bibtype	A - Abstract
ARLID	0640700
utime	20251103150412.1
mtime	20251031235959.9
DOI	10.5281/zenodo.15854639
title (primary) (eng)	Leveraging Quantum Logic for Data-Driven Decision-Making in Imitation Learning for Intelligent Agents
specification	page_count 1 s. media_type E
serial	title Quantum Information and Decision Making in Life Sciences: Book of Abstracts part_title Leveraging Quantum Logic for Data-Driven Decision-Making in Imitation Learning for Intelligent Agents page_num 32-32 publisher place Prague name Czech University of Life Sciences Prague year 2025 editor name1 Guy, Pelikán, Kárný, Gaj, Ružejnikov, Ruman name2 Tatiana Valentine, Martin, Miroslav, Aleksej, Jurij, Marko
keyword	Quantum Mechanics
keyword	Imitation Learning
keyword	Superposition
keyword	Entanglement
keyword	Intelligent Agents
keyword	Decision-Making
author (primary)	ARLID cav_un_auth*0355639 name1 Fakhimi Derakhshan name2 Siavash institution UTIA-B full_dept (cz) Adaptivní systémy full_dept (eng) Department of Adaptive Systems department (cz) AS department (eng) AS country IR share 100 fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source	url https://zenodo.org/records/16900460
cas_special	project project_id CA21169 agency EC ARLID cav_un_auth*0452289 abstract (eng) In this study on imitation learning, the quantum logic of events has been utilized to reformulate data-driven decision-making processes for intelligent agents. By leveraging concepts such as superposition and entanglement, this approach enhances how agents learn from expert demonstrations. Key aspects of this method include the ability to evaluate multiple strategies simultaneously through superposition and to recognize interconnected decision-making patterns through entanglement. Additionally, this framework addresses uncertainty in data, considering probabilistic modelling as a vital component that tackles decision-making challenges in systems characterized by stochastic representations.\nWhile a completely new approach is not being proposed at this stage, this integration of quantum logic offers a solid foundation for potential future innovations in this field. This reformulation may facilitate a deeper exploration of strategies that could lead to valuable applications across various sectors, including robotics, autonomous systems, healthcare, and finance. As work continues, it is hoped that these foundational changes will contribute to advancements in the capabilities of intelligent agents operating under uncertainty and complexity. abstract (eng) In this study on imitation learning, the quantum logic of events has been utilized to reformulate data-driven decision-making processes for intelligent agents. By leveraging concepts such as superposition and entanglement, this approach enhances how agents learn from expert demonstrations. Key aspects of this method include the ability to evaluate multiple strategies simultaneously through superposition and to recognize interconnected decision-making patterns through entanglement. Additionally, this framework addresses uncertainty in data, considering probabilistic modelling as a vital component that tackles decision-making challenges in systems characterized by stochastic representations.\nWhile a completely new approach is not being proposed at this stage, this integration of quantum logic offers a solid foundation for potential future innovations in this field. This reformulation may facilitate a deeper exploration of strategies that could lead to valuable applications across various sectors, including robotics, autonomous systems, healthcare, and finance. As work continues, it is hoped that these foundational changes will contribute to advancements in the capabilities of intelligent agents operating under uncertainty and complexity. action ARLID cav_un_auth*0491465 name DYNALIFE 2025 : Conference on QUANTUM INFORMATION AND DECISION MAKING IN LIFE SCIENCES dates 20250428 mrcbC20-s 20250429 place Prague country CZ RIV BB FORD0 10000 FORD1 10100 FORD2 10101 reportyear 2026 num_of_auth 1 presentation_type PO permalink https://hdl.handle.net/11104/0371110 cooperation ARLID cav_un_auth*0322033 name Česká zemědělská univerzita v Praze, Provozně ekonomická fakulta institution PEF ČZU country CZ confidential S arlyear 2025 mrcbU02 A2 mrcbU14 SCOPUS mrcbU24 PUBMED mrcbU34 WOS mrcbU63 Quantum Information and Decision Making in Life Sciences: Book of Abstracts Quantum Information and Decision Making in Life Sciences: Book of Abstracts Leveraging Quantum Logic for Data-Driven Decision-Making in Imitation Learning for Intelligent Agents Czech University of Life Sciences Prague 2025 Prague 32 32 mrcbU67 Guy, Pelikán, Kárný, Gaj, Ružejnikov, Ruman Tatiana Valentine, Martin, Miroslav, Aleksej, Jurij, Marko 340