Applications of Intelligent Agents

Currently, agents are focus of intense on many sub-fields of computer science and artificial intelligence. Agents are being used in an increasingly wide variety of applications. Many important computing applications such as planning, process control, communication networks and concurrent systems will benefit from using multi-agent system approach. A multi-agent system is a structure given by an environment together with a set of artificial agents capable to act on this environment. Multi-agent models are oriented towards interactions, collaborative phenomena, and autonomy. This article presents the applications of multi-agent technology to the power systems.

Artificial Intelligence (AI) is a concept that has been a subfield of computer science since the 1950s. In recent years, with its growing development power, AI technologies have made significant progress and are now being used in many fields. Like in all areas, the use of AI technologies in academia has provided convenience to academics while also bringing ethical debates. In the literature part of the study, concepts such as AI, academia, academics and academic progress, ethics, ethical theories, academic ethics, and emotional states have been thoroughly examined and defined. In this study, starting from AI and scientific ethics, ethical issues arising from emotional states in academic research have been identified, and concrete solutions to these ethical issues have been proposed. The aim is to discuss the views of academics in order to determine what types of scientific ethical violations and prevention methods are involved. In this context, the semi-structured interview technique, which is one of the qualitative research methods, was preferred as the method. In the study, in-depth semi-structured interviews were conducted with 4 ethics experts and 4 psychology experts selected through snowball sampling technique. The data obtained through semi-structured in-depth interviews will be analyzed using content analysis. Within the context of the literature review and interviews: Ethics is based on the foundation of acting correctly. In this context, scientific ethics can be summarized as acting truthfully and honestly, not distorting data, and not trying to progress unfairly. The use of AI in academia is becoming increasingly widespread. From a positive perspective, this usage significantly contributes to making studies more practical. However, it can lead to problems such as unfair authorship, devaluation of human authorship, and incorrect data. The connection between academics’ professional advancement goals and emotional states becomes prominent in this context. The potential of AI to facilitate progression can lead to unethical use. To prevent such situations, it is recommended to organize training sessions to increase professional awareness, internalize ethics personally, establish ethical committees specific to the field of AI, conduct more effective audits by academic publication and promotion committees, and implement specific regulations for AI. Finally, for future academic studies, it is suggested that the usage of AI in academic research be measured and evaluated by ethics experts. For psychologists, conducting surveys with academics to explore how they use AI in the context of their emotional states and professional advancement goals is recommended.

Education is one of the many facets of human existence that have changed as a result of the advancement of artificial intelligence (AI). The goal of artificial intelligence (AI), a subfield of computer science, is to create computers and systems that can carry out operations that normally call for human intellect. According to data, the number of people using AI is expected to reach 3.33 million annually by 2030. Out of the 100 respondents who received the surveys, 100 said they utilize AI apps. Students in semesters two (19%), four (36%), six (17%), and eight (28%), respectively, make up the distribution of use. 90% of AI is used as a learning reference, compared to 10% for thesis. This study uses the Technology Acceptance Model (TAM) technique as a theoretical framework to ascertain the perceptions that help Industrial Engineering students decide whether to use AI as a learning reference. The analysis's findings indicate that perceived advantages (38.64%), perceived ease of use (40.73%), and actual usage (16.81%) are the perceptions that affect students' decisions in Malikussaleh University's Industrial Engineering department. The development of AI integration tactics in higher education can benefit greatly from these discoveries.

PurposeSocial interaction applications and reference tools are actively used by researchers to share and manage their research publications. Thus, this paper aims to determine the scholarly impact of selected Indian central universities.Design/methodology/approachThis study analyzed 669 articles having both Dimensions citations and Altmetric attention scores published by 35 Indian central universities for 4 subfields of Computer Science using Altmetric Explorer. This paper determined each university’s contribution in the studied subfields of Computer Science and the correlation among Altmetric attention score (aggregated and individual), Dimensions citation, and Mendeley readership counts for all 669 articles and stratified percentile sets of top 25%, and top 50% of the overall number of articles.FindingsThe findings showed that Jawaharlal Nehru University had the maximum Altmetric attention score, Banaras Hindu University received the maximum Dimensions citation, and University of Hyderabad (UoH) received the maximum number of Mendeley readers. Each central university was examined individually and then ranked based on their median values of Dimensions citations and Altmetric attention scores. Further, Twitter had the maximum Altmetric coverage, followed by Google+, Patent and Facebook for the retrieved articles. A significant strong positive correlation was observed between the Dimensions citation and Mendeley readership counts for all the three categories.Research limitations/implicationsBoth Altmetric attention scores and Dimensions citations can help funding agencies to assess and evaluate the research productivity of these universities, thus, making important decisions such as increasing, decreasing, re-distributing their funds.Originality/valueThe current body of research is focused mostly on relationships between citations and individual Altmetric indicators predominantly. For most of the studies, the citations were retrieved from Scopus, Web of Science or Google Scholar database. It was observed that by far, no study had examined the relationship between citations retrieved from Dimensions database, Altmetrics scores (both aggregated and individual) and Mendeley readership counts.

