A Multi-agent Based System for Route Planning

The article is devoted to the establishment of cooperation relations between law enforcement agencies of Ukraine and Europol; the use of a network application of secure information exchange (SIENA), during the exchange of operational and strategic information. The problematic issues of the implementation of measures of international cooperation in the exchange of information between law enforcement agencies of Ukraine with Europol, while countering a criminal offense are considered. The main mechanisms for regulating the procedure for establishing, putting into operation and operating a secure communication line for the exchange of information between law enforcement agencies of Ukraine and Europol are disclosed. Disclosed the normative and legal content of the category “secure exchange of information”. Taking into account the goals, the author set himself the following tasks: to analyze the mechanism for the safe exchange of information, securing the right of law enforcement agencies of Ukraine to cooperate with Europol; consider theoretical doctrinal approaches to justify the general obligation of states to cooperate with each other. The analysis of the mechanism for the exchange of operational and strategic information in the implementation of interaction between the law enforcement agencies of Ukraine and Europol is carried out. The author substantiates the expediency of using the Secure Information Exchange Network Application (SIENA) as the main tool for the exchange of operational and strategic information while countering international crime. The article analyzes the statistics of Europol regarding the number of application messages (SIENA) exchanged between interested parties (including requests, responses to information, etc.). Examples of joint operations that have been successfully implemented with the help of modern tools of law enforcement agencies of the member states of the European Union are given. The problematic issues of the exchange of operational and strategic information using the network application (SIENA) between the law enforcement agencies of Ukraine and Europol are considered. Suggestions were made to improve the use of Europol instruments in the exchange of information.

Artificial Intelligence (AI) has become an increasingly important and popular topic not just within the field of Computer Science but also the world at large. One of the current challenges in the field of AI is multi-agent planning, of which Swarm Intelligence (SI) is a possible solution drawing from natural systems as an inspiration. While individual agents in a flock or swarm are mostly simplistic and similar in nature, together they can develop extremely complex and emergent behaviour. A very well-known implementation of Swarm Intelligence is “Boids”. Bird like objects first introduced by Craig Reynolds in 1987 that flock and move together based on the 3 core steering behaviours of Separation, Alignment, and Cohesion. However, even current work on boids tend to let individual agents be “reactive” in nature, giving instructions to themselves based on their current state and their observations of the environment around them. This project aims to study how adding an additional layer on top of traditional flocking behaviour, by making agents “socially aware” through actively sharing opinions with each other, will affect the dynamics of a flock.

Assalamu’alaikum Wr. Wb. and Greetings.I am pleased and honored to welcome you at the First International Conference on Computer Science and Applied Mathematic (ICCSAM 2018), which was held this year by AMIK and STIKOM Tunas Bangsa Pematangsiantar in collaboration with the Indonesian Computer and Information Professional Association (IPKIN), Indonesian Mathematical Society (INDOMS) and Bank Muamalat. As Chair of the Foundation, I would like to convey a greeting Welcome to Parapat City Lake Toba to presenters from various countries and especially those I respect for the presence of scientists in the field of Computer and Mathematical Sciences and industry practitioners in the 2018 ICCSAM event.I see that this activity is designed to enhance the exchange of knowledge and new discoveries in computer science and mathematics and related fields in industry. I hope that scientists in the field of computer science and mathematics and those who work in the industry can share knowledge and work together as a team that has mutual relations with each other. I am very pleased to say that the theme of this conference, “Advancing Computability Innovation” is very much in line with the objectives of AMIK and STIKOM conferences and missions, namely “to become a Science and Technology based Study Program that meets industrial needs and functions as a research center in Information Data Science. The conference was held in response to output and aoutcame which had a significant contribution in the field of computer science and mathematics as an inseparable unit of science towards the development of local and global industries. It is our happiness and honor to welcome distinguished professors present to convey their expertise at this conference.I hope this meeting will enable the development of productive dialogue between participants from various countries. They also provide invaluable opportunities for networking among participants, institutions and industries. I also hope that diversity in these fields can reveal more opportunities for researchers and practitioners from all over the world to start a lot of research related to the industry in the future.I would like to congratulate AMIK and STIKOM Tunas Bangsa for starting this conference with a synergistic contribution from dedicated partners namely the USU Faculty of Mathematics and Natural Sciences, USU’s Faculty of Computer and Information Technology, the Indonesian Computer and Informatics Professional Association (IPKIN), Indonesian Mathematical Society (INDOMS), Bank Muamalat, Malaysia Pahang University (UMP), University of Essex, United Kingdom, Ankara, Turkey and Institute of Applied Mathematics Middle East Technical University to all delegates for their full support, cooperation and contribution to the 2018 ICCSAM. I also want to thank the Organizing Committee for their perseverance and extraordinary efforts. Various sponsors are also rewarded for their contributions. I really hope that all participants will get a pleasant stay here at NIAGARA HOTEL and bring back unforgettable experiences and valuable knowledge from this conference.Thanks.H. Ahmad Ridwansyah Putra

