Artificial Intelligence Programming Languages Research Articles

Exploring Applications and Practical Examples by Streamlining Material Requirements Planning (MRP) with Python

Background: Material Requirements Planning (MRP) is critical in Supply Chain Management (SCM), facilitating effective inventory management and meeting production demands in the manufacturing sector. Despite the potential benefits of automating the MRP tasks to meet the demand for expedited and efficient management, the field appears to be lagging behind in harnessing the advancements offered by Artificial Intelligence (AI) and sophisticated programming languages. Consequently, this study aims to address this gap by exploring the applications of Python in simplifying the MRP processes. Methods: This article offers a twofold approach: firstly, it conducts research to uncover the potential applications of the Python code in streamlining the MRP operations, and the practical examples serve as evidence of Python’s efficacy in simplifying the MRP tasks; secondly, this article introduces a conceptual framework that showcases the Python ecosystem, highlighting libraries and structures that enable efficient data manipulation, analysis, and optimization techniques. Results: This study presents a versatile framework that integrates a variety of Python tools, including but not limited to Pandas, Matplotlib, and Plotly, to streamline and actualize an 8-step MRP process. Additionally, it offers preliminary insights into the integration of the Python-based MRP solution (MRP.py) with Enterprise Resource Planning (ERP) systems. Conclusions: While the article focuses on demonstrating the practicality of Python in MRP, future endeavors will entail empirically integrating MRP.py with the ERP systems in small- and medium-sized companies. This integration will establish real-time data synchronization between the Python and ERP systems, leading to accurate MRP calculations and enhanced decision-making processes.

Evolution of Artificial Intelligence Programming Languages - a Systematic Literature Review

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-the-art 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.

Computer-Aided Fit, Tolerances and Limit Gauges Design Using Expert System (EXCAT).(Dept.M)

Tolerances and the buildup of tolerances affect the buildability, cost and quality of a product. The effect of the tolerances is so important that there are three major groups in manufacturing organization that are concerned with the tolerances which are design. process engineering and quality control. This work is a step in the field of computer-aided tolerancing (EXCAT). It consists of a helpful program which has a great important to the production and the design engineers. The program is written with an artificial intelligence programming language which is namely TURBO PROLOG, so the program is structured as mini expert system. For this reason the system contains a knowledge base for fit selections according to the function of the assembly, and limit gauges design. One of the benefits of using of the software is saving in the design and production time and so saving in their costs for the long run. This system is suitable for using in small workshop and also may belinked with CAD/CAM system.

Artificial intelligence and a universal quantum computer

In this paper we describe a complete quantum computational model of computation that is based on the production system. The presented description of the model does not require any quantum computation background. By doing so, the reason that quantum computation may be important for pure symbolical artificial intelligence may become clear. The feature universal quantum computer based on this idea will involve programs that are related to the classical artificial intelligence programming languages such as OPS5.

A rule-based and object-oriented AI programming language

This paper presents a new artificial intelligence programming language called ROOP which is machine independent and has the ability to deal with logical programming in general. ROOP is a multiple paradigm language which combines rule-based, procedure, logic and object-oriented programming techniques.

Artificial Intelligence Languages in Engineering Education

Artificial intelligence languages in engineering education This paper looks at the two most commonly used artificial intelligence programming languages, LISP and PROLOG. The differences between conventional programming and symbolic computing and between LISP and PROLOG are presented. Some comparisons between code in PASCAL, LISP and PROLOG are given.

A recursive economic dispatch algorithm for assessing the costs of thermal generator schedules

The authors develop an efficient, recursive algorithm for determining the economic power dispatch of thermal generators within the unit commitment environment. The algorithm uses the equal incremental fuel cost criterion as its basis. In the algorithm, the fuel cost functions of the thermal generators are modeled by quadratic polynomials and the transmission losses are discounted. A method for incorporating the operation limits of the online generators and limits due to ramping generators is developed. The algorithm is amenable to computer implementation using the artificial intelligence programming language Prolog. The performance, of the algorithm was demonstrated through its application to the evaluation of the costs of dispatching 13 thermal generators within a generator schedule in a 24-h schedule horizon. >

