Inference is the act or process of deriving a logical consequence conclusion from premises.
Inference is studied within several different fields.
- Human inference (i.e. how humans draw conclusions) is traditionally studied within the field of cognitive psychology.
- Logic studies the laws of valid inference.
- Statisticians have developed formal rules for inference (statistical inference) from quantitative data.
- Artificial intelligence researchers develop automated inference systems.
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The accuracy of inductive and deductive inferences
Inductive
The process by which a conclusion is inferred from multiple observations is called inductive reasoning. The conclusion may be correct or incorrect, or correct, or correct to within a certain degree of accuracy, or correct in certain situations. Conclusions inferred from multiple observations may be tested by additional observations.
Deductive
The process by which a conclusion is logically inferred from certain premises is called deductive reasoning. Mathematics makes use of deductive inference. Certain definitions and axioms are taken as a starting point, and from these certain theorems are deduced using pure reasoning. The idea for a theorem may have many sources: analogy, pattern recognition, and experiment are examples of where the inspiration for a theorem comes from. However, a conjecture is not granted the status of theorem until it has a deductive proof. This method of inference is even more accurate than the scientific method. Mistakes are usually quickly detected by other mathematicians and corrected. The proofs of Euclid, for example, have mistakes in them that have been caught and corrected, but the theorems of Euclid, all of them without exception, have stood the test of time for more than two thousand years.[1]
Valid inferences
Inferences are either valid or invalid, but not both. Philosophical logic has attempted to define the rules of proper inference, i.e. the formal rules that, when correctly applied to true premises, lead to true conclusions. Aristotle has given one of the most famous statements of those rules in his Organon. Modern mathematical logic, beginning in the 19th century, has built numerous formal systems.
Examples of deductive inference
Greek philosophers defined a number of syllogisms, correct three-part inferences, that can be used as building blocks for more complex reasoning. We'll begin with the most famous of them all:
All men are mortal Socrates is a man ------------------ Therefore Socrates is mortal.
The reader can check that the premises and conclusion are true, but Logic is concerned with inference: does the truth of the conclusion follow from that of the premises?
The validity of an inference depends on the form of the inference. That is, the word "valid" does not refer to the truth of the premises or the conclusion, but rather to the form of the inference. An inference can be valid even if the parts are false, and can be invalid even if the parts are true. But a valid form with true premises will always have a true conclusion.
For example, consider the form of the above argument:
All A are B C is A ---------- Therefore C is B
The form remains valid even if all three parts are false:
All apples are blue. A banana is an apple. ---- Therefore a banana is blue.
For the conclusion to be necessarily true, the premises need to be true.
Now we turn to an invalid form.
All A are B. C is a B. ---- Therefore C is an A.
To show that this form is invalid, we demonstrate how it can lead from true premises to a false conclusion.
All apples are fruit. (true) Bananas are fruit. (true) ---- Therefore bananas are apples. (false)
A valid argument with false premises may lead to a false conclusion:
All fat people are Greek John Lennon was fat ------------------- Therefore John Lennon was Greek
where a valid argument is used to derive a false conclusion from false premises. The inference is valid because it follows the form of a correct inference.
A valid argument can also be used to derive a true conclusion from false premises:
All fat people are musicians John Lennon was fat ------------------- Therefore John Lennon was a musician
In this case we have two false premises that imply a true conclusion.
Incorrect inference
An incorrect inference is known as a fallacy. Philosophers who study informal logic have compiled large lists of them, and cognitive psychologists have documented many biases in human reasoning that favor incorrect reasoning.
Automatic logical inference
AI systems first provided automated logical inference and these were once extremely popular research topics, leading to industrial applications under the form of expert systems and later business rule engines.
An inference system's job is to extend a knowledge base automatically. The knowledge base (KB) is a set of propositions that represent what the system knows about the world. Several techniques can be used by that system to extend KB by means of valid inferences. An additional requirement is that the conclusions the system arrives at are relevant to its task.
An example: inference using Prolog
Prolog (for "Programming in Logic") is a programming language based on a subset of predicate calculus. Its main job is to check whether a certain proposition can be inferred from a KB (knowledge base) using an algorithm called backward chaining.
Let us return to our Socrates syllogism. We enter into our Knowledge Base the following piece of code:
mortal(X) :- man(X). man(socrates).
( Here :- can be read as if. Generally, if P Q (if P then Q) then in Prolog we would code Q:-P (Q if P).) This states that all men are mortal and that Socrates is a man. Now we can ask the Prolog system about Socrates:
?- mortal(socrates).
(where ?- signifies a query: Can mortal(socrates). be deduced from the KB using the rules) gives the answer "Yes".
