Paper Citation Record · LEDGER

NTRL: Encounter Generation via Reinforcement Learning for Dynamic Difficulty Adjustment in Dungeons and Dragons

As of 23 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2506.19530.

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

pith.paper-citation-record.v1
2506.19530 v2

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:40:23.266430Z

measured 24 of 24 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

24 of 24 outbound references displayed

verified exact0
verified fuzzy18
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3e2621de-91ac-4202-9a3f-0821098a8557 · outbound

This paper cites Mearls and J.

NTRL: Encounter Generation via Reinforcement Learning for Dynamic Difficulty Adjustment in Dungeons and Dragons Mearls and J

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:40:23.574391Z

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.

Observation 3341d602-6a54-478a-9307-12c63c34567b · outbound

This paper cites Mearls et al.

NTRL: Encounter Generation via Reinforcement Learning for Dynamic Difficulty Adjustment in Dungeons and Dragons Mearls et al

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:40:23.562429Z

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.

Observation 6ef870eb-339c-49e3-99b1-381418e97992 · outbound

This paper cites Automatic play-testing of dungeons and dragons combat encounters,.

NTRL: Encounter Generation via Reinforcement Learning for Dynamic Difficulty Adjustment in Dungeons and Dragons Automatic play-testing of dungeons and dragons combat encounters,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:40:23.550936Z

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.

Observation c6d37783-3e58-495c-9321-3e61fcdaf055 · outbound

This paper cites No player left behind: evolving dungeons and dragons combat to optimize difficulty and player contributions,.

NTRL: Encounter Generation via Reinforcement Learning for Dynamic Difficulty Adjustment in Dungeons and Dragons No player left behind: evolving dungeons and dragons combat to optimize difficulty and player contributions,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:40:23.538975Z

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.

Observation 37e27a98-9287-40de-b914-e4a83de0da1c · outbound

This paper cites Dynamic difficulty adjustment approaches in video games: a systematic literature review,.

NTRL: Encounter Generation via Reinforcement Learning for Dynamic Difficulty Adjustment in Dungeons and Dragons Dynamic difficulty adjustment approaches in video games: a systematic literature review,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:40:23.526820Z

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.

Observation bd01443c-3ae8-4299-aaaf-1d96169a27fe · outbound

This paper cites an unresolved cited work.

NTRL: Encounter Generation via Reinforcement Learning for Dynamic Difficulty Adjustment in Dungeons and Dragons Unresolved cited work

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T18:40:23.185131Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87f74874-9b91-4b61-abef-c9f99ebe2f34 · outbound

This paper cites Automated Playtesting with Procedural Personas through MCTS with Evolved Heuristics.

NTRL: Encounter Generation via Reinforcement Learning for Dynamic Difficulty Adjustment in Dungeons and Dragons Automated Playtesting with Procedural Personas through MCTS with Evolved Heuristics

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T18:40:23.193917Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ba8a5c8-1ed9-4317-9962-c68baa8a6f7e · outbound

This paper cites Automatic Playtesting for Game Parameter Tuning via Active Learning.

NTRL: Encounter Generation via Reinforcement Learning for Dynamic Difficulty Adjustment in Dungeons and Dragons Automatic Playtesting for Game Parameter Tuning via Active Learning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T18:40:23.198187Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 549c6fdc-9163-48bd-85e2-4a68c82f711b · outbound

This paper cites A survey of monte carlo tree search methods,.

NTRL: Encounter Generation via Reinforcement Learning for Dynamic Difficulty Adjustment in Dungeons and Dragons A survey of monte carlo tree search methods,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T18:40:23.203351Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23dad429-75f2-4ef9-989b-14c24bb91a16 · outbound

This paper cites Artificial intelligence methods for automated difficulty and power balance in games,.

NTRL: Encounter Generation via Reinforcement Learning for Dynamic Difficulty Adjustment in Dungeons and Dragons Artificial intelligence methods for automated difficulty and power balance in games,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:40:23.499688Z

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.

Observation d39e7dcf-44f0-4bf3-b418-fd39a8cdf155 · outbound

This paper cites A review of dynamic difficulty adjustment methods for serious games,.

NTRL: Encounter Generation via Reinforcement Learning for Dynamic Difficulty Adjustment in Dungeons and Dragons A review of dynamic difficulty adjustment methods for serious games,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:40:23.486194Z

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.

Observation 3e67e4c9-13bf-4d23-a97e-2cbcbb9db817 · outbound

This paper cites Dynamic difficulty adjustment using deep reinforcement learning: A review,.

