Software Reviews : IF/THEN ...

Artificial Intelligence (AI) has received significant attention in recent years. It is being adopted to provide solutions to medicine, engineering, education, government and several other domains. To analyze the state-of-theart of research in AI, we present a systematic literature review focusing on the Evolution of AI programming languages. Our search returned 7,604 documents; after reviewing these documents, 78 which were relevant for this study were retained. Our research revealed that the prevalence of AI programming language by volume of publications had experienced peaks and valleys between 1963 and 2018; however, between 2015 to 2020, related publications have been experiencing peaks. During the review period, the PROLOG programming language received the most attention in about 49% of publications; this was followed by LISP, which received almost 22%. The remaining attention was shared between Logic and Object-Oriented Programming (LOOP), ARCHLOG, Epistemic Ontology Language with Constraints (EOLC), Python, C++, ADA and JAVA. However, the predominant AI programming language in recent AI software is C/C++, which takes 70% of the modern AI libraries analyzed in this study. Python is used in 60% of the modern AI libraries analyzed. Their prevalence is as a result of their speed, portability and ease of coding, making them effective in developing trending AI libraries such as TensorFlow and Keras.

Price, consumption, and production influence global food estimation. Food consumption and food production needs improving affordability, government policy and expert proposition in decision making. Expert have developed a scenario combining direct weather impact on key grain producing regions, indirect impact through crop pathogens, consequences for markets, and stock of global food. Global food production tended to increase, meanwhile consumption under production. In Indonesia, production is under consumption. It is anomaly by global situation and world condition. Government policy is tariff, export, and import. Export food influenced by price food commodity is not stable, meanwhile food import tended to increase because by national food consumption increasing demand. Government policy and expert proposition to modern society, need Expert System is increasing rapidly. In Artificial Intelligence, Expert System is a computer system that emulates the decision making ability of a human expert. There are two reasoning strategies in Expert System: Forward Chaining and Backward Chaining. The aim of this paper is to identify which reasoning strategy system (Forward Chaining). This paper focus on the concept of knowledge representation in Artificial Intelligence and implementing of Forward Chaining on Food Affordability. Food Affordability used to planning of food in the future. Finally, Inference using Forward Chaining on Food Affordability, the result it is good and can be used to planning and policy on national food.

Artificial Intelligence (AI) has significantly transformed multiple industries, enabling automation, data-driven decision-making, and advanced problem-solving capabilities. This research paper explores the evolution and advanced applications of AI, focusing on Prolog, a logic-based AI programming language used in expert systems, natural language processing (NLP), and automated reasoning. The study presents a comparative analysis of Prolog versus other AI programming languages, including Python and Lisp, and highlights its continued relevance in knowledge-based AI. Additionally, this paper discusses challenges and future research directions for Prolog-based AI models. Despite the rise of machine learning and deep learning, symbolic AI—represented by Prolog—remains crucial for explainable AI (XAI), rule-based automation, and high-level reasoning. Keywords: Artificial Intelligence, Prolog, AI Programming, Machine Learning, Expert Systems, Symbolic AI

The issue addressed in this research is how to design and build an expert systemwhich capable of diagnosing dog diseases that are caused by parasites, and how this expertsystem later can be useful for dog owners.This expert system is developed based on website technology in order to ease theaccessibility for the users. The finished product is expected to become useful for dog owners,to help them diagnose their dog disease based on the symptoms in order to give the propertreatment. The software development methodology used to develop this expert system is theWaterfall methodology, while the design is done using Unified Modeling Language (UML).The programming language used to build the expert system is PHP for the logic and MySQLfor the database, and the finished product is then tested using black box testing method. Totest system accuracy, disease diagnosis results from the expert system is then compared withdiagnosis results from the real human expert (veterinarian).This expert system is using forward chaining method and Bayes theorem. The forwardchaining concept, in this system, starting with the symptoms that are set into a reasoning andrules to reach some conclusions which in this case are the possible diseases. While the Bayestheorem is used to draw a conclusion from the possible diseases by calculating the probabilityof what kind of disease suffered based on the symptoms, and the weight of the probabilityitself. This expert system, however, still can be developed not only to diagnose diseases thatare caused by parasites, but also to diagnose all kind of dog diseases.