The international scientific and practical journal “Software products and systems” was organized in 1988 on the initiative of the General Director of the NGO “Centerprogramsystem” prof. V. P. Tikhomirov (Tver) and V. M. Silina (Moscow), head of the Main editorial office of the international journal “Problems of management theory and practice”. In the period from 1988 to 2018, the editor-in-chief of the journal was academician S. V. Yemelyanov. Since 2020, the international editorial Board, which includes well-known scientists in the field of information technologies of the Russian Federation, Belarus, Ukraine, Azerbaijan, Germany, Finland, Mexico, Vietnam and other countries, is headed by academician G. I. Savin. Associate members of the editorial Board are the national research University “MEI” (Moscow), the technological Institute of the southern Federal University (Taganrog), TvSTU and the research Institute “Centerprogramsystem”. The journal is reviewed in the bibliographic databases of RSCI, CrossRef, and is included in the List of leading peerreviewed journals and publications recommended by the higher attestation Commission of the Russian Federation for publication of research results in the defense of candidate and doctoral theses in the fields of computer Science, computer engineering and management (05.13.XX). The journal publishes the results of original research in the field of information technologies, applied artificial intelligence systems, including the development of databases and knowledge bases, expert and training systems, decision support systems and image recognition, multiagent systems, data mining systems, neural networks, robots, and telecommunications systems. However, the subject area of research is not limited. The magazine “Software products and systems” regularly acts as an information sponsor for international and national scientific and technical events in the field of information technology and artificial intelligence.

"Are we intelligent enough to understand intelligence? One approach to answering this question is "artificial intelligence," the field of computer science that studies how machines can be made to act intelligently. This book is intended to be a comprehensive introduction to the field of artificial intelligence, written primarily for the student who has some knowledge of computers and mathematics (say, at the junior or senior levels of college). In general, this book is addressed to all persons who are interested in studying the nature of thought, and hopefully much of it can be read without previous, formal exposure to mathematics and computers.

With the intensification of global economic competition, enterprises face the challenge of improving the supply chain management efficiency, and AI technology, as an emerging field in computer science, can provide effective solutions. As a leading e-commerce logistics service provider in China, JD Logistics has accumulated a wealth of logistics technology capabilities and digital transformation experience within and outside the JD Group. Using JD Logistics as a case study, this paper discusses the application of AI technology in supply chain management and its impact on enterprise responsiveness and operational efficiency. AI technology has significantly improved the supply chain operational efficiency and responsiveness of JD Logistics by applying intelligent supply chain planning, predictive analysis and intelligent decision-making. The study systematically summarizes the key role of AI technology in optimizing supply chain management through a literature review and specific case studies, providing an important reference and practical experience for enterprises to achieve digital transformation.

