Graph Learning for Planning: The Story Thus Far and Open Challenges | Paper Citation Record

Observation b77d01aa-8d0a-457b-a0af-ca7812369474 · outbound

This paper cites , " * write output.state after.block = add.period write newline.

Graph Learning for Planning: The Story Thus Far and Open Challenges , " * write output.state after.block = add.period write newline

Reference 1

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Observation f053cab6-ce2b-4e09-8f04-bf29dee87ee2 · outbound

This paper cites write newline.

Graph Learning for Planning: The Story Thus Far and Open Challenges write newline

Reference 2

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Observation d1a284e8-8a5b-4486-9513-288f7de6e697 · outbound

This paper cites The surprising power of graph neural networks with random node initialization.

Graph Learning for Planning: The Story Thus Far and Open Challenges The surprising power of graph neural networks with random node initialization

Reference 3

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This paper cites Improving subgraph-gnns via edge-level ego-network encodings.

Graph Learning for Planning: The Story Thus Far and Open Challenges Improving subgraph-gnns via edge-level ego-network encodings

Reference 4

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Graph Learning for Planning: The Story Thus Far and Open Challenges Unresolved cited work

Reference 5

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This paper cites Bronstein.

Graph Learning for Planning: The Story Thus Far and Open Challenges Bronstein

Reference 6

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Observation 0ead7d26-58f9-43c7-bb96-5d30123d88ea · outbound

This paper cites General policies, subgoal structure, and planning width.

Graph Learning for Planning: The Story Thus Far and Open Challenges General policies, subgoal structure, and planning width

Reference 7

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Observation b2fa3328-4787-4c4d-a551-5b46607a7091 · outbound

This paper cites Breaking the limits of message passing graph neural networks.

Graph Learning for Planning: The Story Thus Far and Open Challenges Breaking the limits of message passing graph neural networks

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:55.157698Z

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This paper cites Kostylev, Mika \" e l Monet, Jorge P \' e rez, Juan L.

Graph Learning for Planning: The Story Thus Far and Open Challenges Kostylev, Mika \" e l Monet, Jorge P \' e rez, Juan L

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:55.143465Z

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Observation 526dbacd-a2a0-4d87-bc1e-86ef18f97311 · outbound

This paper cites Bartlett and Shahar Mendelson.

Graph Learning for Planning: The Story Thus Far and Open Challenges Bartlett and Shahar Mendelson

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:55.129890Z

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This paper cites The computational complexity of propositional STRIPS planning.

Graph Learning for Planning: The Story Thus Far and Open Challenges The computational complexity of propositional STRIPS planning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:55.115701Z

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This paper cites A review of generalized planning.

Graph Learning for Planning: The Story Thus Far and Open Challenges A review of generalized planning

Reference 12

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This paper cites Muggleton.

Graph Learning for Planning: The Story Thus Far and Open Challenges Muggleton

Reference 13

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Observation 7226eb69-bc4e-4071-87e6-0aae0fd605a4 · outbound

This paper cites A review of machine learning for automated planning.

Graph Learning for Planning: The Story Thus Far and Open Challenges A review of machine learning for automated planning

Reference 14

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This paper cites Optimize planning heuristics to rank, not to estimate cost-to-goal.

Graph Learning for Planning: The Story Thus Far and Open Challenges Optimize planning heuristics to rank, not to estimate cost-to-goal

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:55.057882Z

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This paper cites Deep Learning for Generalised Planning with Background Knowledge.

Graph Learning for Planning: The Story Thus Far and Open Challenges Deep Learning for Generalised Planning with Background Knowledge

Reference 16

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Observation b74f7f40-b8d1-4268-947a-fee75a2e7ed1 · outbound

This paper cites Chen and Sylvie Thi \' e baux.

Graph Learning for Planning: The Story Thus Far and Open Challenges Chen and Sylvie Thi \' e baux

Reference 17

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This paper cites Chen and Sylvie Thi \' e baux.

Graph Learning for Planning: The Story Thus Far and Open Challenges Chen and Sylvie Thi \' e baux

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:55.030461Z

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This paper cites Chen, Sylvie Thi \' e baux, and Felipe Trevizan.

Graph Learning for Planning: The Story Thus Far and Open Challenges Chen, Sylvie Thi \' e baux, and Felipe Trevizan

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:55.016768Z

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This paper cites Chen, Felipe Trevizan, and Sylvie Thi \' e baux.

Graph Learning for Planning: The Story Thus Far and Open Challenges Chen, Felipe Trevizan, and Sylvie Thi \' e baux

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:55.000560Z

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Observation 6956f207-9680-42cb-b2f8-b55d9e4fc32b · outbound

This paper cites Higher-dimensional potential heuristics: Lower bound criterion and connection to correlation complexity.

