Artificial Intelligence Logic Research Articles

Modeling $$\mathscr {C}^{0}$$ Family Logics for Artificial Intelligence: Doxastic-Temporal Logics for Reasoning About Goals

Modeling $$\mathscr {C}^{0}$$ Family Logics for Artificial Intelligence: Doxastic-Temporal Logics for Reasoning About Goals

Coupling AI with empirical research – A case of 3D printed food technology

3D-printed foods remain narrowly understood by consumers, limiting their ability to make informed choices and potentially obscuring the broader reality of this market. This research aims to investigate the factors influencing consumers’ knowledge, motivation, and intention to consume 3D foods. Leveraging Artificial Intelligence logic, we foreground consumers’ opinions and sentiments about 3D-printed food preferences, drawing on empirical data from two surveys.The findings of sentiment analysis show that the level of knowledge is critical in forming consumer sentiments both, positive and negative. Survey 1 examines the variations in positive affect, negative affect, and behavioral intention toward 3D-printed foods, considering extensive and limited levels of knowledge adequacy. This investigation uses the Knowledge Attitude-Behaviour theoretical model. Survey 2 identifies relationships through Topic recognition and applies the approach-avoidance motivation theory to discern the connection between health choices. Motivation (approach)/(avoidance) for 3D-printed foods both, had a positive and significant effect on healthy food choices. While Motivation (avoidance) had a positive and significant effect on resistance to new foods, Motivation (approach) of 3-D foods was not supported. The findings represented the importance of motivational tendencies, such as approach and avoidance, in shaping decisions related to healthy eating and receptiveness to innovative food concepts.This research presents an opportunity for researchers and practitioners to deepen their understanding of food development and consumer trends in the 3D-printed food market. Relying solely on User-Generated Content for conclusions may be insufficient. Additional research methodologies and data sources are necessary for a comprehensive understanding of behavior and food preferences.

Mivar-based route planning simulation model for obstacle-aware autonomous agricultural machinery

Autonomous robot navigation is increasingly becoming an important task that requires solutions. This paper explores the practical application of logical artificial intelligence to address the problem of route planning, using the example of a computer game. Within the scope of this work, a knowledge base model was created, and a new version of the mivar reasoner was developed for integration with Unity. This reasoner allows the activation of logical rules with linear complexity. As a result, a system was developed that processes user input for position and moves an autonomous agent in a virtual environment according to the rules. This work confirms the feasibility of using mivar technologies to improve control systems of autonomous robots in the area of agriculture. The study also emphasizes the adaptability of mivar networks in dynamic environments, demonstrating their ability to effectively process changes in real-time. This research shows enhanced decision-making capabilities and reliable navigation strategies for autonomous agents, setting a precedent for future developments in autonomous digital solutions for agriculture. The results obtained open new prospects for the advancement of technologies in the field of autonomous navigation.

Automated search for several alternative logical inferences in a mivar knowledge base

This paper describes an approach to the automatic generation of a mivar knowledge base of a three-dimensional logic space. This knowledge base is created to provide three-dimensional robot movement. Automatic generation of mivar knowledge bases is necessary for the further solution of the problem of planning three-dimensional routes of robots and robotic complexes. This work is the basis for solving problems of resource allocation optimization in the field of transport logistics and machine learning artificial intelligence based on the application of mivar technologies. The variant of practical realization of the automated technique of sequential removal of mivar network rules and search for new robot motion trajectories for their further comparison by the number of activated transition rules on the way from the initial location to the target one is considered. Mivar-based logic artificial intelligence technologies offer great opportunities for dynamic route finding in real time, which will help in further development of machine learning artificial intelligence in the field of transport systems. The paper is intended for researchers involved in the development of mivar expert systems for solving problems of three-dimensional movement of robots and optimizing production planning systems.

