provides a walkthrough to the world of brain-inspired computing and mind-related questions. Bringing together diverse viewpoints and expertise from multidisciplinary communities, the book explores the human quest to build a thinking machine with human-like capabilities. Readers will acquire a first-hand understanding of the brain and mind mechanisms and machineries, as well as how much we have progressed in and how far we are from building a truly general intelligent system like the human brain.Contents: The Brain: The Center of Attraction; Neurons and Synapses: The Key to Memory and Learning; The Cortex Architecture: The Building Block of Intelligence; Many Faces of Memories -- Investigating the Human Multiple Memory Systems; Learning Like a Human: How Does Learning Take Place in Our Brain?; Emotion and Cognition; Laminar Computing; Probabilistic Computing: The Bayesian Mind; Thinking Machine: Higher Theories of Brain and Commonsense Knowledge Generation; Modeling the Entire Brain: Biologically Inspired Cognitive Architectures; Are We There? What Can the Computer Do Today and Tomorrow?; Brain -- A Forest Not Totally Explored: What are Some of the Issues?; Understanding the Brain to Build Intelligent Systems; Conclusions -- The Mind That Matters.

Real-Time Highway Traffic Condition Assessment Framework Using Vehicle–Infrastructure Integration (VII) With Artificial Intelligence (AI)

This paper presents a framework for real-time highway traffic condition assessment using vehicle kinetic information, which is likely to be made available from vehicle-infrastructure integration (VII) systems, in which vehicle and infrastructure agents communicate to improve mobility and safety. In the proposed VII framework, the vehicle onboard equipment and roadside units (RSUs) collaboratively work, supported by an artificial intelligence (AI) paradigm, to determine the occurrence and characteristics of an incident. Two AI paradigms are examined: 1) support vector machines (SVMs) and 2) artificial neural networks (ANNs). Each RSU then assesses the traffic condition based on the information from multiple vehicles traveling on its supervised highway segment. As a case study, this paper developed a model of the VII-SVM framework and evaluated its performance in a microscopic traffic simulation environment for a highway network in Spartanburg, SC. The performance of the VII-SVM was compared with the performance of the corresponding VII-ANN framework, and both frameworks were found to be capable of classifying the travel experience using the kinetic data generated by each vehicle. The performance of the VII-SVM framework, in terms of its detection rate, false-alarm rate, and detection times, was also found to be superior to a baseline California-type incident-detection algorithm. Moreover, the framework provided additional information, including an estimate of the incident location and the likely number of lanes blocked, which will be helpful for implementing an appropriate response strategy. The proposed VII-AI framework thus provides a reliable alternative to traditional traffic sensors in assessing traffic conditions.

Extracting symbolic knowledge from recurrent neural networks—A fuzzy logic approach

Considerable research has been devoted to the integration of fuzzy logic (FL) tools with classic artificial intelligence (AI) paradigms. One reason for this is that FL provides powerful mechanisms for handling and processing symbolic information stated using natural language. In this respect, fuzzy rule-based systems are white-boxes, as they process information in a form that is easy to understand, verify and, if necessary, refine. The synergy between artificial neural networks (ANNs), which are notorious for their black-box character, and FL proved to be particularly successful. Such a synergy allows combining the powerful learning-from-examples capability of ANNs with the high-level symbolic information processing of FL systems. In this paper, we present a new approach for extracting symbolic information from recurrent neural networks (RNNs). The approach is based on the mathematical equivalence between a specific fuzzy rule-base and functions composed of sums of sigmoids. We show that this equivalence can be used to provide a comprehensible explanation of the RNN functioning. We demonstrate the applicability of our approach by using it to extract the knowledge embedded within an RNN trained to recognize a formal language.

