1．INTRODUCTION
Manufacturing Execution Systems (MES) applications have become essential to support real-time production control as well as data collection and require improving production performance. MES has significantly evolved into more powerful and more integrated software applications as computing technologies. This is due to the capability of MES optimizer business processes in the product supply chain, improve product of quality and ensure the safety of manufacturing processes, which can link to automation equipment to record relevant information and to help manufacturers solve these challenges by tracking each movement in real-time. Jindal et al. [1], process information is captured and validated as it happens, and manufacturers can tracks yield and cycle time by operator, equipment, production line, and adjust manufacture processes to accelerate development. Younus, M et al.[2], Integrated MES in a manufacture industry start, guide, respond to and report on manufacturing activities as they occur from order launch to finished product by improving quality and reducing costs of the product. Products can be tracked throughout the manufacture process using the work-in-process (WIP) tools in MES Factory Server, reducing time to complete the product. Hao Guangke et al [3] Service-oriented MES is well configurable so that it can facilitate system fast practices and workshop business agility. Valckenaers P et al [4] the MES performs this task in an autonomic manner, filling in missing details, providing alternatives for unfeasible assignments, handling auxiliary tasks, and so on.
MESA international [5] study of benefits is part of MESA’s aggressive research on the analysis programme designed to support developers/vendors of...
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... J. Sheppard and M. Kaufman, A Bayesian Approach to Diagnosis and Prognosis Using Built-In Test, IEEE Transactions on Instrumentation and Measurement, Vol. 54(3)pp:1003-1018 (2005).
15. J. Pearl, Probabilistic Reasoning in Intelligent Systems: networks of plausible inference. Morgan Kaufmann Publishers, Inc., San Francisco, CA. 1988.
16. J.H.Kim,and J.pearl ,A computational model for causal and diagnostic reasoning in Inference engines[C].In Proc.B`h Int. Joint Conf. On Artificial Intelligence, Karlsrube,West Germany, pp:190-193（1983)
17. D. Heckerman , Causal independence for knowledge acquisition and inference. In Proceedings.9th Conference on Uncertainty in Artificial Intelligence, pp:122-127（1993）.
18. D. Heckerman, and J. Breese ,A new look at causal independence. In Proc. of the Tenth Conference on Uncertainty in Artificial Ingelligence, pp:286-292(1994).
...dson, ‘Thinking Causes’, in Mental Causation, ed. John Heil and Alfred Mele (Oxford, Clarendon Press: 1993) p. 13.
ABSTRACT: There are good reasons for determinism — the option for pure freedom of will proves to be a non-tenable position. However, this collides with the everyday experience of autonomy. The following argument will attempt to show that determinism and autonomy are compatible. (1) A first consideration going back to MacKay makes clear that I myself cannot foresee in principle my own determination; hence fatalism has lost its grounds. (2) From the perspective of physical determination, I show that quantum-physical indetermination is not at all in a position to explain autonomy, while from the perspective of systems theory physical determination and autonomy is well-compatible. (3) The possibility of knowledge denotes a further increase of such autonomy. From this perspective, acting is something like designing-oneself or choice-of-oneself. (4) Consciousness of not being fixed in principle now becomes a determining condition of my acting, which appears to be determined by autonomy. This explains the ineradicable conviction that freedom of will is essential for human beings. (5) I conclude that the autonomy of acting is greater the more that rational self-determination takes the place of stupid arbitrariness.
Russell, Paul. “Hume on Free Will.” Stanford Encyclopedia of Philosophy. Metaphysics Research Lab, CSLI, Stanford University, 14 December 2007.
I will discuss Nelson Goodman’s understanding of the problem of induction. Inductive arguments are arguments in which the premises (propositions) provide strong evidence for the truth of its conclusion. I will begin by examining an inductive argument where using the proposition that “all observed emeralds are green”, we can conclude that “all emeralds are green”. As shown, sometimes, through such arguments we draw conclusions based on unobserved cases. This can be considered a problem (the problem of induction), especially if such conclusions are made without any justification.