Although the use of expert systems (ESs) as a problem-solving tool is relatively new in the transportation profession, it has been recognized that a well-designed ES can contribute in areas where knowledge domain requires an integration of expertise from various disciplines or is very complex and difficult to quantify. To minimize the effort involved in developing essential ES mechanisms, researchers often prefer to use ES shells which provide a standard inference procedure, knowledge representation, and a user-friendly design environment. However, commercial ES shells tend to be designed for only certain classes of problems, and are not as general and flexible as programming languages. More than 150 commercial ES shells are available in the microcomputer market. Most ES shells are ususally claimed by their vendors to be usable for almost any conceivable applications, but such claims have often proved to be extravagant. Hence, the selection of an efficient yet cost-effective commercial shell is one of the most critical tasks in ES development. In reviewing the the vital task of ES shell selection, this paper presents our experience in evaluating more than 30 ES shells for developing an ES for signal design. The proposed ES includes signal settings, phasing plans, controllers, and detector locations.

One of the issues touched by the expert system is diagnostic in the field of health, the initial suspicion to diagnose diseases in the human stomach. Gastric disease or disorder can be infected by the disease so that the public needs to be aware. Therefore, the dissemination of information about the disease is needed to find out more types of the disease early. Types of gastric disease is Irritable Bowel Syndrome, Acute Superficial Gastritis, Chronic atrophic gastritis, peptic ulcer, Gastroesophageal Reflux (GERD), Functional Dyspepsia, Gaster Malignant Tumors, Ulcers Gaster. Thus aims to generate web-based expert systems in medicine to diagnose diseases in human gastric symptoms through perceived user. The methodology used for the research of expert system development methods. Methods to build an expert system that Forward Chaining method to search Depth First Search. References from the internet, books and people skilled in the art that dr.Susilaningsih, who worked at the Hospital PKU Muhmammadiyah Yogyakarta. As for the software using PHP as the programming language and MySQL as database. Keyword : Expert System, Disease in The Human Stomach, WEB, Forward Chaining

Currently, technology capable of absorbing human thought processes and patterns is known as artificial intelligence. An expert system is a component of artificial intelligence that contains knowledge and experience acquired from an expert in a particular field. The aim of this study is to develop a web-based expert system designed to detect digestive disorders in humans. Its purpose is to provide patients with information about their health status, correlated with their presenting symptoms, with a certain degree of accuracy. Through the use of this expert system, patients have the ability to identify potential digestive disorders based on individual symptoms or combinations of symptoms they may be experiencing. Additionally, it facilitates early detection of digestive disorders without the need to delay treatment until doctors or specialists are available. The method used is Forward Chaining, which involves collecting symptom data to determine the appropriate type of disease. Forward Chaining is a reasoning technique used in expert systems and rule-based systems. It begins with known events or conditions and then attempts to match these events with the IF (combination) clauses from the IF-THEN rules that are available.

Mark Watson Morgan Kaufmann Publishers, San Francisco, 1997, 377 pp. ISBN 1-55860-420-0, $44.95 This book is well written and organized to cover how to apply AI (Artificial Intelligence) techniques for practical Internet and Intranet tools by using the Java programming language. In the book, the author uses neural network and genetic algorithms to develop distributed systems that are applied for the Internet and Intranets, such as client/server model and information retrieval tool and so on. Part 1 begins with a good general introduction to AI. The history and survey of Artificial Intelligence research are given briefly. In addition, symbolic processing versus neural networks approaches are discussed. This author opts to use neural network to develop example Internet/Intranet systems in the book. Part 2 consists of Java programs developed for AI, neural networks, genetic algorithms, natural language processing, and expert systems. Chapter 2 illustrates Graphical User Interface (GUI) class that is derived from Applet and Canvas classes in Java, which is used in most example programs in the book. Chapter 3 introduces a frame data structure that is used as a message in distributed systems in the later chapters. Chapter 4 shows neural network classes developed in Java and its theory and implementation. Chapter 5 explains the genetic algorithms and the Java genetic algorithm classes. Chapter 6 describes how to develop and implement natural language processing classes using Java. Chapter 7 shows how to share data objects in distributed systems. For example, distributed AI agent classes are developed to share data objects, WWW data collection agent classes are shown to retrieve documents in the Internet, and small POP (Post Office Protocol) mail classes are developed to send and receive mails. Chapter 8 illustrates how to develop expert systems by using Java Jess, which is based on OPS5/CLIPS expert systems. Part 3 explains how to generate and implement the projects using the Java classes that are developed in Part 2. Chapter 9 shows the project that demonstrates the use of genetic algorithms in chapter 5 to control enemy ships in a scrolling arcade game. Chapter 10 describes the real-time handwriting recognition system developed by using the neural network classes in chapter 4. Chapter 11 illustrates a greedy algorithm to optimize neural networks that is based on the author’s hand-tuning neural training data. Chapter 12 improves neural network training data by applying genetic algorithms, to discard bad training data and to avoid discarding good training data. Chapter 13 describes the distributed natural language processing system that uses natural language processing classes in chapter 6. The data structures in chapter 2 are used to store historical data in a separate server application on any other computer in the Internet or Intranets. The final chapter provides the design and implementation of a complete agent-based information retrieval tool for the Internet. The retrieved documents are stored in ASCII or HTML format. This book is a very good reference book for anyone interested in developing systems using Java in distributed environments, especially applying AI techniques. Also, the author provides the CD-ROM that includes the source code for Java classes developed in the book. Haklin Kimm, University of Tennessee