Route planning of autonomous robots in three-dimensional logic space using mivar technologies

The paper describes an approach to using mivar technologies for planning three-dimensional robot routes, taking into account the given obstacles. This system is of great importance in increasing the autonomy of robots, as it will ultimately help us get closer to creating artificial intelligence in mechanical engineering. At present, there are many tasks related to automation and robotics that require non-trivial solutions. The article describes three models of three-dimensional logical space and presents a visualization of route construction in these spaces for robotic complexes. Obstacles in three-dimensional logical space are understood as the absence of graph vertices and transitions between them in a certain specific area. The use of mivar technologies of logical artificial intelligence in the field of transport systems allows to significantly speed up route planning thanks to a unique mivar algorithm for processing information, which allows creating systems capable of making decisions in real time. This work is intended for researchers dealing with the problem of three-dimensional route planning using mivar technologies.

Model for describing mivar networks in the format of bipartite and tripartite oriented graphs for decision-making and information processing in machine-building AI

Nowadays, there is a strong trend towards the creation of autonomous systems. For their development, it is required to make decision-making systems, which in this article are proposed to be implemented using mivar technologies of logical artificial intelligence. The paper considers the problem of choosing a specific implementation of the mivar approach using the example of bipartite and threadbare oriented graphs of mivar networks. Purpose – to give an example of activation of parameters and rules when solving the problem of route planning in a two-dimensional logical space. To consider the features of the subject area of automated process control systems and analyze three-sided graphs for decision-making and information processing in mechanical engineering. New ways of developing mivar networks based on three-sided and multi-sided graphs are considered for the implementation of the rules for choosing the type "if ..., then ..., else ...", in which some parameters can be filled in when the condition is met, and others when the condition is not met. The approach described in the paper to the creation of decision-making systems based on mivar technologies allows to achieve a higher degree of autonomy in order to further replace the role of a human operator in automated process control systems in production.

INVESTIGATION AND THE ANALYSIS OF MEDICAL BIG DATA AND THE EMPLOYMENT OF ARTIFICIAL INTELLIGENCE FOR DISEASE PREDICTION

The development of technologies like big data, cloud services, Internet+, and artificial intelligence has greatly improved data communication services in the traditional pharmaceutical industry, driving the progress of the healthcare sector in our country. These advancements have facilitated healthcare system reforms, enhanced the quality and efficiency of medical services, and fostered technological innovation. In this context, our research focuses on the following areas and draws the following conclusions: In recent years, the pharmaceutical industry has leveraged technologies such as big data, cloud computing, Internet+, and artificial intelligence to enhance data transmission services. These advancements are considered crucial strategic resources for the pharmaceutical industry and ongoing healthcare system reforms in our country. Their goal is to improve the efficiency and quality of medical services, establish new models of healthcare delivery, and drive economic growth. Given this, our research aims to further explore these advancements. There is an increasing demand for an in-depth exploration of the utilization of logic in the fields of artificial intelligence and computer science. To address this need, the Manual of Logic in Artificial Intelligence and Logic Programming, along with its companion, the Manual of Logic in Computer Science, were developed. These resources combine comprehensive investigations and fundamental research to delve into various underlying topics across multiple disciplines. A foundational understanding of mathematical complexity is assumed as background knowledge, making the material of interest to logicians and mathematicians [1]. However, existing works primarily focus on core concepts and technologies, leaving out extensions, applications, and languages. The handbook aims to provide a comprehensive coverage of constraint programming, offering readers an accurate overview of the entire field and its potential. While each topic is presented in a survey-like manner, some details may be omitted in favor of broader coverage [2].Among various types of uncertainties, randomness and ambiguity are considered crucial and fundamental. The relationship between randomness and ambiguity is discussed, exploring uncertain states and their changes through measures like entropy and hyperentropy. The study investigates uncertainties in chaotic, fractal, and complex networks' evolutions and differentiations. A simple and effective method for simulating uncertainty through knowledge representation is proposed, providing a foundation for automating logical and visual thinking in the presence of uncertainty [4]. The appropriate timing for monitoring implementation remains unclear. Although there is room for improvement, if a data processing algorithm functions correctly, it can be employed separately from the original algorithm. However, there are limitations to its usage [5] Keywords: Big Data, biomedicine, cluster, electronic health cards, healthcare.