The Master of Science in Artificial Intelligence Program at the University of Georgia

Abstract The interdisciplinary Master of Science program in Artificial Intelligence at the University of Georgia is intended to prepare students for careers as developers of artificial intelligence applications or for further graduate work in artificial intelligence or related areas. The program includes foundational courses in computer science, linguistics, logic, philosophy, and psychology as well as specialized courses in artificial intelligence programming languages and techniques. Seminars emphasize knowledge-based systems, natural language understanding, and logic programming. Students are admitted to the program with degrees in many areas including business, computer science, education, linguistics, philosophy, and psychology. A liberal undergraduate education with some previous experience in computing is desirable. It normally takes two years to complete all prerequisites, all required courses, and the thesis.

GENPRO: automatic generation of Prolog clause files for knowledge-based systems in the biomedical sciences

With the increasing interest in using knowledge-based approaches for protein structure prediction and modelling, there is a requirement for general techniques to convert molecular biological data into structures that can be interpreted by artificial intelligence programming languages (e.g. Prolog). We describe here an interactive program that generates files in Prolog clausal form from the most commonly distributed protein structural data collections. The program is flexible and enables a variety of clause structures to be defined by the user through a general schema definition system. Out method can be extended to include other types of molecular biological database or those containing non-structural information, thus providing a uniform framework for handling the increasing volume of data available to knowledge-based systems in biomedicine.

Software Reviews : IF/THEN ...

This set of LOTUS 1-2-3 templates is a tutorial on basic expert system concepts, including explanations and demonstrations of backward chaining, forward chaining, stacks, depth-first and breadth-first searching, knowledge representation, demons and inheritance, and uncertainty. IF/THEN .. illustrates the ubiquitous LOTUS 1-2-3 as a programming language and transcends the requirement of knowing a specialized artificial intelligence (AI) programming language like LISP or Prolog to understand expert systems.

Lisp: An AI programming language

Lisp has been the programming language of choice of most artificial intelligence (AI) researchers since the very early days of AI research. There are many reasons why this is so, some of which will be examined later in this paper, but perhaps the simplest reason is functional crispness. Every language has arbitrary features, and most languages have been overloaded with them in order to support diverse constituencies. Lisp is built around a kernel of mathematical principles which makes it far more flexible than most other (perhaps all) computer programming languages.

SOFTWARE FEATURES FOR INTELLIGENT INDUSTRIAL ROBOTS

The automation of complex handling and assembly operations can be achieved by the introduction of industrial robots, computer‐controlled and equipped with simple tactic sensors. A difficult problem in such robots are emergency situations when a defective component part is encountered. The lack of the capability for an automatic emergency recovery is a serious limitation of the present industrial robots. The purpose of this paper is to investigate and illustrate these emergency situations and the problem of emergency recovery, and to propose a solution by suitable software. It is also pointed out that some features of Artificial Intelligence programming languages are suitable for solving this problem in an industrial robot. The problem of suitable software for such robots is then approached on the basis of the obtained results implemented on a UNIVAC 1108 computer, by simulating the behaviour of a SIGMA robot.

Artificial Intelligence Programming Languages for Computer Aided Manufacturing

Eight Artificial Intelligence programming languages (SAIL, LISP, MICROPLANNER, CONNIVER, MLISP, POP-2, AL, and QLISP) are presented and surveyed, with examples of their use in an automated shop environment. Control structures are compared, and distinctive features of each language are highlighted. A simple programming task is used to illustrate programs in SAIL, LISP, MICROPLANNER, and CONNIVER. The report assumes reader knowledge of programming concepts, but not necessarily of the languages surveyed.

A Model for Control Structures for Artificial Intelligence Programming Languages

Newer programming languages for artificial intelligence extend the class of available control regimes beyond simple hierarchical control. In so doing, a key issue is using a model that clearly exhibits the relation between modules, processes, access environments, and control environments. This paper presents a model which is applicable to diverse languages and presents a set of control primitives which provide a concise basis on which one can define almost all known regimes of control.