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Paper Citation Record · LEDGER

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems

As of 10 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2607.15459.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.15459 v2

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T23:26:00.426163Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

48 of 48 outbound references displayed

  • verified exact3
  • verified fuzzy0
  • unresolved45
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 673cee85-80f7-4a88-b352-6c1e9bfedeed · outbound

This paper cites Courville, and Marc G.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems Courville, and Marc G

Reference 1

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source=arxiv_source observed=2026-08-01T23:25:54.982174Z digest=sha256:ad52c3d0f40a6221330e0a9860561fb4a7cd952b1a24ae76f97441330b7239f1

Observation a7b05676-bcbe-4c98-9bb5-6dbb77e7cf37 · outbound

This paper cites Verifiable reinforcement learning via policy extraction.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems Verifiable reinforcement learning via policy extraction

Reference 2

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source=arxiv_source observed=2026-08-01T23:25:55.106059Z digest=sha256:ea8ce77c052f30f3e88c1526671b41fff4958b8eb47b5b026858152fa2209519

Observation 2707512f-c7ef-46b5-8dfc-9e406906bf92 · outbound

This paper cites an unresolved cited work.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems Unresolved cited work

Reference 3

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source=arxiv_source observed=2026-08-01T23:25:55.245270Z digest=sha256:96f22d29bba84942cd70eab3c44d915c146fa01ed69b15b433d8da3389fcd543

Observation 765d49d4-965f-4599-bb91-ea207323c05f · outbound

This paper cites an unresolved cited work.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems Unresolved cited work

Reference 4

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source=arxiv_source observed=2026-08-01T23:25:55.353921Z digest=sha256:46dfd959264c76f7e246b89798a431df76cd424156eb171ca14192e8a82443b6

Observation b47f40c2-c64f-43e2-ba65-233b89229d60 · outbound

This paper cites Synthesising Reinforcement Learning Policies through Set-Valued Inductive Rule Learning.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems Synthesising Reinforcement Learning Policies through Set-Valued Inductive Rule Learning

Reference 5

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source=arxiv_source observed=2026-08-01T23:25:55.420922Z digest=sha256:ecd543bba03425842375aa8e8ee7666e921a01a9c33a38d2d9cf807476de9fdf

Observation 385b5f86-d786-44f6-9cf6-3e387c0d8fc0 · outbound

This paper cites Interpretable and explainable logical policies via neurally guided symbolic abstraction.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems Interpretable and explainable logical policies via neurally guided symbolic abstraction

Reference 6

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source=arxiv_source observed=2026-08-01T23:25:55.528673Z digest=sha256:e2c8049a57cbc52b8d22dc58c25d5a33dc65f7489b942b65376cf9b9deeb9dda

Observation 5f7eb31e-747e-46af-ab63-5e6df266063d · outbound

This paper cites Interpretable concept bottlenecks to align reinforcement learning agents.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems Interpretable concept bottlenecks to align reinforcement learning agents

Reference 7

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source=arxiv_source observed=2026-08-01T23:25:55.614617Z digest=sha256:72835f49326618b0545326d2a31aa588f592387312e32ab3b655d38a04bbd159

Observation 82596e3a-40c9-40d9-bb8a-04ef28024737 · outbound

This paper cites Towards A Rigorous Science of Interpretable Machine Learning.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems Towards A Rigorous Science of Interpretable Machine Learning

Reference 8

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source=arxiv_source observed=2026-08-01T23:25:55.700229Z digest=sha256:41928cbb7fb6430f7c0b720d41dcf7ab7b10d194c49bc702506298d1bc8d80ab

Observation 05a5cfea-37b4-4bb7-bf0f-5928d70af529 · outbound

This paper cites Garrido-Merchán and Cristina Puente.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems Garrido-Merchán and Cristina Puente

Reference 10

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source=arxiv_source observed=2026-08-01T23:25:55.973042Z digest=sha256:513b7eb50da9616e79dfbbda51558186700a0f28a0d8e7130763a40fd606a253

