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Inductive computations, anytime algorithms and emotions
Abstract
In this paper, we show how utilization of emotions enhances and improves two computing technologies – inductive and anytime – combining their advantages in a synthesized approach based on emotions. Inductive computations are used in many areas of computer and network technology. Inductive reasoning forms the base for scientific exploration. A mathematical model of inductive computations and reasoning is called an
inductive Turing machine, which is a natural extension of the most popular model of computing devices and computations - Turing machine. In comparison with Turing machines, inductive Turing machines represent the next step in the development of computer science providing better models for contemporary computers and computer networks.
Anytime computation enhances the traditional notion of a recursive computational procedure by allowing it to return many possible approximate answers to any given input and choosing which of them is taken as the result of computation. Anytime algorithms use well-defined quality measures to monitor the progress in problem solving and allocate computational resources effectively. The binary notion of correctness is replaced with a multivalued quality measure associated with each answer.
Similar to anytime computation, inductive computation also produces a sequence of
intermediate outputs and it is necessary to determine the result of a computation.
Sometimes the result is determined in the recursive mode when the computing system
itself informs that the result is obtained. In other cases, the user has to make a decision
that the result is obtained. In this case, it is possible to apply the anytime approach based
on quality measures. In some cases, these measures can be exact allowing unique
decision. In other cases, only fuzzy measures can estimate intermediate outputs. That is
why we suggest utilization of emotions in result determination for modeling decision-
making by people.
Key words: artificial intelligence, emotion, recursive computation, inductive computation, measure, decision-making, algorithm
inductive Turing machine, which is a natural extension of the most popular model of computing devices and computations - Turing machine. In comparison with Turing machines, inductive Turing machines represent the next step in the development of computer science providing better models for contemporary computers and computer networks.
Anytime computation enhances the traditional notion of a recursive computational procedure by allowing it to return many possible approximate answers to any given input and choosing which of them is taken as the result of computation. Anytime algorithms use well-defined quality measures to monitor the progress in problem solving and allocate computational resources effectively. The binary notion of correctness is replaced with a multivalued quality measure associated with each answer.
Similar to anytime computation, inductive computation also produces a sequence of
intermediate outputs and it is necessary to determine the result of a computation.
Sometimes the result is determined in the recursive mode when the computing system
itself informs that the result is obtained. In other cases, the user has to make a decision
that the result is obtained. In this case, it is possible to apply the anytime approach based
on quality measures. In some cases, these measures can be exact allowing unique
decision. In other cases, only fuzzy measures can estimate intermediate outputs. That is
why we suggest utilization of emotions in result determination for modeling decision-
making by people.
Key words: artificial intelligence, emotion, recursive computation, inductive computation, measure, decision-making, algorithm
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