Ways of Applying Artificial Intelligence in Software Engineering

As Artificial Intelligence (AI) techniques have become more powerful and easier to use they are increasingly deployed as key components of modern software systems. While this enables new functionality and often allows better adaptation to user needs it also creates additional problems for software engineers and exposes companies to new risks. Some work has been done to better understand the interaction between Software Engineering and AI but we lack methods to classify ways of applying AI in software systems and to analyse and understand the risks this poses. Only by doing so can we devise tools and solutions to help mitigate them. This paper presents the AI in SE Application Levels (AI-SEAL) taxonomy that categorises applications according to their point of AI application, the type of AI technology used and the automation level allowed. We show the usefulness of this taxonomy by classifying 15 papers from previous editions of the RAISE workshop. Results show that the taxonomy allows classification of distinct AI applications and provides insights concerning the risks associated with them. We argue that this will be important for companies in deciding how to apply AI in their software applications and to create strategies for its use

Development and Evaluation of the Oracle Intelligent Tutoring System (OITS)

This paper presents the design and development of intelligent tutoring system for teaching Oracle. The Oracle Intelligent Tutoring System (OITS) examined the power of a new methodology to supporting students in Oracle programming. The system presents the topic of Introduction to Oracle with automatically generated problems for the students to solve. The system is dynamically adapted at run time to the student’s individual progress. An initial evaluation study was done to investigate the effect of using the intelligent tutoring system on the performance of students

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

SOCIO-ECONOMIC IMPLICATIONS OF COMBRAINS

Human brain has invented the Computer&upgraded it to a level of Combrains. With Artificial Chemical Memory, these may grow to function as independent Iintellects, Master/Sponsor representatives and self- decision workers with autonomy&supreme capability. Like any human society learn and function with both constructive and destructive ways,the Combrains will too behave. But the dimension of the Combrain behaviour is wide&cover the whole world with Internet. Even if they are a menace, it will be impossible to stop their growth, motivated by the decreasing cost versus the their analysis,search & inference capabilities. As a result,the Governments have to enunciate different laws to control these inanimate Combrains. The best utility of the Combrain will be to identify,plan&derive implementation methods for Basic Needs;Domestic Investment,Savings,Technology;Labour;Management Decision& Productivity Monitoring; and prediction&preparation for the impact of Intangible components in Development processArtilect, Attraction, Basic Needs, Behaviour, Brain, Caution, Chemical, Combrain, Community, Competition, Constructive, Darwin, Decision, Discipline, Domestic, Dynamic, Expert, Fittest, Gravitation, Inanimate, Intangible, Internet, Investment, Labour, Law, Legislation, Master, Memory, Modules, Neuron, Polymer, Productive, Reaction, Repulsion, Saving, Sponsor, Technology, Wage earner

Technology assessment of advanced automation for space missions

Six general classes of technology requirements derived during the mission definition phase of the study were identified as having maximum importance and urgency, including autonomous world model based information systems, learning and hypothesis formation, natural language and other man-machine communication, space manufacturing, teleoperators and robot systems, and computer science and technology