Reinventing Technological Innovations with Artificial Intelligence E-Book
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- Herausgeber: Bentham Science Publishers
- Kategorie: Fachliteratur
- Serie: Federated learning for Internet of Vehicles: IoV Image Processing, Vision and Intelligent Systems
- Sprache: Englisch
- Veröffentlichungsjahr: 2000
Reinventing Technological Innovations with Artificial Intelligence delves into the transformative impact of Augmented and Virtual Reality (AVR) technology across industries. The book explores the merging of real and digital worlds, paving the way for personalized experiences in areas such as tourism, marketing, education, and more. With the potential to redefine business practices and societal norms in the era of Industry 4.0, AVR technologies hold untapped potential beyond gaming and entertainment. This volume presents a comprehensive overview of the current landscape, challenges, and prospects of integrating AVR with Artificial Intelligence (AI) for innovation and sustainability in various domains.
The book presents 11 edited chapters contributed by technology and innovation experts that explore applications of AI, AR and VR technologies in different sectors in both public and private sectors. The editors have included reviews of technologies that impact human resource management, corporate social responsibility, healthcare, supply chain and criminal investigation. The reviews also highlight the role of AI in sustainable agriculture and smart cities.
Key Features:
Unveils the role of AVR in transforming real surroundings into digitally enhanced personal experiences.
Explores AVR's applications beyond gaming in diverse sectors like marketing, construction, education, and more.
Discusses challenges such as technical limitations, high costs, and resistance to adopting AVR.
Addresses the need to enhance the reliability and effectiveness of AVR technologies in various industries.
Provides a comprehensive perspective on AI innovations, AR, and VR technologies with real-world examples.
The book is an informative reference for researchers, professionals, and experts in technology, innovation, who are interested in the convergence of Augmented and Virtual Reality with AI for practical applications in diverse industries.
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PREFACE
Augmented and Virtual Reality (AVR) technology shapes the margins between the real world and the digital world. The boundary between the two worlds has become so porous that it nudges into more personalized and unique experiences across a range of industries like tourism, marketing, education, social media, construction, and so on. However, its more revolutionary impact would be to the extent where each one of us would be capable of transforming our real surroundings into digitally enhanced personal experiences with a very thin margin between the real and digital worlds. This is going to transform the whole business world and further revolutionize our surroundings i.e Society 5.0.
AR and VR are widely considered, so far, in the gaming and entertainment sectors. But looking at the Gartner report on strategic technology trends 2019, AR and VR technologies are among the top 10 trends and it has been experimented to enhance the productivity of various business domains like marketing, construction, power, education, tourism, telecommunication, automobiles, sports, etc. AR technology joins the information and simulated images in the real milieu to augment the users’ circumstantial perception of their settings, using some technology-equipped devices whereas VR technology replaces the users’ perception of their surroundings to complete virtual surroundings with the help of computers only. The augmentation might be the result of AR, VR or both AVR. Construction industries are using AVR to reduce the risks at working sites. AVR technologies are of extreme significance for the construction segments as the assembled setting is essentially connected to a 3D space and AEC professionals rely mainly on imagery for communication. Boeing’s aerospace giant has been using AVR for electricals. A very famous company of UPS is using AVR (AR & VR separately) to provide training for driver safety. However, AVR technologies have not yet been explored, or used to an extent that would make them more reliable for realistic business necessities. Technical limitations, lack of awareness, resistance to use and accept AVR as a substitute, high cost, and time obligation are some of the major challenges of bursting usage of AVR.
Irrespective of the AVR maturity level, there is a need to focus on the effectiveness of AVR technologies to enhance innovations, and sustainability in various business domains, particularly construction tasks, and for the targeted implementation of research. There is no book volume as of now that focuses on the entirety of AVR’s strengths, its reliable usage in business innovations and the challenges to be addressed by the researchers. The proposed book volume will address all these points with a more holistic approach, ranging from awareness to innovations and reliability to sustainability from a business perspective. More specifically, no edited volume exists that systematically maps (i) how AR and VR technologies can be used, (ii) their potential benefits, (iii) prevalent issues, and (iv) a futuristic innovation plan.
The book on AI innovations is organized as follows.
Chapter 1 throws light on the use of multi-agent systems which often operate in dynamic, open, and complicated settings. Two approaches to improving agent interactions are presented in this chapter. By using ontologies, the technique may allow agents to create “rich” interaction protocols using Petri net (CPN) based methodologies in order to allow agents to create dynamic protocols.
Chapter 2 describes the need for Artificial intelligence (AI) which has gained enormous usage in business in recent years. But the use of Artificial Intelligence is limited to a greater extent when it comes to measuring business ethics and morality. The chapter, conceptually formulates the implementation of AI in CSR programs by using AMOS 21's Structural Equation Modelling (SEM) and SPSS 21 with empirical testing of projected models for AI efficient CSR practices.
Chapter 3 emphasizes how AI integrated with machine learning (ML) and Deep-learning (DL) techniques are used in various disease diagnosis domains, medication discovery, medical visualization, digital health records, and electro-medical equipment.