Graph Learning for Planning: The Story Thus Far and Open Challenges Higher-dimensional potential heuristics: Lower bound criterion and connection to correlation complexity

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.984613Z

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Observation 3594806e-0fc1-470d-af71-1d9be4ceb8d5 · outbound

This paper cites Novelty vs.

Graph Learning for Planning: The Story Thus Far and Open Challenges Novelty vs

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.968535Z

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Observation d4438763-55e7-4c49-b3f1-0f89531ee675 · outbound

This paper cites Neural logic machines.

Graph Learning for Planning: The Story Thus Far and Open Challenges Neural logic machines

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.952687Z

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Observation dfa3b3b3-e098-4496-8a90-ffb1d3ff60ea · outbound

This paper cites Equivalence-based abstractions for learning general policies.

Graph Learning for Planning: The Story Thus Far and Open Challenges Equivalence-based abstractions for learning general policies

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.938889Z

Source-reported events for the cited work

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Observation 6bbeaa41-aa93-41fb-8363-a5f626ed9e3c · outbound

This paper cites Expressing and exploiting subgoal structure in classical planning using sketches.

Graph Learning for Planning: The Story Thus Far and Open Challenges Expressing and exploiting subgoal structure in classical planning using sketches

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.924925Z

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Observation 4bd9419e-c429-458e-a925-1a68ad4ef5d9 · outbound

This paper cites How powerful are k-hop message passing graph neural networks.

Graph Learning for Planning: The Story Thus Far and Open Challenges How powerful are k-hop message passing graph neural networks

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.911229Z

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Observation d4e68786-571a-463e-9f0f-2f48d4ad9a4f · outbound

This paper cites Neural network heuristic functions for classical planning: Bootstrapping and comparison to other methods.

Graph Learning for Planning: The Story Thus Far and Open Challenges Neural network heuristic functions for classical planning: Bootstrapping and comparison to other methods

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.896517Z

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Observation 74d44cb6-0473-4c01-831c-b9f76ed930ad · outbound

This paper cites PDDL2.1: an extension to PDDL for expressing temporal planning domains.

Graph Learning for Planning: The Story Thus Far and Open Challenges PDDL2.1: an extension to PDDL for expressing temporal planning domains

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.881140Z

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Observation 00b120a2-b9a8-44fe-8879-3b5db18905fb · outbound

This paper cites A Concise Introduction to Models and Methods for Automated Planning.

Graph Learning for Planning: The Story Thus Far and Open Challenges A Concise Introduction to Models and Methods for Automated Planning

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.865769Z

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Observation 5af29866-3f6f-40ce-a527-edb2e051d9a2 · outbound

This paper cites Learning to rank for synthesizing planning heuristics.

Graph Learning for Planning: The Story Thus Far and Open Challenges Learning to rank for synthesizing planning heuristics

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.851781Z

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Observation 1f1fd96e-a686-4de1-bc27-908da75c1fe4 · outbound

This paper cites Formal representations of classical planning domains.

Graph Learning for Planning: The Story Thus Far and Open Challenges Formal representations of classical planning domains

Reference 31

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Observation 97550d1c-f2d9-4c3e-806f-596a85d91913 · outbound

This paper cites The logic of graph neural networks.

Graph Learning for Planning: The Story Thus Far and Open Challenges The logic of graph neural networks

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.824892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8a943637-105d-4886-ab5d-38b3af8b1bb7 · outbound

This paper cites LPG: A planner based on local search for planning graphs with action costs.

Graph Learning for Planning: The Story Thus Far and Open Challenges LPG: A planner based on local search for planning graphs with action costs

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.811279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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This paper cites Schoenholz, Patrick F.

Graph Learning for Planning: The Story Thus Far and Open Challenges Schoenholz, Patrick F

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.796517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a5291af4-b505-4fa0-ac3a-84c812d988cf · outbound

This paper cites Exploiting first-order regression in inductive policy selection.

Graph Learning for Planning: The Story Thus Far and Open Challenges Exploiting first-order regression in inductive policy selection

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.782628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fa9cbf8a-2d6d-4be3-9894-b897701b60b8 · outbound

This paper cites Landmarks, critical paths and abstractions: What's the difference anyway? In ICAPS , 2009.

Graph Learning for Planning: The Story Thus Far and Open Challenges Landmarks, critical paths and abstractions: What's the difference anyway? In ICAPS , 2009

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.768791Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 83226b97-3155-49f7-97a2-05d7cc792fbf · outbound

This paper cites A planning heuristic based on causal graph analysis.

Graph Learning for Planning: The Story Thus Far and Open Challenges A planning heuristic based on causal graph analysis

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.755070Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 060ced00-a0bd-47c2-8847-757a88b732c1 · outbound

This paper cites The FF planning system: Fast plan generation through heuristic search.

