QKSA: Quantum Knowledge Seeking Agent

Aritra Sarkar; Zaid Al-Ars; Koen Bertels

doi:10.1007/978-3-031-19907-3_37

QKSA: Quantum Knowledge Seeking Agent

Aritra Sarkar^*, Zaid Al-Ars, Koen Bertels

^*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

1 Citation (Scopus)

70 Downloads (Pure)

Abstract

In this research, we extend the universal reinforcement learning agent models of artificial general intelligence to quantum environments. The utility function of a classical exploratory stochastic Knowledge Seeking Agent, KL-KSA, is generalized to distance measures from quantum information theory on density matrices. Quantum process tomography (QPT) algorithms form a tractable subset of programs for modeling environmental dynamics. The optimal QPT policy is selected based on a mutable cost function based on algorithmic complexity as well as computational resource complexity. The entire agent design is encapsulated in a self-replicating quine which mutates the cost function based on the predictive value of the optimal policy choosing scheme. Thus, multiple agents with pareto-optimal QPT policies evolve using genetic programming, mimicking the development of physical theories each with different resource trade-offs. This formal framework, termed Quantum Knowledge Seeking Agent (QKSA), is a resource-bounded participatory observer modification to the recently proposed algorithmic information-based reconstruction of quantum mechanics. A proof-of-concept is implemented and available as open-sourced software.

Original language	English
Title of host publication	Artificial General Intelligence - 15th International Conference, AGI 2022, Proceedings
Editors	Ben Goertzel, Matt Iklé, Alexey Potapov, Denis Ponomaryov
Publisher	Springer
Pages	384-393
Number of pages	10
ISBN (Print)	9783031199066
DOIs	https://doi.org/10.1007/978-3-031-19907-3_37
Publication status	Published - 2023
Event	15th International Conference on Artificial General Intelligence, AGI 2022 - Seattle, United States Duration: 19 Aug 2022 → 22 Aug 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13539 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th International Conference on Artificial General Intelligence, AGI 2022
Country/Territory	United States
City	Seattle
Period	19/08/22 → 22/08/22

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

Algorithmic information theory
Mutating quine
Quantum computing
Reinforcement learning

Access to Document

10.1007/978-3-031-19907-3_37

978-3-031-19907-3_37Final published version, 279 KB

Cite this

Sarkar, A., Al-Ars, Z., & Bertels, K. (2023). QKSA: Quantum Knowledge Seeking Agent. In B. Goertzel, M. Iklé, A. Potapov, & D. Ponomaryov (Eds.), Artificial General Intelligence - 15th International Conference, AGI 2022, Proceedings (pp. 384-393). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13539 LNAI). Springer. https://doi.org/10.1007/978-3-031-19907-3_37

Sarkar, Aritra ; Al-Ars, Zaid ; Bertels, Koen. / QKSA : Quantum Knowledge Seeking Agent. Artificial General Intelligence - 15th International Conference, AGI 2022, Proceedings. editor / Ben Goertzel ; Matt Iklé ; Alexey Potapov ; Denis Ponomaryov. Springer, 2023. pp. 384-393 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "In this research, we extend the universal reinforcement learning agent models of artificial general intelligence to quantum environments. The utility function of a classical exploratory stochastic Knowledge Seeking Agent, KL-KSA, is generalized to distance measures from quantum information theory on density matrices. Quantum process tomography (QPT) algorithms form a tractable subset of programs for modeling environmental dynamics. The optimal QPT policy is selected based on a mutable cost function based on algorithmic complexity as well as computational resource complexity. The entire agent design is encapsulated in a self-replicating quine which mutates the cost function based on the predictive value of the optimal policy choosing scheme. Thus, multiple agents with pareto-optimal QPT policies evolve using genetic programming, mimicking the development of physical theories each with different resource trade-offs. This formal framework, termed Quantum Knowledge Seeking Agent (QKSA), is a resource-bounded participatory observer modification to the recently proposed algorithmic information-based reconstruction of quantum mechanics. A proof-of-concept is implemented and available as open-sourced software.",

