Artificial intelligence approach to support statistical quality control teaching

Statistical quality control – SQC (consisting of Statistical Process Control, Process Capability Studies, Acceptance Sampling and Design of Experiments) is a very important tool to obtain, maintain and improve the Quality level of goods and services produced by an organization. Despite its importance, and the fact that it is taught in technical and college courses, as well as in companies’ training sectors, SQC has been largely misused. An inappropriate teaching approach may be the cause of such problem; therefore it has motivated the development of a model for SQC teaching, allowing its learners to correctly apply SQC techniques. After a survey regarding the concept needed to correctly apply SQC, its use and teaching/training methods, the model’s contents and methodology were defined. We also realized the opportunity of incorporating a computer environment for the model, permitting the practice of the needed SQC concepts and skills. An Artificial Intelligence approach was used to develop the computer environment, resulting in an Intelligent Tutoring System, the STCEQ. The paper discusses the main characteristics of the system, its functioning, benefits of using such a system and the results we obtained while using this system.     

人工智能的哲学思考

人工智能从诞生之日起就备受社会各界的广泛关注,经过50多年的发展,现在已经成为一门综合性的前沿学科.人工智能的发展加速了科学技术的发展,同时对哲学的发展也起到了积极的推动作用.人工智能的飞速发展带给人类和社会的变化是深刻的,从哲学的角度去分析人工智能与人类智能的关系,对人工智能进行深入的哲学思考,能更好指导人类实践,使人工智能为人类、为社会创造更大的价值,积极推动社会的协调发展.     

Artificial intelligence and statistics

Artificial intelligence (AI) is intrinsically data-driven. It calls for the application of statistical concepts through human-machine collaboration during the generation of data, the development of algorithms, and the evaluation of results. This paper discusses how such human-machine collaboration can be approached through the statistical concepts of population, question of interest, representativeness of training data, and scrutiny of results (PQRS). The PQRS workflow provides a conceptual framework for integrating statistical ideas with human input into AI products and researches. These ideas include experimental design principles of randomization and local control as well as the principle of stability to gain reproducibility and interpretability of algorithms and data results. We discuss the use of these principles in the contexts of self-driving cars, automated medical diagnoses, and examples from the authors’ collaborative research.     

Artificial intelligence and symbols

The introduction of massive parallelism and the renewed interest in neural networks gives a new need to evaluate the relationship of symbolic processing and artificial intelligence. The physical symbol hypothesis has encountered many difficulties coping with human concepts and common sense. Expert systems are showing more promise for the early stages of learning than for real expertise. There is a need to evaluate more fully the inherent limitations of symbol systems and the potential for programming compared with training. This can give more realistic goals for symbolic systems, particularly those based on logical foundations.     

一种R＆D项目选择的人工智能方法

针对目前Ｒ＆Ｄ项目选择方法中存在的种种不足，提出了一种人工智能方法。该方法分为两部分：第一部分采用信息树方法来帮助决策者提高对Ｒ＆Ｄ项目选择过程的认识，说明了Ｒ＆Ｄ项目选择信息树模型实际上是一个认知图模型；第二部分采用前馈式神经网络的方法来进行Ｒ＆Ｄ项目选择中的多准则决策。     

Artificial intelligence and financial services

The author surveys key requirements and specific design techniques for artificial intelligence (AI) applications in the financial services industry. After discussing some of the fundamental challenges which the financial services industry presents for decision technology, the motivations for the use of AI are related to a number of typical applications, which are broadly categorized into suitable development and delivery environments and generic domain utilities required from shells or customized systems for that industry. Markets and trades are then shown to be well described in terms of knowledge representation and data structures, by object-oriented features embedded into logic programming environments. Conflict resolution strategies, preference aggregation and revealed beliefs are studied. A formal analysis of investment decision criteria is given. An example from currency risk management exemplifies this area. Intelligent information screens are presented to stress the need for knowledge-based techniques in financial information retrieval     

Artificial intelligence and economic modelling

Abstract: Artificial intelligence has emerged from the toy problem world and is being applied to real domains in a more general way, the existence of several large application systems supporting the belief that a generation of smarter and more general systems will be developed. However, a new problem, sometimes referred to as the fusion problem, has been identified, which acts to restrict the development of such systems. This paper explains the nature of the problem, and by examining a proposed expert system in economics (ESE), discussing three approaches to a prototype ESE and the problems associated with them, draws some conclusions with regard to data fusion and expert co-operation.     