Observation d232cd2e-986a-4f16-97f0-2587814730b4 · outbound

This paper cites Neural logic reinforcement learning.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems Neural logic reinforcement learning

Reference 11

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source=arxiv_source observed=2026-08-01T23:25:56.132507Z digest=sha256:c43b8b8ea93000291e8e4ada1c7ba90483ee477c438d678c4818b734c1f8d96f

Observation d4072afc-0920-4780-9147-ad9493566de3 · outbound

This paper cites Kakade and John Langford.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems Kakade and John Langford

Reference 12

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source=arxiv_source observed=2026-08-01T23:25:56.227910Z digest=sha256:23eab010284d1af75cfd166ff0695c97d9a92c90b319984cfa9b1adef0323edc

Observation e8e864a3-c648-421f-bc60-c0a6106f6ec5 · outbound

This paper cites Interpretable and Editable Programmatic Tree Policies for Reinforcement Learning.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems Interpretable and Editable Programmatic Tree Policies for Reinforcement Learning

Reference 13

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source=arxiv_source observed=2026-08-01T23:25:56.343456Z digest=sha256:c304397df1f6d7ed61baf7b5840dbee4be1b38c680b25252b36f10c6b805bcb8

Observation 2c949210-8412-4aa4-8402-e3bd0dabe98a · outbound

This paper cites Learning finite state representations of recurrent policy networks.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems Learning finite state representations of recurrent policy networks

Reference 14

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source=arxiv_source observed=2026-08-01T23:25:56.442238Z digest=sha256:c763eb04fda2d0a89e88bec24a47cedcadc5c7b5d34e6dd837e8b3c502ddc2e5

Observation 4e66db30-8122-4a47-b3c1-7f90ae472ff5 · outbound

This paper cites Petersen, Sookyung Kim, Cl \' a udio P.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems Petersen, Sookyung Kim, Cl \' a udio P

Reference 15

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Observation ad88d6ec-33b0-417c-a8ea-7897a2dc695c · outbound

This paper cites Toward interpretable deep reinforcement learning with linear model U - T rees.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems Toward interpretable deep reinforcement learning with linear model U - T rees

Reference 16

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source=arxiv_source observed=2026-08-01T23:25:56.679106Z digest=sha256:d8bcbe0972cf57fe0fcc934df6817b1bd5c64c39e9805ba9ed49c55844e87783

Observation 49117ef8-d842-49ce-9b88-501c09c8d641 · outbound

This paper cites Gordon, and Drew Bagnell.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems Gordon, and Drew Bagnell

Reference 19

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source=arxiv_source observed=2026-08-01T23:25:57.076450Z digest=sha256:37a17d7a945a19b5e505310740ecc7ad2ee2c796ef1d69d647ed318441e5f795

Observation e5fdd9d6-cb2f-4ed7-a79e-0be6c70361e5 · outbound

This paper cites Proximal Policy Optimization Algorithms.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems Proximal Policy Optimization Algorithms

Reference 20

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source=arxiv_source observed=2026-08-01T23:25:57.175985Z digest=sha256:efd62e4d4260b5a9b7f840a7296b4e0e9a9a84aaf8749dba972a6f13adb7cb9c

Observation 073e7dd7-579a-4b48-a778-21fb228342e4 · outbound

This paper cites EXPIL: Explanatory Predicate Invention for Learning in Games.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems EXPIL: Explanatory Predicate Invention for Learning in Games

Reference 21

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source=arxiv_source observed=2026-08-01T23:25:57.278460Z digest=sha256:b2003c0333e293bb457793af46a6495601b83dd44e4f7f934b6fc92e00634350

Observation 1fa479bb-5f13-4de0-86ff-caa12b054a41 · outbound

This paper cites B lend RL : A framework for merging symbolic and neural policy learning.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems B lend RL : A framework for merging symbolic and neural policy learning

Reference 22

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source=arxiv_source observed=2026-08-01T23:25:57.346561Z digest=sha256:9f065636e05c6dbd4077d06b4e036717280481d1db3a20e8ea0bac03668c05f1

