Quantum Operation of Affective Artificial Intelligence

• URL: http://arxiv.org/abs/2305.08112v1
• Date: Sun, 14 May 2023 09:40:13 GMT
• Title: Quantum Operation of Affective Artificial Intelligence
• Authors: V.I. Yukalov
• Abstract summary: Two approaches are compared, one based on quantum theory and the other employing classical terms. The analogies between quantum measurements under intrinsic noise and affective decision making are elucidated. A society of intelligent agents, interacting through the repeated multistep exchange of information, forms a network accomplishing dynamic decision making.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The review analyzes 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 compared, 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. 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. 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.

Related papers

• Heuristic Reasoning in AI: Instrumental Use and Mimetic Absorption [0.2209921757303168]
  We propose a novel program of reasoning for artificial intelligence (AI) We show that AIs manifest an adaptive balancing of precision and efficiency, consistent with principles of resource-rational human cognition. Our findings reveal a nuanced picture of AI cognition, where trade-offs between resources and objectives lead to the emulation of biological systems.
  arXiv  Detail & Related papers  (2024-03-14T13:53:05Z)
• A Neuro-mimetic Realization of the Common Model of Cognition via Hebbian Learning and Free Energy Minimization [55.11642177631929]
  Large neural generative models are capable of synthesizing semantically rich passages of text or producing complex images. We discuss the COGnitive Neural GENerative system, such an architecture that casts the Common Model of Cognition.
  arXiv  Detail & Related papers  (2023-10-14T23:28:48Z)
• Neural Amortized Inference for Nested Multi-agent Reasoning [54.39127942041582]
  We propose a novel approach to bridge the gap between human-like inference capabilities and computational limitations. We evaluate our method in two challenging multi-agent interaction domains.
  arXiv  Detail & Related papers  (2023-08-21T22:40:36Z)
• Brain-Inspired Computational Intelligence via Predictive Coding [89.6335791546526]
  Predictive coding (PC) has shown promising performance in machine intelligence tasks. PC can model information processing in different brain areas, can be used in cognitive control and robotics.
  arXiv  Detail & Related papers  (2023-08-15T16:37:16Z)
• Quantum Circuit Components for Cognitive Decision-Making [0.12891210250935145]
  This paper demonstrates that some non-classical models of human decision-making can be run successfully as circuits on quantum computers. The claim is not that the human brain uses qubits and quantum circuits explicitly, but that the mathematics shared between quantum cognition and quantum computing motivates the exploration of quantum computers for cognition modeling.
  arXiv  Detail & Related papers  (2023-02-06T18:52:10Z)
• A Quantum-like Model for Predicting Human Decisions in the Entangled Social Systems [0.7734726150561088]
  We introduce an entangled Bayesian network inspired by the entanglement concept in quantum information theory. Society's effect on the dynamic evolution of the decision-making process is modeled by the entanglement measures. Results confirm that PEQBN provides more realistic predictions of human decisions under uncertainty.
  arXiv  Detail & Related papers  (2021-11-27T14:03:55Z)
• Cognitive science as a source of forward and inverse models of human decisions for robotics and control [13.502912109138249]
  We look at how cognitive science can provide forward models of human decision-making. We highlight approaches that synthesize blackbox and theory-driven modeling. We aim to provide readers with a glimpse of the range of frameworks, methodologies, and actionable insights that lie at the intersection of cognitive science and control research.
  arXiv  Detail & Related papers  (2021-09-01T00:28:28Z)
• AGENT: A Benchmark for Core Psychological Reasoning [60.35621718321559]
  Intuitive psychology is the ability to reason about hidden mental variables that drive observable actions. Despite recent interest in machine agents that reason about other agents, it is not clear if such agents learn or hold the core psychology principles that drive human reasoning. We present a benchmark consisting of procedurally generated 3D animations, AGENT, structured around four scenarios.
  arXiv  Detail & Related papers  (2021-02-24T14:58:23Z)
• Machine Common Sense [77.34726150561087]
  Machine common sense remains a broad, potentially unbounded problem in artificial intelligence (AI) This article deals with the aspects of modeling commonsense reasoning focusing on such domain as interpersonal interactions.
  arXiv  Detail & Related papers  (2020-06-15T13:59:47Z)
• Neuro-symbolic Architectures for Context Understanding [59.899606495602406]
  We propose the use of hybrid AI methodology as a framework for combining the strengths of data-driven and knowledge-driven approaches. Specifically, we inherit the concept of neuro-symbolism as a way of using knowledge-bases to guide the learning progress of deep neural networks.
  arXiv  Detail & Related papers  (2020-03-09T15:04:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.