Every human being has an interest, and a different talent. Interest is a strong impetus for a person to do something he wants. Talent itself is an existing ability or innate or the ability that can be trained to master the things that interest. The potential of the child should be stimulated in advance so that it can be seen as a skill, knowledge and skillfulness that becomes the provision of his life. One way to recognize the child's talents and interests requires a test to understand the temperament and personality of the child, to understand multiple intelligences and to recognize the direction of the child's interest. The test is applied to an expert system using the Forward Chaining method to help determine the type of intelligence in the child. Each type of child intelligence has several potential talents and special interests of the child. The expert system is implemented in a Visual Basic 6.0 programming language. The results of this study in obtaining an alternative decision to recognize the type of intelligence of children who show potential talents and interests of children. So that talent can be further developed.
 Keywords: Talent and Interests, Forward Chaining, Expert System

Discusses artificial intelligence, particularly expert or knowledge systems, and its relevance for business‐to‐business marketing and marketing decision support systems. Enables the reader to understand the basic concepts of expert systems while providing more technical references, before discussing current and potential applications for decision support systems. Argues that expert systems will ultimately play a major role in such applications, developing over the next two decades. Includes appendices and a glossary of terms and definitions.

In this paper, it is explained that the forward and backward chaining can be naturally integrated on an extended Rete network via the generation of hypotheses from forward chaining inference. We introduce the concept of the hypothesis objects and augment the production rule format by adding the hypothesis condition elements in the LHS part of a rule, which control the forward and backward chaining invocation of rules. A rule can be used in forward directions, backward directions, or mixed directions. To be applied to the backward chaining, a modified version of Rete has been formulated by adding the hypothesis-and-nodes and backward direction edges to the node network. We then develop a backward chaining algorithm which takes advantage of already existing partial match results obtained by the previous forward chaining in the ERMI. The whole mixed inference process has the potential advantages over purely forward and backward chaining strategy.

In the dynamic field of programming education, integrating artificial intelligence (AI) tools has started to play a significant role in enhancing learning experiences. This paper presents a case study conducted during a foundational programming course for first-year students in higher education, where students were encouraged to utilize generative artificial intelligence programming copilot extensions in their programming IDE and browser-based generative AI tools as supportive AI tools. The primary objective was to observe the impact of AI on the learning curve and the overall educational experience.Key findings suggest that the introduction of AI tools significantly altered the learning experience for students. Many who initially struggled with grasping elementary programming concepts found that AI support made understanding basic programming concepts much easier, enhancing their confidence and skills. This was particularly evident in the reduced levels of anxiety typically associated with early programming learning, as the AI copilot provided a non-judgmental, always-available source for clarifying doubts, including queries that students might hesitate to ask in a traditional classroom setting.Notably, some students leveraged the AI to generate similar exercise problems, reinforcing their understanding and skills. The AI's capability to address basic queries also freed up the instructor's time, allowing for more personalized student guidance in more advanced problems. This shift in the instructional dynamic further contributed to a learning environment where students felt more comfortable engaging with complex topics, thereby reducing the psychological barriers often linked with early-stage programming education.The course's structure, enriched by AI, enabled students to delve into more complex programming constructs earlier than traditional curricula would allow. For instance, students were tasked with simulating basic e-commerce operations, such as user registration, product browsing, and cart functionalities. These practical challenges naturally introduced advanced concepts like external data storage, unit testing, and user interface design, which are typically reserved for more advanced courses. With the help of generative AI programming copilot tools, students at any programming skill level were able to develop nearly functional complex structures. Interestingly, even when their projects were not fully functional, students remained motivated. Instead of feeling discouraged by these imperfect outcomes, they showed resilience and a keen interest in understanding and improving their code. This reaction is a significant shift from traditional learning settings, where unfinished or flawed projects often lead to increased anxiety or a drop in motivation.Furthermore, the AI's proactive suggestions inspired students to explore beyond the curriculum. Advanced learners delved into databases, cryptography libraries in Python, and even more advanced user interface design, ensuring that they remained engaged and challenged. This elementary course, enhanced by generative AI tools, also inspired students to learn other programming languages since they now learned that individual learning is more available with the aid of generative AI.In conclusion, the integration of AI in programming education offers a promising avenue for enhancing both the learning experience and outcomes. This case study underscores the potential of AI to revolutionize traditional teaching methodologies, fostering a more dynamic, responsive, and inclusive learning environment.This paper handles the results, possibilities and challenges of AI empowered education in programming. It also gives practical examples as well as future research perspectives.