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author = "Aritra Sarkar and Zaid Al-Ars and Koen Bertels",

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Sarkar, A, Al-Ars, Z & Bertels, K 2023, QKSA: Quantum Knowledge Seeking Agent. in B Goertzel, M Iklé, A Potapov & D Ponomaryov (eds), Artificial General Intelligence - 15th International Conference, AGI 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13539 LNAI, Springer, pp. 384-393, 15th International Conference on Artificial General Intelligence, AGI 2022, Seattle, United States, 19/08/22. https://doi.org/10.1007/978-3-031-19907-3_37

QKSA: Quantum Knowledge Seeking Agent. / Sarkar, Aritra; Al-Ars, Zaid; Bertels, Koen.
Artificial General Intelligence - 15th International Conference, AGI 2022, Proceedings. ed. / Ben Goertzel; Matt Iklé; Alexey Potapov; Denis Ponomaryov. Springer, 2023. p. 384-393 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13539 LNAI).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - QKSA

T2 - 15th International Conference on Artificial General Intelligence, AGI 2022

AU - Sarkar, Aritra

AU - Al-Ars, Zaid

AU - Bertels, Koen

N1 - Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

PY - 2023

Y1 - 2023

N2 - In this research, we extend the universal reinforcement learning agent models of artificial general intelligence to quantum environments. The utility function of a classical exploratory stochastic Knowledge Seeking Agent, KL-KSA, is generalized to distance measures from quantum information theory on density matrices. Quantum process tomography (QPT) algorithms form a tractable subset of programs for modeling environmental dynamics. The optimal QPT policy is selected based on a mutable cost function based on algorithmic complexity as well as computational resource complexity. The entire agent design is encapsulated in a self-replicating quine which mutates the cost function based on the predictive value of the optimal policy choosing scheme. Thus, multiple agents with pareto-optimal QPT policies evolve using genetic programming, mimicking the development of physical theories each with different resource trade-offs. This formal framework, termed Quantum Knowledge Seeking Agent (QKSA), is a resource-bounded participatory observer modification to the recently proposed algorithmic information-based reconstruction of quantum mechanics. A proof-of-concept is implemented and available as open-sourced software.

AB - In this research, we extend the universal reinforcement learning agent models of artificial general intelligence to quantum environments. The utility function of a classical exploratory stochastic Knowledge Seeking Agent, KL-KSA, is generalized to distance measures from quantum information theory on density matrices. Quantum process tomography (QPT) algorithms form a tractable subset of programs for modeling environmental dynamics. The optimal QPT policy is selected based on a mutable cost function based on algorithmic complexity as well as computational resource complexity. The entire agent design is encapsulated in a self-replicating quine which mutates the cost function based on the predictive value of the optimal policy choosing scheme. Thus, multiple agents with pareto-optimal QPT policies evolve using genetic programming, mimicking the development of physical theories each with different resource trade-offs. This formal framework, termed Quantum Knowledge Seeking Agent (QKSA), is a resource-bounded participatory observer modification to the recently proposed algorithmic information-based reconstruction of quantum mechanics. A proof-of-concept is implemented and available as open-sourced software.

KW - Algorithmic information theory

KW - Mutating quine

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DO - 10.1007/978-3-031-19907-3_37

M3 - Conference contribution

AN - SCOPUS:85148698259

SN - 9783031199066

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 384

EP - 393

BT - Artificial General Intelligence - 15th International Conference, AGI 2022, Proceedings

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A2 - Iklé, Matt

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Sarkar A, Al-Ars Z, Bertels K. QKSA: Quantum Knowledge Seeking Agent. In Goertzel B, Iklé M, Potapov A, Ponomaryov D, editors, Artificial General Intelligence - 15th International Conference, AGI 2022, Proceedings. Springer. 2023. p. 384-393. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-19907-3_37

QKSA: Quantum Knowledge Seeking Agent

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