Causal determinism is the concept that preceding causes give rise to everything which exists such that reality could be nothing but what it is. Science depends on this idea as it aims to find generalisations about the conjunction of certain causes and effects and thus hold some power of prediction about their future co-occurrence. However, in human interaction people assume each other to be responsible for their acts and not merely at the whim of causal laws. So the question which troubles philosophers is whether causation dictates entirely the course of human action or whether we as agents possess some free will. I will argue that free will is an inescapable illusion of the mind, something which never did nor ever could exist under causal determinism.
In this paper I will argue that the law of causality is divided to general and empirical law of causality. General law of causality earn its necessity from the fact that, even observing temporal sequences, require the concept of causation, yet, particular laws of causality cannot be necessary in this way. Accordingly, science should answer how it can have necessary judgments such as “ A is the cause of B”.
Oppy, Graham, Dowe, David, The Turing Test, The Stanford Encyclopedia of Philosophy (Summer 2003 Edition), Mar. 10, 2005 .
Paul Rée’s works refute the argument of free will with propositions backed by the principle of causality. The premises of his argument lie on the concept that every action occurs because of a prior cause. This action is the result of a chain of events, within parameters
Many theories of logic use mathematical terms to show how premises lead to conclusions. The Bayesian confirmation theory relates directly to probability. When applying this theory, a logician must know the probability of a given situation, have a conditional rule, and then he or she must apply the probability when the conditional rule is applied. This theory is used to determine an outcome based on a given condition. The probability of a given situation is x, when y occurs, or the probability is z if it does not occur. If y occurs, then the outcome of the given would be x. For example, if there is a high probability that a storm will occur if a given temperature drops and there is no temperature change, then it will most likely not rain because the temperature did not change (Strevens, 2012). By using observational data such as weather patterns, a person can arrive at a logical prediction or conclusion that will most likely come true based...
A significant function of science, and of everyday thinking, is to make sense of available information. Induction is the process of going from the specific to the general thereby reaching a conclusion about the complex nature of the universe from a , thus far, limited set of observations. A person uses a collection of evidence, gained through experience, and uses it to form a conclusion which is conceived to be conform with the given facts. This means the observations may be true, but because of the given limitation of observation the conclusion could still be proven false. David Hume has identified this problem of induction and deems it therefore as logically unjustifiable. It is, however, the primary form of reasoning in science and is used to attain inferences which the scientific community believes to be the most likely form of the observed phenomena in question within a current paradigm. Induction has established itself as an effective method in the natural sciences and is imperative for scientific advancement.
In this paper, I offer a solution to the Gettier problem by adding a fourth condition to the justified true belief analysis of knowledge. First though, a brief review. Traditionally, knowledge had been accounted for with the justified true belief analysis. To know something, three conditions had to be met: first, you had to have a belief; second, the belief had to be justified; third, this justified belief had to be true. So a justified true belief counts as knowledge. Gettier however showed this analysis to be inadequate as one can have a justified true belief that no one would want to count as knowledge.
Crevier, D. (1999). AI: The tumultuous history of the search for Artificial Intelligence. Basic Books: New York.
Inductive reasoning can be quickly summarized as a method through which a conclusion is drawn from particular cases; this conclusion may be applied to another specific case or generalized. All of our conclusions about the world around us, which we rely on daily without question, are dependent on this process. The expectation that our house will not cave in, that water will come from the faucet when turned on, that we will wake the next morning, are all propositions extrapolated from inductive arguments.
In this book, Samir Okasha kick off by shortly describing the history of science. Thereafter, he moves on scientific reasoning, and provide explanation of the distinction between inductive and deductive reasoning. An important point Samir makes, is the faith that humans put into the inductive reasoning
Computer integrated manufacturing is a relatively new technology arising from the application of many computer science sub disciplines to support the manufacturing enterprise. The technology of CIM emphasizes that all aspects of manufacturing should be not only computerized as much as possible but also linked together via a computer communication network into an integrated whole. In short, CIM has the potential to enable manufacturers to build cheaper, higher-quality products and thus improve their competitiveness.