Developing robot programming languages using an existing language as a base—A viewpoint

Robotica â—˝  
1989 â—˝  
Vol 7 (1) â—˝  
pp. 71-77 â—˝  
Author(s):  
S. T. Rock
Keyword(s):  
Artificial Intelligence â—˝  
Programming Languages â—˝  
Real Time â—˝  
Programming Language â—˝  
Robot Programming â—˝  
Intelligent Robot â—˝  
On Line

SUMMARYThe development of robot languages has followed a pattern similar to that of conventional programming languages, where robot languages have been based on an existing programming language. This paper first identifies the use of an existing base as one way of developing robot programming languages, and discusses the areas of difficulty in this approach. Then, on-line and off-line programming of robots is discussed and the requirements of robot programming languages that are different to those of non-specialised programming languages are presented. A discussion and evaluation of some programming languages in terms of their appropriateness for use as the base for an intelligent robot programming language is presented. This leads to the conclusion that no current language forms an adequate base for intelligent robot programming languages. What is needed as a base is a language for use in the artificial intelligence domain, that incorporates real-time facilities.

RESCU—on-line real-time artificial intelligence

1987 â—˝  
Vol 4 (1) â—˝  
pp. 29 â—˝  
Author(s):  
Ray Shaw
Keyword(s):  
Artificial Intelligence â—˝  
Real Time â—˝  
On Line

IoT-based Flood Depth Sensors in Artificial Intelligent Urban Flood Warning Systems

2020 â—˝  
Author(s):  
Li-Chiu Chang â—˝  
Fi-John Chang
Keyword(s):  
Artificial Intelligence â—˝  
Real Time â—˝  
Value Added â—˝  
Flood Forecasting â—˝  
Flood Inundation â—˝  
Disaster Prevention â—˝  
Real Time Monitoring â—˝  
Flood Depth â—˝  
Flood Warning â—˝  
On Line

<p>In the face of increasingly flood disasters, on-line regional flood inundation forecasting in urban areas is vital for city flood management, while it remains a significant challenge because of the complex interactions and disruptions associated with highly uncertain hydro-meteorological variables and the lack of high-resolution hydro-geomorphological data. Effective on-line flood forecasting models through the rapid dissemination of inundation information regarding threatened areas deserve to develop appropriate technologies for early warning and disaster prevention. Artificial Intelligence (AI) becomes one of the popular techniques in the study of flood forecasts in the last decades. We apply the AI techniques with the newly implemented IoT-based real-time monitoring flood depth data to build an urban AI flood warning system. The AI system integrates the self-organizing feature mapping networks (SOM) with the recurrent nonlinear autoregressive with exogenous inputs network (R-NARX) for modelling the regional flooding prediction. The proposed AI model with the IoT-based real-time monitoring flood depth datasets can increase the value-added application of diversified disaster prevention information and improve the accuracy of flood forecasting. We develop an on-line correction algorithm for continuously learning and correcting model’s parameters, automatic operation modules, forecast results output modules, and web page display interface. The proposed AI system can provide the smart early flooding warnings in the urban area and help the Water Resources Agency to promote the intelligent water disaster prevention services.</p><p>Keywords:</p><p>Artificial Intelligence (AI); Artificial Neural Networks (ANN); Internet of Things (IoT); Regional flood inundation forecast; Spatial-temporal distribution</p>

Method for Planning Multitasking Robot Application Programs in RAPID

2010 â—˝  
Vol 166-167 â—˝  
pp. 69-76
Author(s):  
Stelian Brad â—˝  
Emilia Brad â—˝  
Cosmin Ioanes
Keyword(s):  
Software Development â—˝  
Programming Languages â—˝  
Programming Language â—˝  
Robot Programming â—˝  
Careful Planning â—˝  
Planning Tools â—˝  
Robot Tasks â—˝  
Set Up â—˝  
Application Programs

In order to set up well-structured multitasking robot application programs careful planning is required. Robot programming languages (e.g. Karel, RAPID, Melfa, SimPro, etc.) vary from robot to robot constructor. General planning tools used in software development (e.g. UML, IDEF, etc.) require adequate professional skills and a special way of thinking such that robot programmers to apply and adapt them to the specificity of each robot programming language. Customized and intuitive planning tools of robot applications with regard to each particular programming language seem to be preferred by ordinary robot programmers and operators when facing with the development of complex robot tasks. This paper introduces such a tool in relation to the RAPIDTM programming language, specific to ABB robot models. Its effectiveness is revealed in a case study.