Observation b4785357-4054-4f39-b40a-7edf56e9a0a2 · outbound

This paper cites Gymnasium: A Standard Interface for Reinforcement Learning Environments.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems Gymnasium: A Standard Interface for Reinforcement Learning Environments

Reference 23

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source=arxiv_source observed=2026-08-01T23:25:57.400121Z digest=sha256:95e7b1b54b62d8a43e8e4191c2570631c896c375d0994d79f3d6167e719d4a5e

Observation 2e62338d-4c5e-4122-b828-dbf9bc99bf19 · outbound

This paper cites Programmatically interpretable reinforcement learning.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems Programmatically interpretable reinforcement learning

Reference 24

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source=arxiv_source observed=2026-08-01T23:25:57.448602Z digest=sha256:72b23f07317fb8fd85302b3a5bf6d6300faa79f1e75be8495c8ff93728bff8c2

Observation 03412034-877d-4252-83bd-e25057355f8c · outbound

This paper cites Imitation-projected programmatic reinforcement learning.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems Imitation-projected programmatic reinforcement learning

Reference 25

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source=arxiv_source observed=2026-08-01T23:25:57.554458Z digest=sha256:c0752d2f6275651e968327e98c6ddea372a7ff5a10efc23b9f5cdec5b58aa314

Observation 06a8322c-dab3-4e91-a0e3-982ec7f80bd3 · outbound

This paper cites Kakade and John Langford , editor =.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems Kakade and John Langford , editor =

Reference 27

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source=arxiv_source observed=2026-08-01T23:25:57.773262Z digest=sha256:f3e1935089a0be54d9256a491ae64f62c5fdb742363bd86cb0959156b1a82636

Observation 383bf49f-201c-4b57-8057-1dd60b1974b1 · outbound

This paper cites Verifiable Reinforcement Learning via Policy Extraction , booktitle =.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems Verifiable Reinforcement Learning via Policy Extraction , booktitle =

Reference 28

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source=arxiv_source observed=2026-08-01T23:25:57.866516Z digest=sha256:7444252852a45406b3cdfd1e8800765d555a1becc15677f7ebb224ecb92b0547

Observation a396459d-8081-49c3-87f9-956e830ecebb · outbound

This paper cites Programmatically Interpretable Reinforcement Learning , booktitle =.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems Programmatically Interpretable Reinforcement Learning , booktitle =

Reference 29

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source=arxiv_source observed=2026-08-01T23:25:57.977354Z digest=sha256:a8f1525e3a9300da2f659baf44be6ad2c8bdb7a74af891bacee18cc6a734049f

Observation 26049bc2-80cf-4a72-a1a5-863ed9f95268 · outbound

This paper cites Imitation-Projected Programmatic Reinforcement Learning , booktitle =.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems Imitation-Projected Programmatic Reinforcement Learning , booktitle =

Reference 30

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source=arxiv_source observed=2026-08-01T23:25:58.093001Z digest=sha256:4731b460f1737b797565598d51431e6f4b651b3e2d347dd2ed95486ddd373966

Observation 60073971-b706-47eb-ae24-dfaaa1cfce64 · outbound

This paper cites Neural Logic Reinforcement Learning , booktitle =.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems Neural Logic Reinforcement Learning , booktitle =

Reference 31

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source=arxiv_source observed=2026-08-01T23:25:58.202466Z digest=sha256:4c355fa7ceda4268b8da232c15bae04edd89c4a3a964fb4da861375d02108d4e

Observation 30b036a1-7b60-41ea-af5a-9cb578e0f3ee · outbound

This paper cites Interpretable and Explainable Logical Policies via Neurally Guided Symbolic Abstraction , booktitle =.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems Interpretable and Explainable Logical Policies via Neurally Guided Symbolic Abstraction , booktitle =

Reference 32

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source=arxiv_source observed=2026-08-01T23:25:58.312244Z digest=sha256:d44e6807d7a1d61f4c17bbfb80eebbe2fbeeaf459cdf701812b37a093518248b