Commercial off-the-shelf (COTS) software products are being included in ever more complex and critical systems. There are clearly advantages to considering the use of COTS in such systems, but given the rigorous needs of such critical systems or subsystems, there are concerns about the suitability of COTS software for such applications. The AIAA’s Software Systems Technical Committee and the AIAA’s Computer Systems Technical Committee have undertaken an effort to produce a guidebook that captures issues related to the consideration and use of COTS products in these large, complex systems, with a special emphasis on those risks and risk mitigation approaches that relate to mission critical systems. This guidebook identifies a set of characteristics of mission critical systems that makes the consideration, selection and validation processes of COTS products (hardware, software, subsystems, etc.) an emerging factor in systems definition, development and acceptance. The guidebook discusses a large number of risk areas related to using COTS software products in mission critical systems and identifies various mitigation approaches to the risks. It includes detailed processes for selecting and evaluating the products and considerations for using COTS software products within the overall context of the software development life cycle standardized in IEEE/EIA 12207. Mission critical system characteristics such as reliability, safety, availability, maintainability and certification tend to influence whether or not COTS software should be considered for a given application. Once the suitability of COTS software has been determined, then it is possible that additional requirements may be placed on the product and/or the product’s vendor for such mission critical applications. Further, it is possible that certain system requirements and expectations may need to be modified because of the inclusion of COTS software products in that system. As COTS products continue to be considered as candidates for inclusion within mission critical systems, there are likely to be additional concerns and factors to emerge that will influence how both acquirers and suppliers decide if and/or when to use COTS products. Ongoing monitoring of this technology area seems to be warranted.

15 - Measure the superior functionality of machine intelligence in brain tumor disease prediction

Reasoning about knowledge and knowledge representation has been an issue of concern in Artificial Intelligence for over two decades. More recently, researchers have realized that these issues also play a crucial role in other subfields of computer science, including cryptography, distributed computation, data base theory, and expert systems.Any knowledge representation system provides information, usually incomplete, about some parts of perceivable reality, and different authors provide different formal models for the knowledge analysis.Recently, the notion of rough sets [Pawlak 1982] was introduced, which provides a systematic framework for the study of the problems arising from inprecise and insufficient knowledge.A rough set, like fuzzy set, is a matematical model used to deal with approximate classification. These concepts have been proved to be independent of one another [Pawlak 1985(2)].A number of experimental systems has been implemented based on the deterministic rough-set theory. These applications include analysis of medical data of patients with dudendal ulcer [Fibak 1986], control algorithm aquisition in the process of kiln production [Mrózek 1985],linguistic pattern recognition [Wojcik 1986], and approximate reasoning [Rasiowa 1987].A natural, probabilistic extention of the rough-set model has been proven to be useful mathematical tool for dealing with some problems occuring in machine learning like generation of decision rules from inconsistent training examples [Wong 1986(1)] or database design [Yasdi 1987].The problem of teaching a student by several imperfect teachers ([Pettorossi,Raś, Zemankova 1987], [Raś, Zemankova 1986]) is handled also within rough-set framework. The authors deal with the case when the teacher and the student understand each other only partially and define a formal system which provides the basis for writing procedures which generate rules of a knowledge base.The model used in all these investigations assume that in the process of perception one distinguishes entities (objects) and their properties. Properties of objects are perceived through assignment of some characteristics (attributes) and their values of the objects. In this way a universe of discourse (a problem domain) consisting of objects and elementary information items providing characterization of these objects in terms of attributes and attributes values is established. In general, information about objects obtained in this way is not sufficient to characterize objects uniquely; that is, it is not possible to distinguish all the objects by means of the admitted attributes and their values. This means that objects are recognized up to indiscernibility relation determined by elementary information items. Any two objects are indiscernible whehever they assume the same values for all the attributes under consideration.Next we form concepts; that is, we agregate some objects into sets. Information about a concept is composed from information about objects which are instances of concepts. Since objects are not necessarily distinguishable, information characterizing a concept may be ambigous to some extent. In this case we want to have at least some approximation of our information and we express it in terms of rough sets.All cited above approaches use purely semantical methods in their investigations, exept [Pettorossi,Raś, Zemankova 1987], [Raś, Zemankova 1986] where mixed semantical and syntactical (formal system) methods are used.In our paper we focus on purely syntactical methods and problems which can be solved by them. One of the problems is a problem of static learning as defined in [Pawlak 1985(1)].Its main idea is as follows. Suppose now that we are given a finite subset U of the set of objects O B. Elements of U will be called training examples and O B is called a training set.Assume futher that O B is classified into non-empty, disjoint subsets O 1, O 2, ..., O n , (n>2) by a teacher (or expert). The classification represents teacher's knowledge of objects from O B.Let's now assume that there is another person, a student, who is able to characterize each object from O B in terms of attributes from a set A. Description of objects in terms of attributes from A represents student's knowledge of objects from O B.Now we want to know if it is possible to describe the classification O 1, O 2, ..., O n provided by the teacher in terms of attributes from A, or more exactly, to find a classification algorithm which provides teacher's classification on the basis of properties of objects expressed in terms of attributes defined in the student's system S.We give here a purely syntactical, easy programable method to do so. The method consists of two procedures. First procedure, lets call it Procedure one generates, for a given term of a standard term language for a given system, its equivalent normal form.The next procedure, called Procedure Two will verify the correctness of proper decision algorithm.