NTRL: Encounter Generation via Reinforcement Learning for Dynamic Difficulty Adjustment in Dungeons and Dragons Dynamic difficulty adjustment using deep reinforcement learning: A review,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:40:23.474820Z

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.

Observation 44d499c1-f3ff-460e-94bf-45cfaaad1b12 · outbound

This paper cites Investigating reinforcement learning for dynamic difficulty adjustment,.

NTRL: Encounter Generation via Reinforcement Learning for Dynamic Difficulty Adjustment in Dungeons and Dragons Investigating reinforcement learning for dynamic difficulty adjustment,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:40:23.462810Z

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.

Observation a3c892c9-e86b-4e9e-ab01-666cbb2989c7 · outbound

This paper cites The world of anwin: reinforcement learning in role- playing games,.

NTRL: Encounter Generation via Reinforcement Learning for Dynamic Difficulty Adjustment in Dungeons and Dragons The world of anwin: reinforcement learning in role- playing games,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:40:23.449222Z

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.

Observation f4843017-2267-4578-9610-929c4b23420e · outbound

This paper cites A framework for designing reinforcement learning agents with dynamic difficulty adjust- ment in single-player action video games,.

NTRL: Encounter Generation via Reinforcement Learning for Dynamic Difficulty Adjustment in Dungeons and Dragons A framework for designing reinforcement learning agents with dynamic difficulty adjust- ment in single-player action video games,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:40:23.436627Z

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.

Observation a79a4f78-cac3-4a2b-83f4-83e8e4c9a3fd · outbound

This paper cites Dungeons and DQNs: Toward reinforcement learning agents that play tabletop roleplaying games.

NTRL: Encounter Generation via Reinforcement Learning for Dynamic Difficulty Adjustment in Dungeons and Dragons Dungeons and DQNs: Toward reinforcement learning agents that play tabletop roleplaying games

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:40:23.423570Z

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.

Observation b1ceb0f9-d1b2-4bd9-b554-4149357eb845 · outbound

This paper cites Automated playtesting in videogames,.

NTRL: Encounter Generation via Reinforcement Learning for Dynamic Difficulty Adjustment in Dungeons and Dragons Automated playtesting in videogames,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:40:23.409708Z

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.

Observation f267ab66-2d13-44ba-8253-4ad6cf9b252f · outbound

This paper cites A contextual-bandit approach to personalized news article recommendation,.

NTRL: Encounter Generation via Reinforcement Learning for Dynamic Difficulty Adjustment in Dungeons and Dragons A contextual-bandit approach to personalized news article recommendation,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:40:23.396373Z

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.

Observation f42797a8-ba6c-4cf3-8832-b068c7912309 · outbound

This paper cites Simple statistical gradient-following algorithms for connectionist reinforcement learning,.

NTRL: Encounter Generation via Reinforcement Learning for Dynamic Difficulty Adjustment in Dungeons and Dragons Simple statistical gradient-following algorithms for connectionist reinforcement learning,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T18:40:23.245967Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e02d251-f70a-4a26-804d-59490d553066 · outbound

This paper cites Policy gradi- ent methods for reinforcement learning with function approximation,.

NTRL: Encounter Generation via Reinforcement Learning for Dynamic Difficulty Adjustment in Dungeons and Dragons Policy gradi- ent methods for reinforcement learning with function approximation,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T18:40:23.250769Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1de23a6d-91ef-4f98-8c27-f00503f4b989 · outbound

This paper cites DnDSimulator,.

NTRL: Encounter Generation via Reinforcement Learning for Dynamic Difficulty Adjustment in Dungeons and Dragons DnDSimulator,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:40:23.366586Z

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.

Observation 91d1e5b2-df94-4d65-9ad1-ea115d48960a · outbound

This paper cites Mearls, J.

NTRL: Encounter Generation via Reinforcement Learning for Dynamic Difficulty Adjustment in Dungeons and Dragons Mearls, J

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:40:23.352612Z

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.

Observation c404dbbd-5298-4179-9924-cd854867674b · outbound

This paper cites [Online].

NTRL: Encounter Generation via Reinforcement Learning for Dynamic Difficulty Adjustment in Dungeons and Dragons [Online]

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:40:23.339797Z

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.

Observation 0c4b9f82-5b62-4e54-b7da-c8889be775ba · outbound

This paper cites [Online].

NTRL: Encounter Generation via Reinforcement Learning for Dynamic Difficulty Adjustment in Dungeons and Dragons [Online]

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:40:23.326824Z

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.

Pith citing papers

No inbound Pith citation observations are available.