PYTHON PROGRAMMING LANGUAGE IN PEDAGOGICAL UNIVERSITY: FROM THE BASES TO ARTIFICIAL INTELLIGENCE

2019 â—˝  
pp. 26-33 â—˝  
Author(s):  
K. V. Rozov â—˝  
A. V. Podsadnikov
Keyword(s):  
Artificial Intelligence â—˝  
Programming Languages â—˝  
Programming Language â—˝  
Professional Training â—˝  
Future Teachers â—˝  
Python Programming Language â—˝  
Artificial Intelligence In Education â—˝  
Key Issues â—˝  
Organizational Features â—˝  
Python Programming

The article actualizes the choice of Python programming language for professional training of future teachers of informatics in pedagogical university. Python, being one of the most relevant programming languages, is being introduced into education, and the future informatics teacher should be ready to effectively use it to teach the basics of programming. In addition, the Python language is one of the leading tools for the implementation of advanced technologies of artificial intelligence, and therefore, owning this programming language teacher can be part of the mechanism of implementation of artificial intelligence in education. The objective of the article is to consider the features of the organization of the course of programming in Python for the training of future teachers of informatics. Methodology. During the development of the author’s course of programming in Python educational literature on programming in this language was analyzed, as well as articles by practicing teachers and researchers describing their experience of teaching Python. Results and conclusion. To ensure quality training of future teachers of informatics at Novosibirsk State Pedagogical University a course of programming in Python was developed and tested in the discipline “Programming”. During the development of the course, typical problems of teaching Python at school and university were taken into account, identified by both the authors of the article and other practicing programming teachers. The structure of the developed course with the description of its constituent sections is given. The article presents the results of a survey of students of 3d and 4th undergraduate courses, who completed the presented course, on the key issues covered in the article. Respondents noted the relevance of learning Python and the effectiveness of certain organizational features of the course. The continuity of the course for further training of future teachers of informatics to the current technologies of artificial intelligence is revealed.

Using Prolog for Developing Real World Artificial Intelligence Applications

2011 â—˝  
pp. 3960-3964
Author(s):  
Athanasios Tsadiras
Keyword(s):  
Artificial Intelligence â—˝  
Information Technology â—˝  
Programming Languages â—˝  
Programming Language â—˝  
Specific Sequence â—˝  
First Order â—˝  
Search Mechanism â—˝  
High Level â—˝  
The University â—˝  
Order Predicate Calculus

Artificial Intelligence Applications are becoming crucial for enterprises that want to be successful by having the advantage of using high information technology. The development of such applications is assisted by the use of high level computer programming languages that are closer to the programmer than to the computer. Such a programming language is Prolog. Prolog is a logic programming language (Clocksin & Mellish 2003) that was invented by Alain Colmerauer and Phillipe Roussel at the University of Aix-Marseille in 1971. The name Prolog comes from programmation en logique (i.e., “programming in logic” in French). Together with LISP, they are the most popular Artificial Intelligence programming languages. Prolog was generated by an attempt to develop a programming language that extensively uses expressions of logic instead of developing a program by providing a specific sequence of instructions to the computer. Theoretically, it is based on a subset of first-order predicate calculus that allows only Horn clauses (Bratko, 2000). The control of the program execution is based on Prolog’s built-in search mechanism that in fact is an application of theorem proving by first-order resolution.

scholarly journals JAVA and DART programming languages: conceptual comparison

2020 â—˝  
Vol 17 (2) â—˝  
pp. 845 â—˝  
Author(s):  
Afaf Mirghani Hassan
Keyword(s):  
Programming Languages â—˝  
Real Time â—˝  
Programming Language â—˝  
Implementation Strategies â—˝  
Similarities And Differences

