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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=sha256:45d053d8cfcabd8cce058b615447eb91764d92d5879442b8d6138914c386321d

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=sha256:690b76cddedfc19d5e63a5dd39bd5e298bbc126c352319e63df14a91db734258

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:cc0547f2a7ebc185a7dd2049d9bf803b718670a81238d06a903d6a4e5850ac93

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=sha256:5a1566947536a00a97ed78b49740613368794729cdf5ed07349c0af896039cb4

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=sha256:4ffb60449f0f3b69f19dcf04ff44a58d9372426a923b9ddcd58ec6e839bec086

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=sha256:384181def820f2278c87cf1fd30c40ee8a460994c201b5124871589f636d849b

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:b6e44cdf1412d1695aa9ee47a28982074f9ad78ff2fc257f9cbfcb8d3b593794

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=sha256:85a1dfd1d9bf2754c6fe791e65cc61715b784d89bce1a2226288a2f543acf49f

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:f68edbbee1786975be9f82a1e0168d68aad26f9b0cd0a477a91911eac7b0125f

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:f7f21cca63d57ba22fb8d13c855b80692cdd429998bd93663a4f037a9ff538c6

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=sha256:12a0c3746ad1670a7df389eae78ea68f0bea04ba348037bd79b18533a7aebe20

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:d77a4b9639385f34db9ebf4adb95dd53441a03cde464e08703c4ab6f6c85e794

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=sha256:94ea5c55ace09ef93d707ab7598f3d01a1b09c9e1f97b515933d1986a540779d

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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source=arxiv_source observed=2026-08-01T23:25:56.542613Z digest=sha256:e86511bbb1eb4b25f7ee00bc743ed02fab637fa070798ab31d3d71d871a12a01

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=sha256:895d63870e64cee78a3527f26ea9d53873212d3eaec01f06b1731b7f0121eafb

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=sha256:62b9145d68a91618d99577abd1008b0dfc3745e763ff7f66785b1f2fe30886e8

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=sha256:978c64eafb5bbc43d22aa02d0961659d7ec0772930a0b0b5c1d5739ef2d229b6

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:b950d174f13ee5ed52263d113ebd33ccaabd61b9e63c8c663b82d484ea8d07a9

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:af23338799caff5936d6e355a1beef804bdd5419f8110c4457b9f8109c84ac79

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:e00959139e2ae5c6c4d617646117af2cec7bdc293b3ad262e7cbe589a905b9fd

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:ad8f95c142b0c020fe984a16fb194704abafd66453bdbef61778bdc3ef870ad5

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:fc6c2532afb2c19c90b651de38271181a039eea1de8b7e7a9afe4c2ec42fbadc

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=sha256:3a24a5408bfdfe6bb4f863817306fb43fd45edd9b3410eb168bf73432c90ff41

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=sha256:5a9451fc95e15ae0696e9e65055f93eb7281b1db0ab6c1cda981fdb55d64b1bc

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=sha256:0ed15f5489471eef2994e14c62626462d06f2dd837410c6a3ec0b8cbf4f1711c

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:a9c702a7220b5677b5ca677232f019e27ddfaa52efe2ed84d09f3147a715819b

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:a25597f75d219f9dfa66957bae031118588ee424347f37da54187407861727e7

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:bb7c09291f80f28dbdb476bf98ebe7cc421aa7ed883375e49fb1912cafed743f

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=sha256:60e66d5dca8beeb64ad1365a5eae7a032cef89fbc47222375b881c5b97a1b57a

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:c8c047dd755149e38f2e2a6e9625ed5a80712a24cb0d9ceddff1ba639c56eb41

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:da3b5bde334be3b5b5e46690cc177a7e2449a1493aeca3638221f4e260490ef5

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=sha256:29d9d08648fffe5e84bd0b8cb2d4ba09c8406f9008996c519b451e8289b70032

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=sha256:0993e092c63d4aed59cf4d83a67634c25d7bcfa1629e555ce24391b10f3bad81

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=sha256:85577644f22b1a7b0f9746020638aa4a6d241006961151d6635a56ce7752bf3d

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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doi, observed 2026-08-01T23:28:29.588148Z

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source=arxiv_source observed=2026-08-01T23:25:59.242904Z digest=sha256:35d2410f4069330dc6300104aa275c3285782867f53675ecbc9d7a651f7b2d4d

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:a2ec2663b69aaaeb22277c0965d8bec7e61741789675026ed79e78ad70e8a89a

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:ddd43002b848b7120fca9e5e58c5522f97b10f239f0b4d6074a959864853681f

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.

source=arxiv_source observed=2026-08-01T23:25:59.637679Z digest=sha256:f3813b49833db4b507fd9ef128ead31fa241899f94ba7c33390c05d975e27ed3

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

Resolution
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:f3bbb1f337f6b83da7bfe539ffb4e177daf638cf4a7d271265c994210ab73385

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

Resolution
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=sha256:6b8fc4c1a8997f5a42c6292a107692e1bf668dab8cfc00d7490034d2d618df4d

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

Resolution
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:f2acbf81a891b03a3381eda68b17d9c8cd46764ad0f3d5f4dff0f6318b18fc49

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=sha256:96e944ef299ad31d868d65ad9b66f1f2185c447cf86b46d826b55b9f41f7a9bc

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

Resolution
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=sha256:8e1ec2619e96a225d7cebb8fd1c732f3f8458ff780c4457343331e53105f74c7

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

Resolution
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=sha256:310185c892d4b14573b45ef3c4b19a0db420e686a85eb7583ec7bdb67067a137

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

Resolution
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:e666b5c84376d168c146ea981112157108619a17e32730b4166864489cb197c3

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

Resolution
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:cff1e6750a6d51b460dd8c104ba467e22da9f393f4b1a4db8f3f31d05a059b6e

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

Resolution
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=sha256:1ebf776418a88b4954eb4f054b81c337853f44ed4f03707fc4908f098ace6c0c

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=sha256:049afda0fe5f1f82ceb2d666d5256bab001e6d0c0b93c20b05fe8d38ac41cfc1

Pith citing papers

No inbound Pith citation observations are available.