Graph Learning for Planning: The Story Thus Far and Open Challenges The FF planning system: Fast plan generation through heuristic search

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.740682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 568ca063-2cf1-4a55-98e2-8af45b02d5b0 · outbound

This paper cites Expressiveness of graph neural networks in planning domains.

Graph Learning for Planning: The Story Thus Far and Open Challenges Expressiveness of graph neural networks in planning domains

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.727228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 54f28665-b3f6-431d-9877-367027b71b24 · outbound

This paper cites Guiding GBFS through learned pairwise rankings.

Graph Learning for Planning: The Story Thus Far and Open Challenges Guiding GBFS through learned pairwise rankings

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.714289Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9c675a06-53f5-4066-a28a-97b0701c90e7 · outbound

This paper cites McIlraith.

Graph Learning for Planning: The Story Thus Far and Open Challenges McIlraith

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.701642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a5aa5651-ac31-4675-b8ab-52970f21fbe9 · outbound

This paper cites Relational queries computable in polynomial time (extended abstract).

Graph Learning for Planning: The Story Thus Far and Open Challenges Relational queries computable in polynomial time (extended abstract)

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.689060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fa09742e-b0f7-4eb5-b58d-79229d5eb24f · outbound

This paper cites Learning action strategies for planning domains.

Graph Learning for Planning: The Story Thus Far and Open Challenges Learning action strategies for planning domains

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.675341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 694f0bc6-9154-4bff-87d8-a7d8a6b80ed9 · outbound

This paper cites Kriege, Fredrik D.

Graph Learning for Planning: The Story Thus Far and Open Challenges Kriege, Fredrik D

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.661600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0b3a48cd-97b1-43eb-a94a-6bb66e0422b7 · outbound

This paper cites Learning generalized relational heuristic networks for model-agnostic planning.

Graph Learning for Planning: The Story Thus Far and Open Challenges Learning generalized relational heuristic networks for model-agnostic planning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.647937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1930e028-5622-4500-a520-0bf65579a04f · outbound

This paper cites an unresolved cited work.

Graph Learning for Planning: The Story Thus Far and Open Challenges Unresolved cited work

Reference 46

Resolution: unresolved; raw_fallback, observed 2026-08-11T23:51:54.633424Z

Source-reported events for the cited work

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Observation b8c4b488-ef1b-4e4b-884e-19aab506f435 · outbound

This paper cites Width and serialization of classical planning problems.

Graph Learning for Planning: The Story Thus Far and Open Challenges Width and serialization of classical planning problems

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.620181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6d18af38-1107-4e56-a8d5-125d60b041d7 · outbound

This paper cites Bronstein, Martin Grohe, and Stefanie Jegelka.

Graph Learning for Planning: The Story Thus Far and Open Challenges Bronstein, Martin Grohe, and Stefanie Jegelka

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.606182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0fe7dbdb-581e-48c7-84c4-7e99c7a9c667 · outbound

This paper cites Online planner selection with graph neural networks and adaptive scheduling.

Graph Learning for Planning: The Story Thus Far and Open Challenges Online planner selection with graph neural networks and adaptive scheduling

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.592125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c5b060f5-1f7a-437e-911d-a1b1d2aaf7a7 · outbound

This paper cites Tenenbaum, and Leslie Pack Kaelbling.

Graph Learning for Planning: The Story Thus Far and Open Challenges Tenenbaum, and Leslie Pack Kaelbling

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.578967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation eb4c4d2a-af4e-498a-bccf-9251e2825eac · outbound

This paper cites Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe.

Graph Learning for Planning: The Story Thus Far and Open Challenges Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.565885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 71ce1b13-97b4-4311-b3d1-ee871692971b · outbound

This paper cites Speqnets: Sparsity-aware permutation-equivariant graph networks.

Graph Learning for Planning: The Story Thus Far and Open Challenges Speqnets: Sparsity-aware permutation-equivariant graph networks

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.552244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 867e1478-1b98-495f-b4bf-c051839fb7ad · outbound

This paper cites Weisfeiler and leman go sparse: Towards scalable higher-order graph embeddings.

Graph Learning for Planning: The Story Thus Far and Open Challenges Weisfeiler and leman go sparse: Towards scalable higher-order graph embeddings

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.539553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 171dcc89-45c9-4061-8327-b183a169f70f · outbound

This paper cites On using admissible bounds for learning forward search heuristics.

Graph Learning for Planning: The Story Thus Far and Open Challenges On using admissible bounds for learning forward search heuristics

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.526649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a2f39247-9f0d-4ff5-aed2-587582ed1977 · outbound

This paper cites an unresolved cited work.