Enhancing case-based reasoning for personnel rostering with selected tabu search concepts

In this paper, we investigate the advantages of using case-based reasoning (CBR) to solve personnel rostering problems. Constraints for personnel rostering problems are commonly categorized as either ‘hard’ or ‘soft’. Hard constraints are those that must be satisfied and a roster that violates none of these constraints is considered to be ‘feasible’. Soft constraints are more flexible and are often used to measure roster quality in terms of staff satisfaction. We introduce a method for repairing hard constraint violations using CBR. CBR is an artificial intelligence paradigm whereby new problems are solved by considering the solutions to previous similar problems. A history of hard constraint violations and their corresponding repairs, which is captured from human rostering experts, is stored and used to solve similar violations in new rosters. The soft constraints are not defined explicitly. Their treatment is captured implicitly during the repair of hard constraint violations. The knowledge in the case-base is combined with selected tabu search concepts in a hybrid meta-heuristic algorithm. Experiments on real-world data from a UK hospital are presented. The results show that CBR can guide a meta-heuristic algorithm towards feasible solutions with high staff satisfaction, without the need to explicitly define soft constraint objectives.

Soft-computing techniques and ARMA model for time series prediction

The challenge of predicting future values of a time series covers a variety of disciplines. The fundamental problem of selecting the order and identifying the time varying parameters of an autoregressive moving average model (ARMA) concerns many important fields of interest such as linear prediction, system identification and spectral analysis. Recent research activities in forecasting with artificial neural networks (ANNs) suggest that ANNs can be a promising alternative to the traditional ARMA structure. These linear models and ANNs are often compared with mixed conclusions in terms of the superiority in forecasting performance. This study was designed: (a) to investigate a hybrid methodology that combines ANN and ARMA models; (b) to resolve one of the most important problems in time series using ARMA structure and Box–Jenkins methodology: the identification of the model. In this paper, we present a new procedure to predict time series using paradigms such as: fuzzy systems, neural networks and evolutionary algorithms. Our goal is to obtain an expert system based on paradigms of artificial intelligence, so that the linear model can be identified automatically, without the need of human expert participation. The obtained linear model will be combined with ANN, making up an hybrid system that could outperform the forecasting result.

Study of classical conditioning in Aplysia through the implementation of computational models of its learning circuit

The learning phenomenon can be analysed at various levels, but in this paper we treat a specific paradigm of artificial intelligence, i.e. artificial neural networks (ANNs), whose main virtue is their capacity to seek unified and mutually satisfactory solutions which are relevant to biological and psychological models. Many of the procedures and methods proposed previously have used biological and/or psychological principles, models, and data; here, we focus on models which look for a greater degree of coherence. Therefore we analyse and compare all aspects of the Gluck–Thompson and Hawkins ANN models. A multithread computer model is developed for analysis of these models in order to study simple learning phenomena in a marine invertebrate (Aplysia californica) and to check their applicability to research in psychology and neurobiology. The predictive capacities of the models differs significantly: the Hawkins model provides a better analysis of the behavioural repertory of Aplysia on both the associative and the non-associative learning level. The scope of the ANN modelling technique is broadened by integration with neurobiological and behavioural models of associative learning, allowing enhancement of some architectures and procedures that are currently being used.

Safeguarding information intensive critical infrastructures against novel types of emerging failures

The complexity of information intensive critical infrastructures, like electricity networks, telecommunication networks and public transportation networks is today augmented much more than in the past: such complexity augments the number of possible failures and anomalous working conditions and consequently decreases the survivability of the infrastructures. In this paper, the possibility is investigated to detect early anomalies and failures inside information intensive critical infrastructures by the introduction of anomaly detectors being “self-aware” about the normal working conditions of the infrastructure itself. This approach has the objective to improve the performance of the most popular signature-based algorithms for intrusion detection, and makes use of different classes of time-oriented algorithms based on artificial intelligence paradigm. It has the advantage to work also in presence of unknown and unexpected types of attacks or failures. The tests, to evaluate the performance of the utilised detectors, are executed inside an emulated supervisory control and data acquisition (SCADA) system of an electrical power transmission grid, and a proposal for the future integration inside real SCADA systems is also reported.