A central problem in the study of autonomous cooperating systems is that of how to establish mechanisms for controlling the interactions between different parts (which are called agents) of the system. One way to integrate such mechanisms into a multi-agent system is to exploit the technique of cooperation or negotiation protocols. In a protocol we distinguish to essential layers: the communication layer specifying the possible flow of messages between different agents, and the decision layer, which controls the selection of a message (speech-act) that the agent sends in a specific situation. In this report we first give a short introduction of our agent model InteRRap which provides the basis for the modeling of the different scenarios considered in the AKA-Mod project at the DFKI. The techniques we will discuss in the following are located in the plan based component and in the cooperation component of this model. The domain of application is the MARS scenario (Modeling a Multi-Agent Scenario for Shipping Companies) which implements a group of shipping companies whose goal it is to deliver a set of dynamically given orders, satisfying a set of given time and/or cost constraints. The complexity of the orders may exceed the capacities of a single company. Therefore, cooperation between companies is required in order to achieve the goal in a satisfactory way. This domain is of considerable interest for studies with economical background as well as for research projects. We give a short summary of results from economical studies that are concerned with the real-world situation in Germany in the transportation domain. They show the need for the development of new techniques from the field of computer science to tackle the problems therein. Then, an overview on related research is presented. Two approaches are discussed in more detail: the first one being based on OR-techniques and a second one being based on the concept of partial intelligent agents attempting to integrate techniques from OR and DAI. Both approaches are concerned with the situation in a single company. However, our purpose to handle the case of distributed shipping companies requires additional mechanisms, e.g. to cope with the problems of task allocation and task decomposition in multi-agent systems. Mechanisms for distributed task decomposition and task allocation processes in multi-agent systems belong to the core of our studies. Therefore, we will first discuss techniques for these problems in a general setting and then describe their implementations in the MARS system. In this description, particular emphasis is placed on the cooperation within a shipping company. Here, one company agent has to allocate a set of orders its truck agents. The truck agents support the company agents by giving cost estimations based on their route planning facility. Thus, this procedure provides the basis for the decisions of the company agents and is discussed in very detail. Finally, we present results from a series of benchmark tests. The test sets have also been run with OR-implementations and thus, give us the opportunity to compare our implementation against these approaches.

The society has entered the era of big data, computer network technology has improved people's living standards to a new level, and continues to progress toward the direction of artificial intelligence. Artificial intelligence is one of the three major scientific and technological achievements of the 20th century, and mathematics is its key theoretical foundation. Taking artificial intelligence in the era of big data as the research object, this paper analyzes the application of traditional mathematical operations in artificial intelligence in the field of computer science and technology in the future from the perspective of mathematical thinking. This paper mainly introduces the typical mathematical basis of artificial intelligence, including linear algebra, probability theory, mathematical statistics, discrete mathematics and mathematical modeling, and prospects the development trend of mathematical basis of artificial intelligence, hoping to provide some reference for the application of artificial intelligence in computer network technology.

Despite great effort in research and teaching as well as in raising women’s quotas in the public sector and in enterprises, a gender gap in computer science can still be observed. The reasons are manifold, including the lack of interest of girls because of existing stereotypes of male nerds, misconceptions in the field of computer science, and concrete differences in students’ self-concept and performance. Girls do not choose schools or studies in the field of computer science because working on computers or coding is unattractive to them. Research shows poorer performance, missing interest, and lower self-concept for girls in secondary computing education as well as a high dropout rate of female students in computer science courses at the bachelor’s level. Software development exams contribute to those high dropout rates, and women often choose a different subject or even stop their university education altogether. This leads to fewer female employees in the field of computer science, and this situation calls for special measures at several levels. The current paper provides an overview of the initiatives of our university that aim to address several aspects of the gender gap. The programs offered aim to (1) increase interest in computer science and recruit (highly) gifted girls for our talent programs, (2) recruit and support female bachelor’s and master’s students in computer science, and (3) redesign programming courses and teaching materials to reduce the gender gap in performance and support, especially for female students. This study also describes a cyber tutoring program in which highly gifted girls between 13 and 16 years collaborate with and are supervised by female role models in higher positions in STEM. The evaluation results gained in the first year of this case study are promising and suggest that the extension and further development of the program would be advantageous.

The work done by some authors in the fields of computer science, artificial intelligence, and multi-agent systems foresees an approximation of these disciplines and those of the social sciences, namely, in the areas of anthropology, sociology, and psychology. Much of this work has been done in terms of the humanization of the behavior of virtual entities by expressing human-like feelings and emotions. Some authors (e.g., Ortony, Clore & Collins, 1988; Picard, 1997) suggest lines of action considering ways to assign emotions to machines. Attitudes like cooperation, competition, socialization, and trust are explored in many different areas (Arthur, 1994; Challet & Zhang, 1998; Novais et al., 2004). Other authors (e.g., Bazzan et al., 2000; Castelfranchi, Rosis & Falcone, 1997) recognize the importance of modeling virtual entity mental states in an anthropopathic way. Indeed, an important motivation to the development of this project comes from the author’s work with artificial intelligence in the area of knowledge representation and reasoning, in terms of an extension to the language of logic programming, that is, the Extended Logic Programming (Alferes, Pereira & Przymusinski, 1998; Neves, 1984). On the other hand, the use of null values to deal with imperfect knowledge (Gelfond, 1994; Traylor & Gelfond, 1993) and the enforcement of exceptions to characterize the behavior of intelligent systems (Analide, 2004) is another justification for the adoption of these formalisms in this knowledge arena. Knowledge representation, as a way to describe the real world based on mechanical, logical, or other means, will always be a function of the systems ability to describe the existent knowledge and their associated reasoning mechanisms. Indeed, in the conception of a knowledge representation system, it must be taken into attention different instances of knowledge.