On the other hand, asking the Prolog system the following:
?- mortal(plato).
gives the answer "No".
This is because Prolog does not know anything about Plato, and hence defaults to any property about Plato being false (the so-called closed world assumption). Finally ?- mortal(X) (Is anything mortal) would result in "Yes" (and in some implemenations: "Yes": X=socrates) Prolog can be used for vastly more complicated inference tasks. See the corresponding article for further examples.
Automatic inference and the semantic web
Recently automatic reasoners found in semantic web a new field of application. Being based upon first-order logic, knowledge expressed using one variant of OWL can be logically processed, i.e., inferences can be made upon it.
Bayesian statistics and probability logic
Philosophers and scientists who follow the Bayesian framework for inference use the mathematical rules of probability to find this best explanation. The Bayesian view has a number of desirable features—one of them is that it embeds deductive (certain) logic as a subset (this prompts some writers to call Bayesian probability "probability logic", following E. T. Jaynes).
Bayesianists identify probabilities with degrees of beliefs, with certainly true propositions having probability 1, and certainly false propositions having probability 0. To say that "it's going to rain tomorrow" has a 0.9 probability is to say that you consider the possibility of rain tomorrow as extremely likely.
Through the rules of probability, the probability of a conclusion and of alternatives can be calculated. The best explanation is most often identified with the most probable (see Bayesian decision theory). A central rule of Bayesian inference is Bayes' theorem, which gave its name to the field.
See Bayesian inference for examples.
Nonmonotonic logic
Source: Article of André Fuhrmann about "Nonmonotonic Logic"
A relation of inference is monotonic if the addition of premisses does not undermine previously reached conclusions; otherwise the relation is nonmonotonic. Deductive inference, at least according to the canons of classical logic, is monotonic: if a conclusion is reached on the basis of a certain set of premisses, then that conclusion still holds if more premisses are added.
By contrast, everyday reasoning is mostly nonmonotonic because it involves risk: we jump to conclusions from deductively insufficient premises. We know when it is worth or even necessary (e.g. in medical diagnosis) to take the risk. Yet we are also aware that such inference is defeasible—that new information may undermine old conclusions. Various kinds of defeasible but remarkably successful inference have traditionally captured the attention of philosophers (theories of induction, Peirce’s theory of abduction, inference to the best explanation, etc.). More recently logicians have begun to approach the phenomenon from a formal point of view. The result is a large body of theories at the interface of philosophy, logic and artificial intelligence.
See also
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References
- ^ Euclid, The Elements, Dover, 1956, ISBN 486600882.
| Look up inference or infer in Wiktionary, the free dictionary. |
| Logic portal |
- Hacking, Ian (2000). An Introduction to Probability and Inductive Logic. Cambridge University Press. ISBN 0521775019.
- Jaynes, Edwin Thompson (2003). Probability Theory: The Logic of Science. Cambridge University Press. ISBN 0-521-59271-2. http://titles.cambridge.org/catalogue.asp?isbn=0521592712.
- McKay, David J.C. (2003). Information Theory, Inference, and Learning Algorithms. Cambridge University Press. ISBN 0-521-64298-1. http://www.inference.phy.cam.ac.uk/mackay/itila/book.html.
- Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, NJ: Prentice Hall, ISBN 0-13-790395-2, http://aima.cs.berkeley.edu/
- Tijms, Henk (2004). Understanding Probability. Cambridge University Press. ISBN 0521701724.
- Fuhrmann, André. Nonmonotonic Logic. http://www.uni-konstanz.de/FuF/Philo/Philosophie/Fuhrmann/papers/nomoLog.pdf.
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Missourinet.com
Anyone can access campaign contribution information and make their own inference on certain judges. "People of Missouri don't want their judges bought and ...
and more »
Jonah Schupbach
2009-05-20 11:14:50
Loading... Philosophy, Science, and Method. : Close preview. Loading... Archives. May 2009 April 2009. Subscribe. Posts Comments. Choice & . Inference. 2009 All Rights Reserved. Free WordPress Themes | Fresh WordPress Themes.
Q. 1. The cloud has a flatter bottom than top. 2. The cloud appears to be growing larger. 3. The cloud base height appears to be constant. 4. The air in the cloud is rising. Please just write down the number! Thanks!
Asked by bob! - Tue Jul 1 20:44:10 2008 - - 3 Answers - 0 Comments
A. 2. An inference is when you use already known information to gather new information. You already knew what size it WAS, and you used that to judge that it is getting bigger.
Answered by Roese - Tue Jul 1 20:50:45 2008