<p>This paper elaborates on the concepts of a new programming language “Dart”, which has been developed by Google and considered for future use. Here, we compare it to the most famous, real time, and updated language “Java”. This is to define similarities and differences between the two important languages, explain programs’ behavior, with a focus on investigating alternative implementation strategies and problem definitions. We used programming languages’ concepts and terminologies to compare between the main characteristics of the two languages, Dart &amp; Java.</p>

Adaptive Signal Analysis and Interpretation for Real-time Intelligent Patient Monitoring

1994 â—˝  
Vol 33 (01) â—˝  
pp. 60-63 â—˝  
Author(s):  
E. J. Manders â—˝  
D. P. Lindstrom â—˝  
B. M. Dawant
Keyword(s):  
Computer Architecture â—˝  
Real Time â—˝  
Data Abstraction â—˝  
Monitoring Strategy â—˝  
Intelligent Monitoring â—˝  
Patient Status â—˝  
On Line â—˝  
Main Components â—˝  
And Control â—˝  
Adaptive Signal

Abstract:On-line intelligent monitoring, diagnosis, and control of dynamic systems such as patients in intensive care units necessitates the context-dependent acquisition, processing, analysis, and interpretation of large amounts of possibly noisy and incomplete data. The dynamic nature of the process also requires a continuous evaluation and adaptation of the monitoring strategy to respond to changes both in the monitored patient and in the monitoring equipment. Moreover, real-time constraints may imply data losses, the importance of which has to be minimized. This paper presents a computer architecture designed to accomplish these tasks. Its main components are a model and a data abstraction module. The model provides the system with a monitoring context related to the patient status. The data abstraction module relies on that information to adapt the monitoring strategy and provide the model with the necessary information. This paper focuses on the data abstraction module and its interaction with the model.

Real-Time Frame-Rate Control for Energy-Efficient On-Line Object Tracking

2018 â—˝  
Vol E101.A (12) â—˝  
pp. 2297-2307
Author(s):  
Yusuke INOUE â—˝  
Takatsugu ONO â—˝  
Koji INOUE
Keyword(s):  
Object Tracking â—˝  
Real Time â—˝  
Rate Control â—˝  
Energy Efficient â—˝  
Time Frame â—˝  
Frame Rate â—˝  
On Line

PERANGKAT LUNAK KOMPUTER

2020 â—˝  
Author(s):  
Cut Nabilah Damni
Keyword(s):  
Programming Languages â—˝  
Programming Language â—˝  
Operating Systems â—˝  
Source Code â—˝  
Computer Software â—˝  
Computer Programs â—˝  
Application Systems â—˝  
Executable Programs

AbstrakSoftware komputer atau perangkat lunak komputer merupakan kumpulan instruksi (program atau prosedur) untuk dapat melaksanakan pekerjaan secara otomatis dengan cara mengolah atau memproses kumpulan intruksi (data) yang diberikan. (Yahfizham, 2019 : 19) Sebagian besar dari software komputer dibuat oleh (programmer) dengan menggunakan bahasa pemprograman. Orang yang membuat bahasa pemprograman menuliskan perintah dalam bahasa pemprograman seperti layaknya bahasa yang digunakan oleh orang pada umumnya dalam melakukan perbincangan. Perintah-perintah tersebut dinamakan (source code). Program komputer lainnya dinamakan (compiler) yang digunakan pada (source code) dan kemudian mengubah perintah tersebut kedalam bahasa yang dimengerti oleh komputer lalu hasilnya dinamakan program executable (EXE). Pada dasarnya, komputer selalu memiliki perangkat lunak komputer atau software yang terdiri dari sistem operasi, sistem aplikasi dan bahasa pemograman.AbstractComputer software or computer software is a collection of instructions (programs or procedures) to be able to carry out work automatically by processing or processing the collection of instructions (data) provided. (Yahfizham, 2019: 19) Most of the computer software is made by (programmers) using the programming language. People who make programming languages write commands in the programming language like the language used by people in general in conducting conversation. The commands are called (source code). Other computer programs called (compilers) are used in (source code) and then change the command into a language understood by the computer and the results are called executable programs (EXE). Basically, computers always have computer software or software consisting of operating systems, application systems and programming languages.

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