Observation 30263fdd-78a2-4050-806d-7cc19c8be0c8 · outbound

This paper cites The Thirteenth International Conference on Learning Representations,.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems The Thirteenth International Conference on Learning Representations,

Reference 33

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source=arxiv_source observed=2026-08-01T23:25:58.466159Z digest=sha256:6cefe69eb038c396a116bde39b3e3799a69c5ab30e0e9479088c40e74187f855

Observation 4e2a84e7-3495-445e-99f2-4ca2ab645dfa · outbound

This paper cites Interpretable Concept Bottlenecks to Align Reinforcement Learning Agents , booktitle =.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems Interpretable Concept Bottlenecks to Align Reinforcement Learning Agents , booktitle =

Reference 34

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source=arxiv_source observed=2026-08-01T23:25:58.612636Z digest=sha256:35cab321c8cf773fc85474d20feab8beb8094d6c7637007c025b0e2b3130f7d0

Observation f0a0faef-5e95-4c2a-a53f-73be27f44917 · outbound

This paper cites Petersen and Sookyung Kim and Cl.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems Petersen and Sookyung Kim and Cl

Reference 35

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source=arxiv_source observed=2026-08-01T23:25:58.736051Z digest=sha256:cde6e51ef560abf00be96975a644cffde0cc5cf36eb00d2933a646608a9319a9

Observation a487b52a-1981-46b8-87fb-c6041d315f29 · outbound

This paper cites 2024 , url =.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems 2024 , url =

Reference 36

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source=arxiv_source observed=2026-08-01T23:25:58.871100Z digest=sha256:703d81a1c0440e3fa6e3b30907051e01346e583a59eee776264d25d2dffdc62b

Observation 92fa4c3a-5121-48fd-b1c1-150f659a341b · outbound

This paper cites Courville and Marc G.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems Courville and Marc G

Reference 37

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source=arxiv_source observed=2026-08-01T23:25:59.021678Z digest=sha256:b032f7f5c67311e3f9baafa5c0bc0805921e2829cb897c755b690c292285225e

Observation 10e9eb86-925f-4427-864b-516c423e32c8 · outbound

This paper cites Ross Quinlan , title =.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems Ross Quinlan , title =

Reference 38

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verified exact
doi, observed 2026-08-01T23:28:29.335729Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-01T23:25:59.146726Z digest=sha256:24e59b399d03a128700dbd027b1e4f5830fa2298aaf54505db4d56e93891db07

Observation e569c93a-c744-4e75-89c1-dcca5f3993bf · outbound

This paper cites an unresolved cited work.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems Unresolved cited work

Reference 39

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source=arxiv_source observed=2026-08-01T23:25:59.242904Z digest=sha256:7b5e4f0f2a23a8bfe80cd1626f2911875cf3a560cb402008977035c861ef2150

Observation 71785031-4105-4c7a-8ccf-4b4982a39658 · outbound

This paper cites A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning , booktitle =.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning , booktitle =

Reference 40

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source=arxiv_source observed=2026-08-01T23:25:59.336648Z digest=sha256:b6e04ad0686f9b2c5187f5c2037afc8a9feadd0426443b33f01e3bf6047ad3c9

Observation a65ce775-2b2f-4f8b-a0cb-a054e0d7cdb6 · outbound

This paper cites 7th International Conference on Learning Representations,.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems 7th International Conference on Learning Representations,

Reference 41

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source=arxiv_source observed=2026-08-01T23:25:59.500851Z digest=sha256:ba0a562474e145f5f38117da19831f71a6b8469742a50feaff665412951eec3f

Observation 7ef71411-43df-436e-a273-8dc8eabdfc9a · outbound

This paper cites Theory Pract.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems Theory Pract

Reference 42

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Unavailable: canonical work link unavailable.