Algorithm for the classification of phases and stages of sleep in patients with chronic disorders of consciousness based on logical artificial intelligence

BACKGROUND: The analysis of sleep patterns in patients with chronic disorders of consciousness is attracting increasing attention in terms of diagnosis, prognosis, and treatment of severe brain damage. The study describes a software package based on an artificial intelligence (AI) expert system designed to classify the phases and stages of sleep, taking into account the characteristics of impaired cortical rhythm in such patients.
 AIM: To develop a specialized AI-based software package focused on patients with chronic impairment of consciousness for automatic classification of sleep phases and stages, with an emphasis on identifying sleep spindles and non-rapid eye movement (REM) and REM sleep phases.
 MATERIALS AND METHODS: To ensure the correct operation of the software package, receiver operating characteristic (ROC) curves were analyzed considering the binary classification of slow sleep, REM sleep, and wakefulness.
 RESULTS: The average sensitivity and specificity of the algorithm were 87.9 and 70.1, respectively. The average area under the ROC curve was 0.790. The algorithm for determining the REM phase demonstrates low specificity with high sensitivity, and its graph was similar to that of wakefulness, as well as the irregularity of the presence of REMs in the REM sleep phase in patients with CNS and the frequent presence of nystagmus in the waking state. Information about the presence of nystagmus, entered at the start of the program, allowed us to slightly increase the efficiency of the algorithm; however, this aspect probably needs further improvement.
 CONCLUSION: A software package that takes into account the features of electroencephalography of patients with chronic disorders of consciousness and analyzes sleep and wakefulness automatically is not only useful as a diagnostic tool for neurologists and somnologists but also contributes to a wider dissemination of this technique in clinical practice.

Prospects of the nanoelectronic element base of infosystems of autonomous aircraft

The ways of creating a nanoelectronic element base (NEB) of Autonomous Air Vehicle (AАV) with small dimen-sions and weight are analyzed. The autonomy of these devices predetermines the principles of building their in-formation systems using artificial intelligence logic, significant memory resources, as well as functionally special-ized and high-speed information processing methods. And the cost restrictions involve the use of practically de-bugged nano- and microtechnologies for optical devices and integrated circuits. In addition, the small volume and weight of AAV necessitate the miniaturization of their NEB. At the same time, the operation of the AAV in various extreme conditions with increased radiation and bursts of the electromagnetic field (for example, in space) is not ruled out. Under these conditions, the semiconductor elements of their electronics are likely to fail. Therefore, in NEB of AAV, it is expedient to switch to nanovacuum-channel transistors (NVCT), which do not have semiconduc-tors (or their role is auxiliary). In NVKT, the nanochannel of electron transfer between the electrodes is replaced by a vacuum one, and the control is carried out by the voltage on the field electrode - the gate. At the same time, the nanoscale length of the gap between the emitter and the collector provides low voltages on the electrodes of the NVKT. In general, replacing a semiconductor with a vacuum can simplify and reduce the cost of transistor technol-ogy, make it more resistant to radiation and high-energy fields, and will allow to reach THz frequencies. And the creation of new types of NVKT (for example, using the control of the electron spin orientation) will increase the per-formance of AAV information transmission and processing tools.

Application of Fuzzy Logic in Artificial Intelligence

Abstract: In 1965, Lotfi Zadeh created fuzzy logic as a development of Boolean logic in light of the numerical hypothesis of fuzzy sets, which is a speculation of conventional set hypothesis. Fuzzy logic offers a great deal of adaptability for exploiting reasoning, adding an idea of degree when checking states and allowing states to be other than obvious or misleading. , taking into account possible errors and weaknesses. We begin by giving a brief overview of fuzzy logic before talking about its application to artificial and computational intelligence. Then, with a focus on fuzzy expert systems, we discuss cutting-edge applications of fuzzy logic, with one representative example being thoroughly examined. The article's conclusion makes recommendations for how fuzzy logic and artificial intelligence might complement one another. We focus our endeavors on few plainly characterized sub-spaces of artificial intelligence, staying away from scattering, and keeping a good arrangement between essential examination and applications.

