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QKSA: Quantum Knowledge Seeking Agent

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Artificial General Intelligence (AGI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13539))

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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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Authors and Affiliations

  1. Department of Quantum and Computer Engineering, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands

    Aritra Sarkar & Zaid Al-Ars

  2. QBee.eu, Leuven, Belgium

    Aritra Sarkar & Koen Bertels

Authors
  1. Aritra Sarkar
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  2. Zaid Al-Ars
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  3. Koen Bertels
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Correspondence to Aritra Sarkar.

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Editors and Affiliations

  1. SingularityNET Foundation, Amsterdam, The Netherlands

    Ben Goertzel

  2. SingularityNET Foundation, Amsterdam, The Netherlands

    Matt Iklé

  3. SingularityNET Foundation, Amsterdam, The Netherlands

    Alexey Potapov

  4. Russian Academy of Sciences, Novosibirsk, Russia

    Denis Ponomaryov

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Sarkar, A., Al-Ars, Z., Bertels, K. (2023). QKSA: Quantum Knowledge Seeking Agent. In: Goertzel, B., Iklé, M., Potapov, A., Ponomaryov, D. (eds) Artificial General Intelligence. AGI 2022. Lecture Notes in Computer Science(), vol 13539. Springer, Cham. https://doi.org/10.1007/978-3-031-19907-3_37

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