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Observation 2c60be33-99dc-4d24-a92d-eb7d38c57c3e · outbound

This paper cites Toward Interpretable Deep Reinforcement Learning with Linear Model.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems Toward Interpretable Deep Reinforcement Learning with Linear Model

Reference 43

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verified exact
doi, observed 2026-08-01T23:28:28.724163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-01T23:25:59.717096Z digest=sha256:f22f3dfe27cb093b71cb00473e086d5ae315b4d93fbe1064f4e9c30a5681634c

Observation cb821429-8fb6-40bf-8a13-4724f3304aa3 · outbound

This paper cites an unresolved cited work.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems Unresolved cited work

Reference 44

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unresolved
no resolver link, observed 2026-08-01T23:25:59.796264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T23:25:59.796264Z digest=sha256:3633aa092aac56f7e2e8ad8fff7f28a22fb833588b213b57f7063fc91e262a65

Observation 5fe88b83-3c0b-4e53-838f-894facf9ba18 · outbound

This paper cites an unresolved cited work.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems Unresolved cited work

Reference 45

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unresolved
no resolver link, observed 2026-08-01T23:25:59.861800Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T23:25:59.861800Z digest=sha256:88e683f6e1a5cff4173e518d6476e3c1957c06995e66ae99f413893d8f7972ed

Observation a8796f42-b7f1-4b49-a341-97c538fc5af1 · outbound

This paper cites 2017 , eprint =.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems 2017 , eprint =

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-01T23:25:59.922798Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T23:25:59.922798Z digest=sha256:2d394c5f0b03aa7be8c641adce780212a659428f5c9876f20ae2640061df86d4

Observation c66bcd36-7546-44ec-92c3-5a1144398f71 · outbound

This paper cites Jonker and Ann Nowé , title =.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems Jonker and Ann Nowé , title =

Reference 47

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unresolved
no resolver link, observed 2026-08-01T23:25:59.991915Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T23:25:59.991915Z digest=sha256:9f23e9259998c1f3d09883f2f4083d5f42987b47b936f40102e8426e4e108138

Observation 8617782b-4dae-44c6-9d5e-c2cfa4bd624c · outbound

This paper cites 2024 , eprint =.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems 2024 , eprint =

Reference 48

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unresolved
no resolver link, observed 2026-08-01T23:26:00.082561Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T23:26:00.082561Z digest=sha256:9dfe17a5dbade865b2b1694de81371e3561b6f7660ba576125c392beecc5fa5e

Observation 5030e046-d50a-4226-b4fd-50afcf72c8b7 · outbound

This paper cites 2024 , eprint =.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems 2024 , eprint =

Reference 49

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unresolved
no resolver link, observed 2026-08-01T23:26:00.174154Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T23:26:00.174154Z digest=sha256:9e973a0a273d14d9bdf371b0f6dc9b656d3b99682ae6e9ffa837c0c85c4bfe5b

Observation ecd95ef5-6dda-443e-84fb-78b1801785df · outbound

This paper cites Garrido-Merchán and Cristina Puente , title =.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems Garrido-Merchán and Cristina Puente , title =

Reference 50

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unresolved
no resolver link, observed 2026-08-01T23:26:00.256065Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T23:26:00.256065Z digest=sha256:f0973f361b115b0a09bae22f2fa61ff00ec9bd4e757f524e9c8954e9d2880d7b

Observation e16022cf-4706-497d-bcf3-42dd60b54ffb · outbound

This paper cites an unresolved cited work.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems Unresolved cited work

Reference 51

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unresolved
no resolver link, observed 2026-08-01T23:26:00.337919Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T23:26:00.337919Z digest=sha256:c32cb6853dad47904ab9965931c58f4c2ed147e6511e23a10d1b9c9cd0dfcc2e

Observation 6bb04b70-941a-4afc-b0d4-464a09c4174f · outbound

This paper cites 2017 , eprint =.

From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems 2017 , eprint =

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-01T23:26:00.426163Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T23:26:00.426163Z digest=sha256:b2bf4a46113cab719adb7bfeb9d35c4c7f9878e989f1e9d9a7567077b009aed3

Pith citing papers

No inbound Pith citation observations are available.