Fuzzy Floor Dust Cleaning Robot Prototype Based On Arduino

The design of a Floor Cleaning Robot Based on Arduino Uno R3 aims to make it easier for humans to clean the dust on the floor efficiently. This robot consists of several series of components including Arduino Uno r3: Arduino Uno r3, sensor ultrasonic, motor servo, This robot consists of several components including Arduino Uno R3, ultrasonic sensor, servo motor, motor shield driver, dc motor, and vacuum blower motor. The entire system synergizes in the process of cleaning the dust on the floor and all of them are connected to a power source in the form of a dc battery, which supplies a voltage of 7 volts for the Arduino circuit and 12 volts for the vacuum blower. The working principle of this robot starts when the ultrasonic sensor can measure and distinguish the closest distance between the robot and an obstacle, at the same time the servo motor will move up to 180° to assist the ultrasonic sensor in detecting obstacles, both the front, right, and left sides of the robot. This Floor Cleaning Robot is programmed by adapting fuzzy logic artificial intelligence, the fuzzy rules used in this robot aim to control the speed of the robot based on the obstacle distance detected by the ultrasonic sensor. Fuzzy logic executes the data and continues the command to drive the motor so that the robot can work efficiently cleaning dust on the floor by minimizing the occurrence of collisions against obstacles. Dust suction carried out by the vacuum blower motor is not included in the Arduino circuit, because the vacuum blower motor requires an input power of 12 volts. It is not possible to unite it to the Arduino circuit because the power it has is only 7 volts, but these two separate circuits can still synergize well in the process of cleaning the dust on the floor. The results of observations and experiments show that the fuzzy logic embedded in the Arduino as the brain of the dust-cleaning robot on the floor works quite well.

Design of quantum optical experiments with logic artificial intelligence

Logic Artificial Intelligence (AI) is a subfield of AI where variables can take two defined arguments, True or False, and are arranged in clauses that follow the rules of formal logic. Several problems that span from physical systems to mathematical conjectures can be encoded into these clauses and solved by checking their satisfiability (SAT). In contrast to machine learning approaches where the results can be approximations or local minima, Logic AI delivers formal and mathematically exact solutions to those problems. In this work, we propose the use of logic AI for the design of optical quantum experiments. We show how to map into a SAT problem the experimental preparation of an arbitrary quantum state and propose a logic-based algorithm, called Klaus, to find an interpretable representation of the photonic setup that generates it. We compare the performance of Klaus with the state-of-the-art algorithm for this purpose based on continuous optimization. We also combine both logic and numeric strategies to find that the use of logic AI significantly improves the resolution of this problem, paving the path to developing more formal-based approaches in the context of quantum physics experiments.

An architecture and performance evaluation framework for artificial intelligence solutions in beyond 5G radio access networks

The evolution of mobile communications towards beyond 5th-generation (B5G) networks is envisaged to incorporate high levels of network automation. Network automation requires the development of a network architecture that accommodates multiple solutions based on artificial intelligence (AI) and machine learning (ML). Consequently, integrating AI into the 5th-generation (5G) systems such that we could leverage the advantages of ML techniques to optimize and improve the networks is one challenging topic for B5G networks. Based on a review of 5G system architecture, the state-of-the-art candidate AI/ML techniques, and the progress of the state of the art, and the on AI/ML for 5G in standards we define an AI architecture and performance evaluation framework for the deployment of the AI/ML solution in B5G networks. The suggested framework proposes three AI architectures alternatives, a centralized, a completely decentralized and an hybrid AI architecture. More specifically, the framework identifies the logical AI functions, determines their mapping to the B5G radio access network architecture and analyses the associated deployment cost factors in terms of compute, communicate and store costs. The framework is evaluated based on a use case scenario for heterogeneous networks where it is shown that the deployment cost profiling is different for the different AI architecture alternatives, and that this cost should be considered for the deployment and selection of the AI/ML solution.