Chapter 4 discusses the method of combining information in the form of image alternatives with a software programme that stores knowledge with real images. Augmented and virtual reality (AVR) technologies aid in the explanation of concepts to improve academic learning through the use of two-dimensional media in education.
Chapter 5 discusses the role of VR in 3D reconstruction and visualization of crime situations such as criminal assaults, traffic accidents, and homicides by establishing a new method for criminal investigation.
Chapter 6 explains how rapid advances in artificial intelligence are enhancing the performance of many sectors and enterprises, including green supply chain management. It further analyzes the future outlook of the market for Artificial Intelligence (AI) in GSCM and green sustainability if they follow SDGs.
Chapter 7 discusses the use of information-driven systems to offer problem-specific knowledge to decision-makers using internet-based distributed platforms. An XML-based approach to representing and exchanging domain-specific information for informed decision support is shown in the chapter. The technology's implementation specifics, commercial ramifications, and future research goals are presented.
Chapter 8 portrays the importance of the farming sector which is considered to be the backbone of the Indian economy. The work emphasizes on the use of an automated watering system to reduce the farmer's manual involvement in the field at an effective cost by implementing an artificial intelligence system based on sensing, a control mechanism with required correction for the maximum yielding of irrigation.
Chapter 9 introduces AI as a useful aid to urban planning thereby creating a safer and more sustainable future for its citizens. Applications of AI in smart cities are then discussed, followed by a brief discussion on the prevailing best practices. Challenges in creating AI-enabled smart cities in India are also outlined in the chapter.
Chapter 10 portrays that Augmented Reality is the need of the hour for Human Resource Management in this era of globalization wherein the world has become flat and businesses have no boundaries. The chapter presents the evolution, applications, and challenges of VR and AR with respect to HRM.
Chapter 11 intends to explore how AI-enabled technology, in the fashion industry and fashion environment, is influencing the green economy status of the fashion industry, especially in the post-COVID-19 era of innovative e-commerce fashion.
The work given in the book will give some interesting insights to the readers.
List of Contributors
List of Abbreviations
Adarsh Garg,Valentina Emilia Balas,Rudra Pratap Ohja,Pramod Kumar SrivastavaAgent Interactions Environments
Kuldeep Singh Kaswan1,*,Jagjit Singh Dhatterwal2,Ankita Tiwari3Abstract
Any system capable of acting as an intelligent agent has all of these characteristics. When an agent has the capacity to interact with other agents, it is able to do so in a multi-agent system (MAS). Systems with several agents often operate in dynamic, open, and complicated settings. Many factors, such as domain restrictions, the number of agents, and the interactions between agents, are not fixed in an open environment. There are several problems in coordinating the interactions and cooperation of agents; as a result of this, many existing agent interaction protocols are not well-suited for open settings, which is a significant impediment to agent interaction. Two approaches to improving agent interactions are presented in this chapter. To begin, by using ontologies, the technique may allow agents to create “rich” interaction protocols. When it comes to agent interaction in open settings, we employ colored Petri net (CPN) based methodologies in order to allow agents to create dynamic protocols.
INTRODUCTION
One of today's most essential design ideas is multi-agent systems. Computational systems that include intelligent agents are called multi-agent systems (MAS). If you want to know what's going on in the world around you at any given time, you need an intelligent agent. There are four key characteristics of intelligent agents in general [1]:
Self-control and the opportunity to interact with and work with other agents is a key aspect of social intelligence, which is characterized by autonomy and self-control.Agents' social skills may be honed via the use of MASs. There are MAS agents that live and work together in the same family. In a multi-agent society, it is difficult to control the connections between the many actors. When one of the agents chooses to influence others to attain a set of objectives,they get involved with one another. The exchange of messages and declarative interpretations of textual information creates interactions between agents in a system [2].Agent communication languages (ACLs) include Knowledge Query and Manipulation Language and the Foundation for Intelligent Physical Agents (FIPA's) ACL (FIPA, 2004).Protocols for agent interaction specify common patterns for communications sent back and forth between them. Because of the limitations of many current agent interaction protocols, MASs cannot be used in a broad variety of contexts [3].As a first step, many current MASs application sectors need agents to operate in dynamic and unexpected (open) settings. Interaction among agents in these situations may be affected by unexpected messages, message loss, or message order abnormalities. Agent-interaction procedures as they now exist are unable to cope with the unforeseen situations that may arise. Secondly, certain MASs have a variety of agent designs, and different agents may interact in different ways [4]. One agent can't be sure that the other agents will comprehend or accept the discussion he or she conducts with the other. To make problems worse, the vast majority of agents have interaction protocols hard-coded into their programming. Agent designers are in charge of determining whether to use a certain protocol, what data to send, and how to carry out tasks in the proper order. Changing protocols after they have been pre-programmed into an agent is a trade-off. KQML, for example, is a modern interaction protocol that isn't specifically designed to transfer knowledge [5]. No one should use this “poor” (Lesser, 1998) method of sharing complex information. Many existing interaction protocols are rigid and inflexible, which make it difficult to implement MASs. In this regard, MASs researchers are working to establish a flexible and knowledge-rich interaction protocol [6].