There are two trends in the software initiatives of the U. S. Department of Defense which will have direct impact on future software development and which suggest a focus for current basic research in software. These trends are 1) the move toward smart weapons, suggesting the use of Expert Systems to control these weapons, and 2) the directive that all software for embedded computer systems be written in Ada. The research focus suggested by these developments is an analysis of the Ada programming language and its associated programming support environment (APSE) to determine how to design and construct an Expert System for natural implementation in Ada.It is well known that Ada can be used as a language for Expert Systems. There now exist several Expert System Shells written in Ada and more are being developed every day. The issue still open to research is how to do this optimally, making the best use of the expressive power of the language. An absurd example will make this clearer. It is also known that it is possible to write an Expert System in COBOL. Doing such would be a very tedious and error prone task, because the expressive power of the COBOL language is focused on commercial applications.Another possibility for writing an Expert System in Ada would be to take an existing Expert System in LISP and translate the code, line for line into Ada. Admittedly this is a plausible task; this author has done some preliminary investigation in this area. However, it is arguable that such an approach will not result in efficient Ada code, for constructs which are natural to LISP might not translate into constructs natural to Ada.While it is difficult to give a precise definition of an Expert System, it is easy to list characteristics which are usually associated with such a system. This paper presents the results of some preliminary research on the implementation of these features by means of some of the structures in Ada, such as Generic Program Units, Packages and Data Encapsulation, which are typical of the language's rich expressive power and which must form the basis of any natural implementation in Ada of an Expert System.One of the features expected in an Expert System is forward and backward chaining, probably implemented with a version of the Rete algorithm. For this reason, this research included writing several small symbolic pattern matching procedures and testing their performance.This research also investigated the use of Ada packages with encapsulated data types to implement a blackboard structure. It was found that a global data structure and appropriate operators could be implemented. As a side result of this work, the frame construct with attached operators and inheritance mechanisms was implemented.The research reported here also addressed the issue of Object Oriented Programming which has become quite popular in LISP programming. Here the match to Ada is quite natural for the latter language itself is also designed to support the Object Oriented Approach. Several LISP textbook examples were translated to the corresponding Ada structure and manipulated with some test programs.Several directions for future research have been generated as a result of this preliminary work. One open question is how to implement concepts originally developed for a language with late binding, such as LISP, in a language with strong typing such as Ada. Another issue concerns the translation of LISP code into Ada code. Early work suggests that it will be more profitable to view the LISP as a high level specification language for an Ada system rather than a program to be translated with literal fidelity. These and several other projects are being pursued and will be reported out in future papers.

A doctor or medical scholar needs a reference book to learn how to diagnose tropical diseases. This reference book is sometimes a hassle if you have to carry it everywhere. This reference book is also impractical if you have to search it first to find the symptoms and diseases you want to study. So that we need a system to make it easier for doctors and medical scholars to study the science of diagnosis and look for symptoms and diseases. Expert systems are knowledge-based programs that provide expert quality solutions to problems in a specific domain. This expert system is used in the fields of medicine, agriculture, business, and others. Expert systems in the field of medicine make it easy to identify diseases suffered by patients through the symptoms present in the patient. This expert system helps doctors make diagnoses to convince doctors about the results of the diagnosis. The expert system in this study uses a combined method. The combined method is forward chaining and backward chaining. The forward chaining method is used to determine specific symptoms that appear, while the backward chaining method is used to trace general symptoms that arise from specific symptoms that have been previously selected. The result of combining these methods provides a diagnostic percentage of 100%. Meanwhile, the user's assessment of the system gives a good response.

This paper addresses the problem of supporting the software development process through the artificial intelligence. The expert systems could advise the domain engineer in programming without the detailed experience in programming languages. He will use and integrate, with the help of deductive database and domain knowledge, the previously developed software components to new complex functionalities. The service oriented architecture (SOA) and loosely coupled software allow to fulfill these requirements. The objective of this document is to provide the knowledge representation of atomic Web services which will be registered as the facts in the deductive database as well as the inferring techniques. Also, the use of domain specific language (DSL) for modeling domain engineerpsilas requests to the expert system will be considered within this document.