Improving the performance of industrial mixers that are used in agricultural technologies via chaotic systems and artificial intelligence techniques

In this study, it is aimed to show how important to apply chaotic systems and Fuzzy Logic artificial intelligence technique to increase the production performance of industrial mixers used in agriculture in terms of important criteria such as product quality, homogeneity, time, and energy saving by using. A PLC (Programmable Logic Controller) controlled mixer whose all functions can be controlled by the HMI (Human Machine Interface) operator panel is designed and manufactured for experimental studies. Water, leonardite and potassium hydroxide (KOH) mixture components are mixed in a newly designed mixer in three different ways by using traditional, chaos, and artificial intelligence techniques in order to obtain liquid humic acid which is used as plant nutrition and soil modulator in agriculture. For the chaotic mixing option, chaotic systems with different dynamic features are chosen from the literature. After converting the time series values of chaotic systems to the frequency values with the program written on the PLC device, it is provided to rotate at variable speeds according to different chaotic systems of the mixer motor connected to the frequency inverter. Fuzzy Logic is chosen for the artificial intelligence mixing option. Fuzzy Logic membership functions and rules are established in accordance with the results of the chaotic systems mixing experiments and the data obtained from experts in humic acid production. Mixing experiments are carried out by using an interface program that makes the choice of chaotic systems and motor rotation speed range. By mixing humic acid components with traditional methods, the comparison is done in terms of product quality, solute ratio, pH values and total energy consumption. When the developed mixer is provided to operate with chaos and artificial intelligence methods, it is observed that more efficient results are obtained in terms of criteria such as product quality, homogeneity, time, and energy savings compared to the traditional mixing methods.

Adaptive Congestion Prediction in Vehicular Ad-hoc Networks (VANET) Using Type-2 Fuzzy Model to Establish Reliable Routes

Urban areas are more prone to accidents and traffic congestions due to ever-increasing vehicles and poor traffic management. The increase in the emission of harmful gases is another important issue associated with vehicular traffic. Attaining a level of QOS is often challenging as it has to meet the eco-friendly factors along with reliable and safe transportation. Smart and accurate congestion management systems in VANET can significantly reduce the risk of accidents and health issues. To fulfil the requirements of QOS the congestion control methods should consider the properties such as fairness, decentralization, network characteristics, and application demands in VANET. We proposed an Adaptive Congestion Aware Routing Protocol (ACARP) for VANET using the dynamic artificial intelligence (AI) technique. The ACARP presents the adaptive congestion detection algorithm using the type-2 fuzzy logic AI technique. The fuzzy model detects the congestion around each vehicle using three fuzzy inputs viz. bandwidth occupancy, link quality, and moving speed. This is followed by inference model to estimate congestion probability for each vehicle. Finally, defuzzification determines status of congestion detection using the pre-defined threshold value for each vehicle. The status of congestion and its probability values were utilized to establish safe and reliable routes for data transmission. It also saves significant communication overhead and hence congestions in the network. The simulation results provide the evidence that the proposed protocol improves the QOS and assist in reduction of traffic congestions significantly.

Creating a “Logical intelligent plant care system” in digital agriculture based on Mivar approach

This work is devoted to developing an automated information processing and management system based on logical artificial intelligence. The proposed system has been designed to support the process of growing agricultural and house plants. This research is one of the global trends of intelligent tools and services application in the agricultural industry for improving human environmental and food safety. This work is a practical demonstration on mivar expert systems operation in the control loop with cyclic and calendar planning of processes. The results obtained in this work can be used as an information basis for implementing automated farms in urban environments. This implementation is based on mivar knowledge base platform which allows considering individual characteristics of crop growth, making decisions in conditions of heterogeneous sensor data, and timely adjusting the process of plants growing.

