Developing Artificial Intelligence Agents for a Turn-Based Imperfect Information Game

Artificial intelligence (AI) is often employed to play games, whether to entertain human opponents, devise and test strategies, or obtain other analytical data. Games with hidden information require specific approaches by the player. As a result, the AI must be equipped with methods of operating without certain important pieces of information while being aware of the resulting potential dangers. The computer game GNaT was designed as a testbed for AI strategies dealing specifically with imperfect information. Its development and functionality are described, and the results of testing several strategies through AI agents are discussed

Audit and AI: Can Artificial Intelligence Restore Public Trust?

Due to the fallout from a series of corporate fraud scandals in the late 2000s, the auditing world has lost much of the public trust that is very important to the profession. Much of the value of an audit opinion is determined by the trust the public places in the auditors behind the opinion. Without trust in the auditors, the audit opinion has very little value. The recent increase in the usage of artificial intelligence (AI) in many industries presents a solution to the problem of auditors. Increased usage of AI in the audit process has the potential to better meet public demand for an audit as well as restore public trust

A brief network analysis of Artificial Intelligence publication

In this paper, we present an illustration to the history of Artificial Intelligence(AI) with a statistical analysis of publish since 1940. We collected and mined through the IEEE publish data base to analysis the geological and chronological variance of the activeness of research in AI. The connections between different institutes are showed. The result shows that the leading community of AI research are mainly in the USA, China, the Europe and Japan. The key institutes, authors and the research hotspots are revealed. It is found that the research institutes in the fields like Data Mining, Computer Vision, Pattern Recognition and some other fields of Machine Learning are quite consistent, implying a strong interaction between the community of each field. It is also showed that the research of Electronic Engineering and Industrial or Commercial applications are very active in California. Japan is also publishing a lot of papers in robotics. Due to the limitation of data source, the result might be overly influenced by the number of published articles, which is to our best improved by applying network keynode analysis on the research community instead of merely count the number of publish.Comment: 18 pages, 7 figure

Implementation of Artificial Neural Network in Embedded Systems

As we know the artificial intelligence, and specifically artificial neural networks, have improved rapidly in the last decade which leads to the application of these systems in commercial fields like in buying online products, medical applications, financial applications, etc. But we know also that ANN usually are complex systems that need a lot of computing power in order to function properly which limits their application in number of fields, including here embedded systems because of their limited hardware and software properties. In this paper our goal is to implement a fully functional ANN in ATmega328p microcontroller, which will be programmed and trained in microcontroller. Our study case is solar tracker, where we aim to create a functional tracker which works based on ANN and with very limited hardware resources. With this implementation we aim to prove that ANN can function in practical systems without need of high computing power but just with simple low cost embedded system

Pursuing an AI Ontology for Landscape Architecture

Technological advancements have become ubiquitous within landscape architecture. One of the latest advancements is in Artificial Intelligence, including techniques such as Machine Learning, Artificial Neural Networks and problem optimization. These advancements have already worked their way into landscape architecture. In this theoretical paper we briefly identify what the state of the art in AI is, as well as its potential and limitations in the discipline. Specifically, we argue for the need to create a disciplinary ontology to make knowledge explicit and shared amongst humans and machines

Project Management in the Era of Artificial Intelligence

This study discusses the advantages of AI integration in project management, specifically in areas such as resource allocation, decision-making, risk management, and planning. By interpreting vast amounts of data from various sources, AI provides project managers with valuable insights to make better decisions. Although some tasks can be automated, human intervention is necessary for accuracy and efficacy. Therefore, AI should complement human skills, not replace them. Project managers require analytics skills and stay updated on AI technology to integrate it effectively. Ultimately, this study highlights that AI integration can enhance productivity and efficient project delivery

Milestones in Software Engineering and Knowledge Engineering History: A Comparative Review

We present a review of the historical evolution of software engineering, intertwining it with the history of knowledge engineering because “those who cannot remember the past are condemned to repeat it.” This retrospective represents a further step forward to understanding the current state of both types of engineerings; history has also positive experiences; some of them we would like to remember and to repeat. Two types of engineerings had parallel and divergent evolutions but following a similar pattern. We also define a set of milestones that represent a convergence or divergence of the software development methodologies. These milestones do not appear at the same time in software engineering and knowledge engineering, so lessons learned in one discipline can help in the evolution of the other one

Towards An Artificial Intelligence Maturity Model: From Science Fiction To Business Facts

Artificial intelligence (AI) has become increasingly prevalent in organisations in different sectors. The rapid development of AI technology has rendered it essential to understand strategies for its implementation. Despite current trends, the uncertainties in the process required to establish strong AI capabilities are the major concerns for high level management. Therefore, this research-in-progress aims to understand AI practices in organisations through the development of an organisation-level AI maturity model (AIMM). However, to the best of our knowledge, no fully developed and theoretically derived AI maturity model currently exists. This research, therefore, represents an early attempt to develop an AI maturity model at the level of organisations; the results of this research will provide organisations with insights into the successful evolution and adoption of AI and can be used as a theoretical foundation for future research

Agriculture in Africa: the emerging role of artificial intelligence.

This chapter critically considers the application of artificial intelligence (AI) to agriculture in Africa. It contends that, while African countries can utilise AI to address agricultural challenges, realising the full potential of AI in agriculture requires the judicious adaptation of pervasive AI technologies to serve African interests. Africa's young, vibrant population along with the movement of people, goods and services around the continent, promoted under the African Union's (AU) Agenda 2063 provide a fecund platform for AI-driven agricultural transformation. This is pivotal because of the multilayered agricultural paradoxes on the continent. For instance, Africa is endowed with an abundance of uncultivated arable land and diverse agro-ecological zones, from rain-forest vegetation to dry and arid vegetation, which engender the growth of wide-ranging food and cash crops, yet it suffers an alarming increase in food insecurity. An AU, United Nations (UN) Economic Commission for Africa (UNECA) and Food and Agriculture Organisation of the UN (FAO) Report on Food Security and Nutrition in Africa confirmed that 281.6 million people on the continent, comprising one-fifth of the population, faced hunger in 2020; 346.4 million Africans suffered from severe food insecurity while 452 million suffered from moderate food insecurity in the same year