Science Fair Project Encyclopedia
To infer is to draw a conclusion based on what one already knows and on that alone. Suppose you see rain on your window - you can infer from that, quite trivially, that the sky is grey. Looking out the window would have yielded the same fact, but through a process of perception, not inference (note however that perception itself can be viewed as an inferential process).
Inference is studied within several different fields. Human inference (ie how humans draw conclusions) is traditionally studied within the field of cognitive psychology. The rules and process of inference are some of philosophy's oldest subject matters. Logic studies the laws of valid inference. Statisticians have developed formal rules for inference from quantitative data. Artificial intelligence researchers develop automated inference systems.
Inferences can be correct or incorrect. Philosophical logic has attempted to define the rules of proper inference, ie the rules that when applied to true premisses 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 that embody Aristotelian logic (or variants thereof).
An example: the classic syllogism
All men are mortal Socrates is a man ------------------ Therefore Socrates is mortal.
The reader can check that the premisses and conclusion are true. The inference is valid - it strongly appeals to our common sense (whether that suffices to make a inference valid is an open question).
In predicate logic (a simple but useful formalisation of Aristotelician logic), this syllogism can be stated as follows:
∀ X, man(X) → mortal(X) man(Socrates) ------------------------------- ∴mortal(Socrates)
Or in its general form:
∀ X, A(X) → B(X) A(x) ------------------------ ∴B(x)
∀, the universal quantifier, is pronounced "for all". It allows us to state a general property. Here it is used to say that "if any X is a man, X is also mortal". Socrates is a man, and the conclusion follows.
Compare to the following inference:
All dogs are mortal Socrates is mortal ------------------- Therefore Socrates is a dog
∀ X, dog(X) → mortal(X) mortal(Socrates) ------------------ ∴dog(Socrates)
This inference is obviously incorrect, because it led us from true premisses to the wrong conclusion. Note that while an inference that leads from true premisses to a wrong conclusion is necessarily incorrect, true premisses and a true conclusion do not necessarily imply that the inference was correct.
Although in this particular example the error in reasoning is particularly easy to spot, it is not always so, especially if the conclusion appears true. An incorrect inference is know 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 favour incorrect reasoning.
Automatic logical inference
Although now somewhat past their heyday, AI systems for automated logical inference once were extremely popular research topics, and have know industrial applications under the form of expert systems.
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 the conclusions the system arrives at are relevant to its task.
An example: inference using Prolog
Prolog (Programming in Logic) is a programming language based on a subset of predicate calculus. Its main job is to check whether a certain proposition is can be inferred from the KB using an algorithm called backward chaining.
mortal(X) :- man(X). man(socrates).
This states that all men are mortal and that Socrates is a man. Now we can ask Prolog about Socrates.
On the other hand :
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 hypothesis ). Prolog can be used for vastly more complicated inference tasks. See the corresponding article for further examples.
Inference and uncertainty
Traditional logic is only concerned with certainty - one progresses from certain premisses to certain conclusions. There are several motivations for extending logic to deal with uncertain propositions and weaker modes of reasoning.
- Philosophical motivations
- A large part of our everyday reasoning does not follow the strict rules of logic, but is nevertheless effective in many cases
- Science itself is not deductive, but largely inductive, and its process cannot be captured by standard logic (see problem of induction).
- Technical motivations
- Statisticians and scientists wish to be able to infer parameters or test hypothesis on statistical data in a rigorous, quantified way.
- Artificial intelligence systems need to reason efficiently about uncertain quantities.
Common sense and uncertain reasoning
The reason most examples of applying deductive logic, such as the one above, seem artificial is because they are rarely encountered outside fields such as mathematics. Most of our everyday reasoning is of a less "pure" nature.
To take an example : late at night, you're woken up by sounds of creaks in the ceiling. You infer from that that your neighbour upstairs (we assume you live in a flat) is having another bout of insomnia and pacing in his room, sleepless. You then proceed to go back to sleep.
Although that reasoning seems sound, it does not fit in the logical framework descrived above. First, it is based on uncertain facts : what you heard are creaks, not directly footsteps. But even if those facts were certain, the inference is of an inductive nature : maybe you've often heard your neigbour at night, and the best explanation you've found is that he or she is an insomniac. Hence tonight's footsteps.
But it is easy to see that this line of reasoning does not necessarily lead to true conclusions : maybe your neighbour had a very early plane to catch, which would explain the footsteps just as well. Uncertain reasoning can only find the best explanation among many alternatives.
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 have probability 1, and certainly false propositions have 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.
Frequentist statistical inference(to be written)
Fuzzy logic(to be written)
- Fuzzy Logic
- Bayesian inference
- Expert system
- Inference engine
- Inference procedure
- Logical assertion
- Rules of inference
- List of rules of inference
- Ian Hacking. An Introduction to Probability and Inductive Logic. Cambridge University Press, (2000).
- Edwin Thompson Jaynes. Probability Theory: The Logic of Science. Cambridge University Press, (2003). ISBN 0521592712.
- David J.C. McKay. Information Theory, Inference, and Learning Algorithms. Cambridge University Press, (2003).
- Stuart Russell, Peter Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, (2002).
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