A technique for agent relationships is covered in this chapter that may enhance both theoretical and practical aspects of agent interactions. Agents may design “knowledge-rich” protocols for interfacing as a first step using this method. An ontology facilitator is a person who helps agents identify, acquire, and develop ontologies [7]. Colored Petri nets (CPNs) may be used to construct a strategy that allows agents to dynamically establish interaction protocols, which indicates that it is not the job of agents to create protocols; instead, agents use their talents and condition to determine what protocols should be used.
Here is a breakdown of the rest of the denomination's structure: Both ontology-based MASs and the usage of PNs and CPNs to specify agent procedures are discussed in this work, which is divided into two sections. In the fourth part, agents may use CPN-based approaches to construct dynamically flexible protocols. To conclude this denomination's methodology section, its is explored for potential applications. The project's results and future intentions are summarized in this section.
ONTOLOGY-BASED INTELLIGENT AGENT INTERACTION
Agents require common terminologies to construct their knowledge and theoretical frameworks of the topic of interest to accomplish knowledge-level communication. A semantic web or a computer language may be used to build ontologies, in which these conceptualizations can be articulated. There must be a common ontology for the MAS's working environment to allow agents to create knowledge “rich” interaction protocols. Ontology facilitators should be included to help agents seek, acquire, and construct conceptual frameworks [8].
Multi-Agent System Ontology Expressions
The intellectual discipline of philosophers is where the term “ontology” comes from. It is possible for an agent or a group of agents in MASs to have an ontology that is computer-readable interpretation of knowledge regarding ideas, connections, and limitations.
MASs ontology
In general, MAS ontologies may be divided into two types: common ontologies and special ontologies. It is possible to create broad ontologies, which explain the aggregate knowledge of an entire multi-agent society, and more narrow conceptual frameworks, which define the understanding of just one particular agent in that society. An ontology representations format and standard working domain ontologies are both necessary components of the MAS design process. Several renowned supervised learning research institutes have already developed standard ontologies for a broad variety of application disciplines as a consequence of the advantages of predictive modeling (for example, the Stanford KSL Ontolingua Server) [9].
As a result, MAS domain ontologies may be created or current ontologies can be referenced.
Ontologies are conventions for machine-readable understanding, and they are commonly expressed in Semantic web technologies such as RDF or computational science which are formal languages. Ontology interpretations still lack a balanced scoring methodology (format). That is, there are a number of ontology languages that have been extensively and effectively employed in a range of application domains. As an example, in several applications, many researchers have found success using DAML+OIL [10]. It showed that ontologies may be used to describe expertise in an online auction mechanism by evaluating the benefits of many commonly used ontology technologies. As seen in Fig. (1), an “item” is used as an example of how one can express an ontology in the digital commerce involved in transportation. OIL is used to represent the ontology in this example.
Fig. (1)) Ontology Framework.Editable Ontology-Based MASs
An ontology-based MAS's conceptual framework must contain an ontology facilitator, which makes it easier for agents to find, acquire, and change ontology data. The methodology for ontology-based MASs, as well as the ontology accelerators, have been provided [11]. MAS ontologies are kept in the encyclopedia base, the ontological board, and the ontology editor, as illustrated in Fig. (2). MAS ontologies convert and modify new ontologies retrieved from the ontological board, and then modify this additional taxonomy to common ontologies that may be read by all agents of MAS Ontology.
Fig. (2)) Ontology-based MAS’s.AGENT ONTOLOGY INTERACTIONS
It is possible to add knowledge-level signals in interactions with ontologies and ontology enhancers. Agents' ability to adapt their communication protocols based on their current situation is a recurring issue [12]. In this section, we provide a CPN-based method for creating flexible protocols for interaction. In the first part, we quickly present the fundamental ideas of CPNs, and in the second, we show how CPNs may be used for agent engagements.
Petri Nets and Coloured PN’s
Tokens are displayed in the 4-tuple depicted as a collection of Places (P1, P2, P3), Transitions (T1 to P1 and P2 to T1), and Arcs (T2 to P1). This 4-tuple may quantitatively describe the basic construction of a PN. P1 and P3 each carry a single token in the starting condition. A system's net architecture and discharge rules determine how it transitions between states [13]. Transitional firing regulations for various types of PN's are not the same. When they fire, all PNs, on the other hand, do the same thing: A transformation may be activated if the token quantity for all input locations is more than or equivalent to the strengths of their arcs. Transitions are collections of non-empty types, commonly known as colored sets; tokens in the transition's input positions will be shifted to the transition's output places when it is activated. It's a list of transformations; it's an Arcs collection; it's the node utility, the color function, the guard one, the expressions one, and the introduction one. P is the array of locations and T is the list of transitions.
