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 | |
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-careOtherwise 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