In this lively series of essays, first published in 2004,Tom Dean explores interesting fundamental topics in computer science with the aim of showing how computers and computer programs work and how the various subfields of computer science are connected. Along the way, he conveys his fascination with computers and enthusiasm for working in a field that has changed almost every aspect of our daily lives. The essays touch on a wide range of topics, from digital logic and machine language to artificial intelligence and searching the World Wide Web, considering such questions as: • How can a computer learn to recognize junk email? • What happens when you click on a link in a browser? • How can you program a robot to do two things at once? • Are there limits on what computers can do? The author invites readers to experiment with short programs written in several languages. Through these interactions he grounds the models and metaphors of computer science and makes the underlying computational ideas more concrete. The accompanying web site http://www.cs.brown.edu/~tld/talk/ provides easy access to code fragments from the book, tips on finding and installing software, links to online resources, exercises and sample lectures.

Computational creativity is emerging as a sub-field of computer science; it attempts to make machines the subjects of artistic creations. Therefore, we examine whether machines can be considered the subjects of artistic creations through experiments on audiences’ appreciation of works by artificial intelligence (AI) and humans. According to our qualitative and quantitative findings, audiences reported no significant differences in appreciating works by AI and humans. Thus, it seems inappropriate to declare that “AI cannot be the subject of an artistic creation.” Prior to asking, “Can machines create art?” it is necessary to ask “Isn’t a human a kind of machine?” It is contradictory if we deny that humans are machines, because they are also formed by evolution, a mechanical algorithm. If humans are a type of machine, and if they also make art, eventually, this will come to mean that machines create art. In conclusion, it is not natural to deny the hypothesis, “Machines can be the subjects of artistic creations.”

Artificial intelligence (AI) is a subfield of computer science with the goal of creating intelligent machines (1) Machine learning is a branch of artificial intelligence. In machine learning a datasets are used for training diagnostic algorithms. This review comprehensively explains the applications of AI in the diagnosis in paediatric dentistry. The online database searches were performed between 25th May 2024 to 1st July 2024. Original research studies that focus on the automated diagnosis or predicted the outcome in Paediatric dentistry using AI were included in this review. AI is being used in varied domains of paediatric dentistry like diagnosis of supernumerary and submerged teeth, early diagnosis of dental caries, diagnosis of dental plaques, assessment of bone age, forensic dentistry and preventive oral dental healthcare kit. The field of AI, deep machine learning and CNN's is an upcoming and newer area, with new developments this will open up areas for more sophisticated algorithms in multiple layers to predict accurately, when compared to experienced Paediatric dentists.

Artificial Intelligence or AI is a subfield of computer science, which can be defined as the intelligence exhibited by a machine or a software having a remarkable impact on the field of biology and medicine. Imaging, on the other hand has become an essential component of many fields in medicine, biomedical applications, biotechnology and laboratory research by which images are processed and analysed. Putting together AI and imaging, the tools and techniques of artificial intelligence are useful for solving many biomedical problems and using a computer based equipped hardware software application for understanding images, researchers and clinicians can enhance their ability to study, diagnose, monitor, understand and treat medical disorders. Therefore the main idea behind this research paper is to focus on understanding the artificial intelligence, its concepts and various models available for the segmentation(or classification) of medical images, its applications, advantages and disadvantages and results and more.

