MPS: An artificially intelligent software system for the analysis and synthesis of metabolic pathways

1988 ◽  
Vol 31 (6) ◽  
pp. 587-602 ◽  
Author(s):  
Alex Seressiotis ◽  
James E. Bailey
Keyword(s):  
Metabolic Pathways ◽  
Software System ◽  
Intelligent Software ◽  
Analysis And Synthesis

scholarly journals Usage of an ıntelligent software system in teaching algorithm and flowchart concepts

2015 ◽  
Vol 5 (5) ◽  
pp. 569-586 ◽  
Author(s):  
Utku Köse ◽  
Aslıhan Tüfekçi
Keyword(s):  
Artificial Intelligence ◽  
Educational Software ◽  
Computer Programming ◽  
Evaluation Process ◽  
Programming Algorithm ◽  
Software System ◽  
Success Rates ◽  
General Evaluation ◽  
Intelligent Software ◽  
Educational Processes

Objective of this work is to introduce an Artificial Intelligence supported educational software system, which has been developed for teaching important subjects of computer programming: algorithm and flowchart concepts, and touch upon the findings, which were obtained for evaluating success of the system. The software system introduced in the work is tightly connected to the rule of teaching essential computer programming concepts, but ensures this task in the context of an Artificial Intelligence supported, intelligent mechanism. In order to have idea about whether the software is an effective teaching tool or not, a general evaluation process has been planned; students subjected to the work have been taken into this process. Findings obtained via evaluation process have shown that the developed software system is successful at effectively teaching the algorithmic thinking logic, which is the essential focus, and algorithm - flowchart concepts. Additionally, it is also possible to express that the software system has improved students' success rates in the courses related to essentials of computer programming and students have found both software and the performed educational processes pretty effective.

scholarly journals Intelligent software system for optimizing adaptive control of business planning processes

2020 ◽  
Vol 15 (89) ◽  
pp. 9-28
Author(s):  
Andrey F. Shorikov ◽  
◽  
Elena V. Butsenko ◽  
Keyword(s):  
Mathematical Modeling ◽  
Decision Making ◽  
Adaptive Control ◽  
Business Plan ◽  
Optimal Time ◽  
Software System ◽  
Business Planning ◽  
Intelligent Software ◽  
The Face ◽  
Planning Processes

The article describes the functionality developed by the authors of an intelligent software system for optimizing adaptive control of business planning processes in the face of uncertainty. The results are based on a new method for optimizing adaptive project management using network economic and mathematical modeling. Based on this method, a methodology has been developed for solving the problem of optimizing adaptive control of business planning processes, which in the proposed intelligent software decision support system uses a block containing an adaptive control optimization model. As the objective function (evaluation functional) in the method used, the value of the length of the time period for the execution of the business plan, which needs to be minimized, is considered. The method used allows you to create a class of acceptable strategies for adaptive control of the implementation process for the business plan in question. Within the framework of this class of strategies, an optimal adaptive control strategy for the implementation of business planning processes is formed, the optimal time for its implementation and the optimal schedule for implementing the business plan as a whole, and the corresponding optimal adaptive control strategies are calculated. Application of the proposed new method in an intelligent software system allows for feedback and optimal time for the implementation of the business project as a whole. The developed intelligent system is designed to automate the modeling of business planning processes and optimize adaptive decision-making control during their implementation on the basis of network economic and mathematical modeling, as well as methods and tools for developing intelligent soft systems. The created system takes into account the existing specific technical and economic conditions and information support. The results obtained in this work can serve as the basis for creating intelligent instrumental systems for supporting managerial decision-making in the implementation of business planning processes in the face of information uncertainty and risks.

scholarly journals Analysis and synthesis of bird vocalizations: An FFT-based software system

1983 ◽  
Vol 15 (2) ◽  
pp. 251-253 ◽  
Author(s):  
Christopher Clark ◽  
Robert J. Dooling ◽  
Timothy Bunnell
Keyword(s):  
Software System ◽  
Analysis And Synthesis

scholarly journals Intelligent software system for replacing a force sensor in the case of clearance measurement

Procedia CIRP ◽  
2019 ◽  
Vol 79 ◽  
pp. 517-522 ◽  
Author(s):  
Nicolas Meier ◽  
Jan Papadoudis ◽  
Anthimos Georgiadis
Keyword(s):  
Force Sensor ◽  
Software System ◽  
Intelligent Software

scholarly journals Autonomous Driving in Traffic: Boss and the Urban Challenge

AI Magazine ◽  
2009 ◽  
Vol 30 (2) ◽  
pp. 17 ◽  
Author(s):  
Chris Urmson ◽  
Chris Baker ◽  
John Dolan ◽  
Paul Rybski ◽  
Bryan Salesky ◽  
...  
Keyword(s):  
Real Time ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Software System ◽  
Intelligent Software ◽  
The World

The DARPA Urban Challenge was a competition to develop autonomous vehicles capable of safely, reliably and robustly driving in traffic. In this article we introduce Boss, the autonomous vehicle that won the challenge. Boss is complex artificially intelligent software system embodied in a 2007 Chevy Tahoe. To navigate safely, the vehicle builds a model of the world around it in real time. This model is used to generate safe routes and motion plans in both on roads and in unstructured zones. An essential part of Boss’ success stems from its ability to safely handle both abnormal situations and system glitches.

scholarly journals A Software System for Online Learning Applied in the Field of Computer Science

2007 ◽  
Vol 2 (1) ◽  
pp. 84 ◽  
Author(s):  
Gabriela Moise
Keyword(s):  
Online Learning ◽  
Computer Science ◽  
Intelligent Agents ◽  
Online Teaching ◽  
Semantic Networks ◽  
Computer Assisted ◽  
Software System ◽  
Computer Assisted Learning ◽  
Computer Science Courses ◽  
Intelligent Software

<p>The computer-assisted learning is a very modern study area, which can be applied to the learning process. The main objective of this paper is to present a software system for online learning based on the intelligent software agents technologies. The main ideas on which this paper is built are: to any person is associated a learning profile (the idea is based on the existence of multiple intelligences, defined by Gardner [3]); the pedagogical resources can be shaped through educational semantic networks or through conceptual maps; a flexible software system in computer assisted learning must be based on the intelligent agents’ technology. The system dedicated to computer-assisted learning must be adapted to the learning profile of each student.<br />The author presents a flexible online teaching software system, which learns to teach according to the learning profile of each student (the author defines this system in the PhD thesis and includes: intelligent agent structures, reward learning algorithms, algorithms to generate plans for an agent).<br />The application includes two agents: the supervising agent and the pedagogical agent, which determines the optimal pedagogical resources for teaching the course. The application has been designed in Microsoft Visual Studio 6.0 and uses Microsoft Agent Technology, which allows vocal recognition. Also, the Protéjé 3.0 software has been used, software that allows building ontology for computer assisted learning. The system has been experimented on the Graph Theory Course, taught at postuniversitary computer science courses, the results proving the necessity of defining a strategy for selecting the pedagogical resources presented to the students according to their learning profile.</p>

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