Artificial intelligence and robotic assembly

Traditionally, most industrial robots are programmed by teaching. The emergence of robot-level programming languages has improved the programmer's ability to describe and modify the robot moves. However, commercially available robot-level programming languages still fall short of the robot user's need to program complex tasks, and consequently, are not widely used in industry. There is an increasing need for integrating sensors feedback into the robot system to provide better perception and for improving the capacity of the robot to reason and make decisions intelligently in real time.The role of artificial intelligence in programming and controlling robots is discussed. Available robot programming systems including robot-level, object-level, and task-level languages are reviewed. The importance of developing intelligent robots in broadening the scope of flexible automation and opening the door to new robotic applications in space, under water and in harsh environments is outlined. The current development and implementation of programming and control systems for intelligent robots, at McMaster University, are explained. A number of research issues are discussed such as (1) automatic task planning, (2) knowledge representation and use, (3) world modeling, (4) reasoning in automatic assembly planning, and (5) vision monitoring of actions. Examples of geometric, functional, and handling reasoning, as they apply to assembly, are provided. The systems described in this paper are being implemented in the center for flexible manufacturing research and development. Several pieces of hardware are used, including a six-axis articulated robot, a grey-level vision system with a multi-camera, Micro VAX II, and a variety of graphics monitors. The languages available for software development include Common LISP, C, OPS5, VAL II, PASCAL, and FORTRAN 77. The domain of application is currently focused on mechanical assembly.     

Artificial intelligence and natural magic

Robotics with AI is part of a long tradition that has run from ancient times that treated the precursors of robots, the automata, as part of Natural Magic or conjury. Deception is an integral part of AI and robotics; in some ways they form a science of illusion. There are many robot tasks, such as caring for the elderly, minding children, doing domestic chores and being companionable, that involve working closely with humans and so require some illusion of animacy and thought. We discuss how the natural magic of robotics is assisted by the cultural myth of AI together with innate human predispositions such as zoomorphism, the willing suspension of disbelief and a tendency to interpret AI devices as part of the social world. This approach provides a justifiable way of meeting the goals of AI and robotics provided that researchers do not allow themselves to be deceived by their own illusions.     

Artificial intelligence solution to transmission loss allocation problem

The problem of transmission loss allocation of deregulated power system has been solved through the application of artificial neural network (ANN). Two network structures namely Levenberg–Marquardt back propagation (LMBP) and Bayesian regularization back propagation (BRBP) have been trained and their performance compared. It has been found that LMBP network gives faster solution for same accuracy level. As the working range of power flow transaction is quite vast, a huge volume of data need to be stored and processed for the training of neural network. The time needed for training of neural network against such huge data is prohibitive for real time application of the ANN based solution tool where raw data are used for training. A simple filtering technique has been found to be very effective to improve the solution time and training data volume requirement and make the proposed technique suitable for real time applications. With the use of filtered data for training both the training network have shown comparable performance.     

Artificial intelligence: consciousness and conscience

AI & SOCIETY - Our society is in the middle of the AI revolution. We discuss several applications of AI, in particular medical causality, where deep-learning neural networks screen through big...     

Artificial intelligence and automatic programming in CAI

This paper discusses generative computer-assisted instruction (CAI) and its relationship to Artificial Intelligence Research. Systems which have a limited capability for natural language communication are described. In addition, potential areas in which Artificial Intelligence could be applied are outlined. These include individualization of instruction, determining the degree of accuracy of a student response, and problem-solving.A CAI system which is capable of writing computer programs is described in detail. Techniques are given for generating meaningful programming problems. These problems are represented as a sequence of primitive tasks each of which can be coded in several ways. The manner in which the system designs its own solution program and monitors the student solution is also described.     

An artificial intelligence approach to language instruction

This paper describes an implemented, prototype system for a sophisticated, intelligent tutor for instruction in a foreign language. The system is an application of artificial intelligence research in natural language, but it implements several ideas that depart from standard approaches to natural language understanding.

For instance, the semantic analyzer diagnoses several kinds of comprehension problems and semantic errors that a student might make. Some fine distinctions in meaning are represented to detect misuse of words. Not only is a model of good syntax included in the tutor, but also a model of incorrect forms, rich enough to pinpoint specific syntactic mistakes. Finding the intended interpretation is complicated by the likelihood of student errors. Therefore, perfect syntactic form is not necessary for semantic analysis of the student's input.

The problems discussed and solutions presented are closely related to the more general problem of how to respond to a natural language input that surpasses the computer's model of language or of context.     

Hybrid artificial intelligence approach to urban planning

Knowledge-based modeling and implementation of the various urban planning processes represent an intensive research area. This paper presents a hybrid artificial intelligence system using a knowledge-based approach, neural networks and fuzzy logic that automates the decision-making process in urban planning. The system is used for developing urban development alternatives based on real-world data. Results show that, by integrating knowledge-based systems, artificial neural networks and fuzzy systems, the system achieves improvements in the implementation of each respective system as well as an increase in the breadth of functionality within the application. With this approach, the best of three technologies can be compiled together to solve complex urban problems. We discuss the structure of the combined technologies, as well as providing examples of its application in the field of urban development.     

Artificial intelligence: A contribution to systems theories of sociology

The aim of my contribution is to try to analyse some points of similarity and difference between post-Parsonian social systems theory models for sociology — with special reference to those of W. Buckley, F.E. Emery and N. Luhmann — and expert systems models¹ from Artificial Intelligence. I keep specifically to post-Parsonian systems theories within sociology because they assume some postulates and criteria derived from cybernetics and which are at the roots of AI. I refer in particular to the fundamental relevance of the system-environment relationship in both sociology and AI.