The increasing demand in air transportation is pushing the current air traffic management (ATM) system to its limits in the airspace capacity and workload of air traffic controllers (ATCOs). ATCOs are in an urgent need of assistant tools to aid them in dealing with increased traffic. To address this issue, the application of artificial intelligence (AI) in supporting ATCOs is a promising approach. In this work, we build an AI system as a digital assistant to support ATCOs in resolving potential conflicts. Our system consists of two core components: an intelligent interactive conflict solver (iCS) to acquire ATCOs' preferences, and an AI agent based on reinforcement learning to suggest conflict resolutions capturing those preferences. We observe that providing the AI agent with the human resolutions, which are acquired and characterized by our intelligent interactive conflicts solver, not only improves the agent's performance but also gives it the capability to suggest more human-like resolutions, which could help increase the ATCOs' acceptance rate of the agent's suggested resolutions. Our system could be further developed as personalized digital assistants of ACTOs to maintain their workloads manageable when they have to deal with sectors with increased traffic.

Artificial intelligence is a subfield of computer science that refers to the intelligence displayed by machines or software. The research has influenced the rapid development of smart devices that have a significant impact on our daily lives. Science, engineering, business, and medicine have all improved their prediction powers in order to make our lives easier in our daily tasks. The quality and efficiency of regions that use artificial intelligence has improved, as shown in this study. It successfully handles data organisation and environment difficulties, allowing for the development of a more solid and rigorous model. The pace of life is quickening in the digital age, and the PC Internet falls well short of meeting people’s needs. Users want to be able to get convenient network information services at any time and from any location

So far, agent research can be broadly categorized into two areas: intelligent agents and mobile agents. Intelligent agent research has grown from a background of AI, humancomputer interaction, and autonomous systems, while mobile agents research stems from the areas of distributed computing and networked systems. Intelligent agents research addresses intelligent control, planning, domain modeling, user modeling, perception, interagent cooperation, and adaptivity. Intelligent agents can take on many different roles, ranging from representing a human user’s interests to actively collaborating with users and each other. Intelligent agents are being designed to tackle nearly every tedious, non-creative activity that people perform (e.g., Web searches), as well as many more abstract functions (e.g., entertainment). Major issues in intelligent agents research include knowledge representation and communication, architectures for intelligent autonomy, mixed-initiative behavior, collaboration techniques, and learning. Mobile agents address physical communication, agent mobility, tracking and locating mobile agents, security and reliability of agents, etc. Mobile agents are suited for optimizing communication ‐ moving the action towards the source of data ‐ and thereby avoiding the need for huge data transfers. This is beneficial in data-intensive applications such as data mining. Mobility can also improve reliability in certain cases. In the field of electronic commerce, mobile agents naturally reflect the characteristics of buyer and the seller, effectively playing the roles of traveling salesman or broker. They can also be applied for easier software customization, software distribution, and to represent disconnected users. The main issues are in security, providing the infrastructure on the Web (naming, locating, controlling, and messaging) and improving survivability. The biggest challenge for all new agent technology is developing successful applications. For this minitrack, we selected two characteristics of agents that can span the diverging areas of intelligent and mobile agents: mobility and communication. The accepted papers reflect the research issues described in this summary. The minitrack is organized in three sessions. The first session explores the coordination of multiple intelligent agents using dramatically different communication schemes. Andersson and Ygge present an efficient market-based approach to managing very large scale groups of agents engaged in coordination via producer/ consumer transactions. Their CoTree algorithm allows largescale markets to operate efficiently with sparse communication, rapid updates as agent preferences change, and minimal data caching requirements on massively distributed systems. In contrast, Nelson, Penner, and Soken discuss the Interaction Society model of coordination, in which relatively small-scale groups of knowledge-intensive agents collaborate using complex communication about discrete tasking and information requirements. Their model for inter-agent communication and interactions with human users is being applied to build a multi-agent advising system for coordinating military Search and Rescue operations. Finally, Barbuceanu discusses the role of obligations and interdictions in the context of coarse-grain agent interactions. By explicitly capturing social obligations and the costs associated with violating those constraints, he shows how rational agents can achieve coordinated behavior without necessarily sharing goals. The second session addresses Java-based mobile agent systems. The paper by Covaci, Busse and Zhang, describes how a mobile agent system can be used for network management. Their framework is based on existing solutions such as TMN, and can be extended to SNMP.

Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030