Quantum operation of affective artificial intelligence

Abstract

The review analyses the fundamental principles which Artificial Intelligence should be based on in order to imitate the realistic process of taking decisions by humans experiencing emotions. Two approaches are considered, one based on quantum theory and the other employing classical terms. Both these approaches have a number of similarities, being principally probabilistic. The analogies between quantum measurements under intrinsic noise and affective decision making are elucidated. It is shown that cognitive processes have many features that are formally similar to quantum measurements. This, however, in no way means that for the imitation of human decision making Affective Artificial Intelligence has necessarily to rely on the functioning of quantum systems. The analogies between human decision making and quantum measurements merely demonstrate formal common properties in their functioning. It is in this sense that one has to understand quantum operation of Artificial Intelligence. Appreciating the common features between quantum measurements and decision making helps for the formulation of an axiomatic approach employing only classical notions. Artificial Intelligence, following this approach, operates similarly to humans, by taking into account the utility of the considered alternatives as well as their emotional attractiveness. Affective Artificial Intelligence, whose operation takes account of the cognition-emotion duality, avoids numerous behavioural paradoxes of traditional decision making. A society of intelligent agents, interacting through the repeated multistep exchange of information, forms a network accomplishing dynamic decision making based on the evaluation of utility and affected by the emotional attractiveness of alternatives. The considered intelligent networks can characterize the operation of either a human society of affective decision makers, or the brain composed of neurons, or a typical probabilistic network of an artificial intelligence.

Discrete versus Continuous Algorithms in Dynamics of Affective Decision Making

“…The mathematically rigorous axiomatic formulation of the theory has been carried out in Refs. [9][10][11]. The theory starts with the process of making decisions by separate individuals.…”

“…The parameter β is a belief parameter characterizing the level of certainty of a decision maker in the fairness of the decision task and in the subject confidence with respect to their understanding of the overall rules and conditions of the decision problem [9][10][11]. Here, we keep in mind rational beliefs representing reasonable, objective, flexible, and constructive conclusions or inferences about reality [29,30].…”

“…The average values for positive or negative emotions of the attraction factor can be estimated by non-informative priors as ±0. 25, respectively [10,11]. The description of decision making by independent agents in the frame of probabilistic affective decision making has been studied and expounded in detail in Refs.…”

“…The description of decision making by independent agents in the frame of probabilistic affective decision making has been studied and expounded in detail in Refs. [9][10][11]. The aim of the present paper is to consider the extension of the theory from single-step affective decision making of a single agent to multistep dynamic affective decision making by a society of many decision makers.…”

“…The agents make decisions in the frame of affective decision theory that is a probabilistic theory where the agents choose alternatives taking account of both utility and emotions [9,10]. This theory can serve as a basis for creating affective artificial intelligence [11]. The society of intelligent agents forms an intelligent network.…”