Introduction to the TPLP Special Issue from the 16th European Conference on Logics in Artificial Intelligence (JELIA 2019)

Introduction to the TPLP Special Issue from the 16th European Conference on Logics in Artificial Intelligence (JELIA 2019)

Digital Twin for Designing and Reconfiguring Human–Robot Collaborative Assembly Lines

This paper discusses a digital twin-based approach for designing and redesigning flexible assembly systems. The digital twin allows modeling the parameters of the production system at different levels including assembly process, production station, and line level. The approach allows dynamically updating the digital twin in runtime, synthesizing data from multiple 2D–3D sensors in order to have up-to-date information about the actual production process. The model integrates both geometrical information and semantics. The model is used in combination with an artificial intelligence logic in order to derive alternative configurations of the production system. The overall approach is discussed with the help of a case study coming from the automotive industry. The case study introduces a production system integrating humans and autonomous mobile dual arm workers.

Air Pollution Monitoring based Fuzzy Controller with Embedded System

Air pollution has a wide and great influence on the concentration of constituents of the atmosphere, which leads to many effects such as acid rains and global warming. In order to avoid such unwanted adverse imbalances in the nature, designing an air pollution monitoring system (APMS) is very important. This paper discussed the development of an effective solution for monitoring the air pollution by making an Arduino-Based Air Pollution Monitoring System (APMS). Carbon monoxide and Carbon Dioxide concentration levels in air were measured and monitored using MQ4 and MQ7 gas sensors and Arduino atmega microcontroller. These sensors can detect many harmful gases and can be used for measuring their amount very accurately. The concentrations of CO and CO2 in particle per million (PPM) will be monitored and displayed on the LCD very easily. Based on these measurements the pollution level could be monitored, determined, and displayed. The experiments were carried out using the developed wireless APMS under various physical conditions. The results showed that the designed system collects reliable and reasonable real time pollution data. Three hour sampling time was executed in each location. One of the logical functions that are widely used is fuzzy logic. A fuzzy logic artificial intelligence for gas sensors is used which clarifies the presence as well as the concentration of CO and CO2 efficiently. Fuzzy logic will gives the decision about whether the air is polluted or unpolluted. This logic function we can process some existing data into a form of output which can be in the form of a status or state of action that will be performed by a tool. This proposed system will contribute in the construction of an APMS in the outdoor or even in the indoor environment.

AN EFFICIENT FOG ENABLED HEALTHCARE MAINTENANCE SYSTEM USING INTERNET OF THINGS WITH DEEP LEARNING STRATEGIES

Internet of Things (IoT) is the ruling term now-a-days, in which it attracts several smart gadgets and application due to its robust nature and support. In healthcare industry several new technologies are required to improve the stability and provide transparent services to clients. The integration of healthcare maintenance system with respect to Internet of Things support leads to a drastic change in healthcare field as well as this provision provides huge advantages to users. This paper is intended to provide an intense healthcare maintenance scheme by using latest technologies such as Deep Learning, Internet of Things, Fog Computing and Artificial Intelligence. All these innovations are associated together to build a new deep learning strategy called Intense Health Analyzing Scheme (IHAS), in which this proposed approach provides all provisions to clients such as Doctors and Patients with respect to monitor the patient details from anywhere at anytime without any range boundaries. The Fog Computing is an innovative domain, in which it provides ability to the server to operate based on hurdle free processing logic. Artificial Intelligence logic is used to manipulate the health data based on previously trained health records, so that the predictions are more fine compare to the classical healthcare schemes. In traditional schemes it is difficult to raise an alert based on the emergency situation predictions, but in the proposed deep learning strategy assists the proposed approach to send an alert instantly if any emergency cases occurred on patient end. Generally the Fog Servers are used to reduce the occupancy of the storage server and provide reliable storage abilities to server, but in this proposed approach, the fog server is utilized for priority wise data handling nature and stores the health records accordingly. In this nature, the fog servers are handled and provide high efficient results to the clients in an innovative way. With the help of deep learning procedures, the health records are clearly prioritized and maintained into the server end for monitoring. For all this paper introduced a new logic of healthcare maintenance scheme IHAS to provide efficient support to patients as well as doctors in clear manner.