The realm of Artificial Intelligence is the discipline of crafting intelligent machines and smart computer systems through science and engineering. It is connected with parallel endeavors in utilizing computers to grasp human intelligence though intelligence can transcend methods that are biologically observable. Although there is no universally agreed upon definition for Artificial Intelligence (AI), it is typically recognized as the field of computer science which can emulate human cognitive functions that include but are not limited to processes like perception, reasoning or decision-making. This research paper focuses on what artificial intelligence is all about, how we are depended onto AI in our daily life or what are the applications of AI, Different AI tools or machines powered by AI

Artificial intelligence (AI) is a rapidly evolving field that has seen tremendous growth in recent years. In this chapter, the authors provide an overview of current trends in AI and their applications in computer science. They also discuss the future directions of AI research and their potential impact on the field of computer science. They start by introducing the basic concepts of AI and its various subfields. Then, they present an overview of current trends in AI research, including machine learning, natural language processing, computer vision, and robotics. The authors discuss how these trends are applied in computer science, such as autonomous vehicles, fraud detection, and personalized medicine. Finally, they discuss the future directions of AI research, including the development of more explainable AI systems, the integration of AI with other emerging technologies, and the ethical considerations of AI.

Awareness is a concept that has been frequently studied in the context of Computer Supported Cooperative Work. However, other fields of computer science can benefit from this concept. Recent research in the multi-agent systems field has highlighted the relevance of complex interaction models such as multi-party communication and context awareness for simulation and adaptive systems. In this article, we present a generic interaction model that enables to use these different models in a standardized way. Emerging as a first-order abstraction, the environment, in the sense of a common medium for the agents, is a suitable paradigm to support the agents' awareness. We present an operational model, called Environment as Active Support of Interaction, to take into account all the agents that can be interested in a communication. This model is then extended for the regulation of multiagent systems interactions. Priority policies are given to manage the rules governing the context (un-)awareness of the agents. We also present a new AUML connector to create protocols that take into account the agent awareness to implement proactive behaviour, and several communication scenarios are proposed to show practical applications of this model.

Artificial intelligence is a relatively new field of computer science that has evolved rapidly in recent years from the stage of laboratory research to that of the field with various spheres of applications. There is currently a whole set of techniques that allow the construction of programs and artificial intelligence systems, systems considered commercial products, successfully applied in the field of mass production. The purpose of this article is to provide a theoretical picture of the artificial intelligence algorithms used in serial production programming. Current research trends in mass production planning are oriented towards the adoption of hybrid techniques, based on specific algorithms of artificial intelligence, which try to combine the advantages of local search strategies (such as optimization by the ant colony model - Ant Colony Optimization, optimization by the particle set model - Particle Swarm Optimization , Genetic algorithms) with those of global search (such as Simulated annealing), in order to determine those optimal and efficient solutions in the case of mass production scheduling.

Artificial Intelligence (AI) is a field of computer science that creates systems capable of simulating human intelligence. Itmeans any technology that seeks to mimic human intelligence, which covers a huge range of capabilities such as voice and image recognition, machine learning techniques,and semantic search.It involves the development of algorithms and computational models that allow machines to learn, reason, make decisions, and solve complex problems autonomously. AI in marketing isthe application of AI technologies like machine learning, natural language processing, and data analytics to optimize and automate marketing tasks. This allows marketers to create more efficient, personalized, and data-driven campaigns.AI in marketing is synonymous with precision, personalization, and efficiency. Marketing AI works by processing data with algorithms and pattern recognition to simulate human intelligence. From chatbots to social media management to full-scale marketing campaigns and automation, artificial intelligence is changing the way marketers do their jobs.In this paper, we will explain various uses of AI in marketing.