Uncertainty Using Probabilities and Other Methods to deal with Uncertain Data and Knowledge ...
With uncertainty, an agent typically cannot guarantee to satisfy its goals, and even trying to maximize the probability of achieving a goal may not be sensible.
Uncertainty Coefficients. These are indices of stochastic dependence; the concept of stochastic dependence is derived from the information theory approach to the analysis of frequency tables and the user should refer to the appropriate references (see Kullback, 1959; Ku & Kullback, 1968; Ku, ...
uncertainty_rule r1 if e1 is high ( affirms 3.20; denies 0.895 ) and e2 is high ( affirms 9.00; denies 0.895 ) then h is high .
3 Uncertainty management in rulebased expert systems 55 3.1 Introduction, or what is uncertainty? 55 ...
Uncertainty has presented a difficult obstacle in artificial intelligence. Bayesian learning outlines a mathematically solid method for dealing with uncertainty based upon Bayes' Theorem.
Uncertainty Analysis and Other Inference Tools for Complex Computer Codes. Anthony O'Hagan, Marc C. Kennedy and Jeremy E. Oakley v  d  e Statistics ...
Energy and Uncertainty: Models and Algorithms for Complex Energy Systems PDF Warren Powell ...
Controlling Uncertainty: Decision Making and Learning in Complex Worlds Book by Dr Magda Osman Discovery Channel ...
Uncertainty  In the context of expert systems, uncertainty refers to a value that cannot be determined during a consultation. Many expert systems can accommodate uncertainty.
Goals have some uncertainty of being achieved and you need to weigh likelihood of success against the importance of a goal. Nature of Environments ...
[19] "Fuzzy Sets, Uncertainty, and Information", by G.J. Klir and T.A. Folger (PrenticeHall, Englewood Cliffs, N.J., 1988). [20] "Industrial Applications of Fuzzy Control" ed. M. Sugeno (NorthHolland, New York, 1985). Ahead to Part 6 ...
reasoning under uncertainty reasoning about situations, e.g. in medical diagnosis, in which good or complete data is not available, and in which decisions must be made based on available data and knowledge of likelihoods of the various possibilities.
FUD  Fear, Uncertainty and Doubt (the classic marketing technique of IBM back in the badold days...now practiced by Microsoft [8]) FWIW  For What It's Worth FYI  For Your Information GIF  Graphic Interchange Format GIYF  Google Is Your Friend ...
Management of Imprecision and Uncertainty: Data and knowledgebases in many typical AI problems, such as reasoning and planning, are often contaminated with various forms of incompleteness. The incompleteness of data, hereafter called imprecision, generally appears in the database for i) lack of ...
Can also be used to make decisions where uncertainty occurs (fuzzy control). This is a form of nonAristotelian logic (see general semantics). Game Theory The study of interactions between intelligent agents, concentrating on whether outcomes are zero, positive or negative sum.
A modeling technique that provides a mathematically sound formalism for representing and reasoning about uncertainty, imprecision, or unpredictability in our knowledge. For example, seeing that the front lawn is wet, one might wish to determine whether it rained during the previous night.
Many otherwise excellent colleagues have objected that nanotechnology might be impractical because of quantum uncertainty. "You cannot be sure where the atoms are," they would complain, grumbling about the uncertainty principle, tunneling, and strange correlations.
In AI search, there are computational limits and uncertainty due to the opponent's move as in twoplayers games. So it is wise to have search and execution interleaved with each search determining only the next move to be made. This paradigm is also applicable to singleagent problems.
Sophisticated methods for reasoning about uncertainty and for coping with incomplete knowledge have led to more robust diagnostic and planning systems.
An agent need not worry about uncertainty in an accessible deterministic environment. If the environment is inaccessible, it may appear to be nondeterministic. It is often best to think of an environment as deterministic or nondeterministic from the point of view of the agent. Episodic vs.
This aspect is, namely, the collection of processes that deal with decision under great uncertainty. Given the premises 1_3, the conclusion (4) follows easily.
John Casti also provides an excellent introduction to the problems inherent in predicting weather, the stock market, and other complex phenomena in his book ``Complexification'' (Harper Collins, 1994) and its predecessor ``Searching for Uncertainty: What Scientists Can Know About the Future'' ...
Knowing statistics in your everyday life will help the average business person make better decisions by allowing them to figure out risk and uncertainty when all the facts either aren't known or can't be collected.
Although artistic appropriation is often permitted under fair use doctrines, the complexity and ambiguity of these doctrines creates an atmosphere of uncertainty among cultural practitioners.
unless uncertainty exists first, there can be no information." (Campbell, p. 215216, 254) Shannon's equation aside, the ability to invent relationships, which may be necessary for the operation of memory without degenerating into concrete in a changing and uncertain world, ...
A formal framework for making logical decisions in problem areas containing risk, uncertainty and probabilities, typically employing Bayesian inference methods. Decision tree ...
Understanding Uncertainty Through Simulation with XLSim Duane Bong: Monte Carlo Simulation Brian T. Luke: Simple Monte Carlo Simulation Lecture Note of A OneDay Short Course in Principles of Monte Carlo Simulation FinPortfolio: Monte Carlo Simulation and Financial Planning ...
Using extensive interviewing of medics who were expert in this domain, the MYCIN team extracted around 450 rules for determining the cause of a disease. Using uncertainty reasoning to model these rules, MYCIN performed as well as some experts and outperformed junior doctors on the whole.
Roughly speaking, these applications are those in which there is a lot of historic data available and nobody knows exactly the structure and parameters that may model such data. In other words, big amounts of data and high uncertainty, regarding the way the data is produced.
5 for all of the output i.e. that there is complete uncertainty about which is the correct class. It turns out that E has a maximum value in this case. Thus, the more uncertain the network is, the larger the error E. This is as it should be.
They had to be acquired from extensive interviewing of experts, who in turn acquired them from textbooks, other experts, and direct experience of cases. Second, the rules had to reflect the uncertainty called certainty factors which seemed (at the time) to fit well with how doctors assessed the ...
See also: What is the meaning of System, Information, Knowledge, Artificial intelligence, Process?
