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Paper Citation Record · LEDGER

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

As of 23 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 2026-08-23T06:30:58.430688+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=sha256:341a71074c85c8018ed56719abfb59a9441b3944564a7f86d61b9cc2b05c5e95

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=sha256:446a4168697c941a356e0f4bd6478d954b7fff89bb1f41e3acf244157f93e4e6

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=sha256:498dffe86f71bf29dbc8c7cc86f32906c6b92f8fa07334c408a222e401926c73

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=sha256:5420b60aa298312533b19b4d0a28722e66b0cd5139635680c1d97a5336589e78

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=sha256:3c2f61ce6d0e4686c13a5a9efa2cd52a764343cb7062b07dcc91ec28954c9ef8

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=sha256:55397d776dbe1356b3d2dd214d1badf4be074d91ffd965c8140512af6ddf307a

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=sha256:3c4905f6989f3ebd40c6c134b473ca30ca246c46d3e142c5627610701adc640d

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=sha256:7676483c4999170aa4d103a5766d08d49b7e4257754f7c785facdbf0a988ff3e

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=sha256:4dca1409589a118bfa6200dbf25533139552b13c0e1f698709f250f225e74f39

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=sha256:140f4da47f7bba51367a12bcad6aeb4fd221bfedd93794d4206d53b760591e94

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:f59bef8e3c4bace692f9e1c68a6d05de3b24dce7725d0fc78ab61f1760135e65

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=sha256:3c95aa413bb197d9da1865e49ba7b2d9f949d05e4fb6a82e46a8f6d6cbb09395

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=sha256:3432e952e3f7195980ba73387d7a9c34e3960e24d2635775e1ecf3dc4d202857

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=sha256:8bd4a8ffdfc0994ac85d2b4be750225ec40444d40cc74e593d875077a4c5868e

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:a6188f56fd7b55d1d3b932d0905917148467c48c7c7bb1226dafc0eeb609e890

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=sha256:64aa066f44776a2a05c6a600c63929f17f279fb6c2da39d3d870a8fcfa16377e

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:b21c064335f5fc9e88bbbba1d3fc2a4d840a26b0f336568d0603b9f6f6224882

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:de39c5158d590ae7495ded0e4473736f882cbaa6524f94e7f2abc040e13ce69f

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:e19be1c1d998949979e0ce2c7c7c1b4e335dd35c69ac943ffb48121d95091197

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:b3c0d0ad6cde048d9efa5cdcd383cfd4d4c0483dec55e64770ce2ccc64a83c69

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:c4127628e814b8019bb69c567efb0676a76f9f5ef24fb013584425ccc421d02e

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=sha256:28c8c6e96b3951eb81d6b7db93929b9faa4f0695560a583cfe7a65c4a74141d8

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=sha256:09cef929577c012737a99b9e5a646a636bdd9ef5bd40e3fbefb50f6db1057ddb

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:b8d53a532f502904f2f44e62f157cac31958ce2c8a0e55182bd0aaf5d406079d

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=sha256:0185f0781dd9488fe860a87e969701efb39f257e55a9aadd1faf40d4b26b0381

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=sha256:28b6e38b8a05e8461082e2dafae7961cb2ecabed5c87052868cc3738b681d334

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=sha256:91e50c529eb347a8440425ff897b33e35fcc15865e36a03a2c87dc6a71aa8656

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=sha256:6764c50776a53056c637ddaa3392b0e924ab7faeac21aa342f3c22d5a6ac80ef

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:e4501e8ee43500c0ae0e49de0151888e4de4af0c33bacbe7b1416948f8511281

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=sha256:64927ae8357f0719f21a27eb427d68acbde04a478d9bfba69519e6c8205619de

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:f3dcfde8b676709eecca9d4b16452f83f4c664b6997795def52d9d40c253787e

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=sha256:5d1ee80adc0e5634d7f51745a2e961149daf9b0a174602f83f071301183ef512

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=sha256:23ba590b78925f85ba499b9aaac7f0e18570df40c2d4978bf4993afcc1ff5342

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 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-08-01T23:25:59.146726Z digest=sha256:353c66a5f669829a94902b7e66499e40fa4fa69fce2aa1f633e80921dfa54ba8

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=sha256:489376c3d2d669c491bbb4442d7a73f99f54fc459572671eaa20b81a073c1e1f

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:ff9b895ce28689cd59938ced890fb6a4783a0f026fca8d1a3bad1d14e9ab5d39

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=sha256:320d74d4d781bf94e209bf71f81cd235d05b6e382a0987ef4c2d077be9f4373f

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 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-08-01T23:25:59.717096Z digest=sha256:2d7321343981fb37d803c9122f284a175bb98cb9509890fee5a3bd7b6937396e

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=sha256:282c39065998dee9c68e58d46024ee7ab902cc13ba0d80e8861d0c1fe4808941

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=sha256:4975a854f0b9db99995028e895d112ecbeb19d242e41a91141e31c3971e84807

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:a2453cf1acf301baff94cab1f310116cc8cb60aa74ed508c2f052e1708624e98

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=sha256:980f7022f9a3e1ee54f0f4e44ea350bfa584f4966694db79989ec0389031c7eb

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:fb05fb4deaff4e465025e37c60e707e5ba356269fa6414c5b8435fe2738cb048

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=sha256:2567f2ea01270936545416df699178342279757b2ca63c97f97406e0d219208d

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=sha256:823996af7ee72ca5ae4f670122af4d8d6bc3e0ba533cc27ee7d6a203408d9ef4

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:c1e1258b7d88942aaeee480379572ba5a27eea97120dacca98d7d28d46222704

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:da9c19ba211e6259012878fec2e212c5bcfd7f2ceb1ffb4d3fa9e984d9ae2460

Pith citing papers

No inbound Pith citation observations are available.