Graph Learning for Planning: The Story Thus Far and Open Challenges Unresolved cited work

Reference 55

Resolution: unresolved; raw_fallback, observed 2026-08-11T23:51:54.513446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 26f6b282-1850-441e-98e6-f00889b2d9da · outbound

This paper cites Rosenschein.

Graph Learning for Planning: The Story Thus Far and Open Challenges Rosenschein

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.499762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9a41b8d8-de3a-44bb-9f15-40ecd1f7a519 · outbound

This paper cites The LAMA planner: Guiding cost-based anytime planning with landmarks.

Graph Learning for Planning: The Story Thus Far and Open Challenges The LAMA planner: Guiding cost-based anytime planning with landmarks

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.485470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7f24979a-6da8-4bb1-b67e-1b382829999a · outbound

This paper cites Sutton and Andrew G.

Graph Learning for Planning: The Story Thus Far and Open Challenges Sutton and Andrew G

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.471570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e6174186-7d98-4e81-8785-8f4f7062eb49 · outbound

This paper cites Learning general optimal policies with graph neural networks: Expressive power, transparency, and limits.

Graph Learning for Planning: The Story Thus Far and Open Challenges Learning general optimal policies with graph neural networks: Expressive power, transparency, and limits

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.457642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a7ad2c37-bb21-4a16-8f9d-da823c32bc04 · outbound

This paper cites Learning general policies with policy gradient methods.

Graph Learning for Planning: The Story Thus Far and Open Challenges Learning general policies with policy gradient methods

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.442669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f9ddce6b-ac4c-4aae-bd01-4e2d7121dd03 · outbound

This paper cites Learning More Expressive General Policies for Classical Planning Domains.

Graph Learning for Planning: The Story Thus Far and Open Challenges Learning More Expressive General Policies for Classical Planning Domains

Reference 61

Resolution: verified exact; local_arxiv, observed 2026-08-11T23:51:54.092472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a27bcfbb-cb3d-44dd-9bdb-9975ea899b6b · outbound

This paper cites Tenenbaum, Tom \' a s Lozano - P \' e rez, and Leslie Pack Kaelbling.

Graph Learning for Planning: The Story Thus Far and Open Challenges Tenenbaum, Tom \' a s Lozano - P \' e rez, and Leslie Pack Kaelbling

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.428400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5f1f7862-3099-4568-a93b-3fd0b213d489 · outbound

This paper cites Heuristics and symmetries in classical planning.

Graph Learning for Planning: The Story Thus Far and Open Challenges Heuristics and symmetries in classical planning

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.414290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9b3a0967-cc54-4bd6-a576-53e1a46f9ce1 · outbound

This paper cites Correlation complexity of classical planning domains.

Graph Learning for Planning: The Story Thus Far and Open Challenges Correlation complexity of classical planning domains

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.399963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8b4bf9c9-0ef8-44ba-9da8-2a677c316920 · outbound

This paper cites Foundations and Applications of Generalized Planning.

Graph Learning for Planning: The Story Thus Far and Open Challenges Foundations and Applications of Generalized Planning

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.386382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 64b36de2-e236-49c7-a919-d56282276393 · outbound

This paper cites Theoretical foundations for structural symmetries of lifted PDDL tasks.

Graph Learning for Planning: The Story Thus Far and Open Challenges Theoretical foundations for structural symmetries of lifted PDDL tasks

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.372587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 297dc406-f174-4d7c-996f-a5facd528df9 · outbound

This paper cites Search-guidance mechanisms for numeric planning through subgoaling relaxation.

Graph Learning for Planning: The Story Thus Far and Open Challenges Search-guidance mechanisms for numeric planning through subgoaling relaxation

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.359496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation cff8ba9d-3ccf-423a-858e-69c7d2eed5d6 · outbound

This paper cites Weisfeiler-lehman graph kernels.

Graph Learning for Planning: The Story Thus Far and Open Challenges Weisfeiler-lehman graph kernels

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.346613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ef2f40ad-9b27-485e-985a-771518ffa654 · outbound

This paper cites Trevizan, Sam Toyer, Sylvie Thi \' e baux, and Lexing Xie.

Graph Learning for Planning: The Story Thus Far and Open Challenges Trevizan, Sam Toyer, Sylvie Thi \' e baux, and Lexing Xie

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.332595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4e6b1829-0adc-46c4-97af-0436025db062 · outbound

This paper cites L earning D omain- I ndependent P lanning H euristics with H ypergraph N etworks.

Graph Learning for Planning: The Story Thus Far and Open Challenges L earning D omain- I ndependent P lanning H euristics with H ypergraph N etworks

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.318966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 86232810-9c2a-433c-b67e-5e48a21af95a · outbound

This paper cites The 2023 international planning competition.

Graph Learning for Planning: The Story Thus Far and Open Challenges The 2023 international planning competition

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.305099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 36c20dbc-fe75-4dd2-902e-38913e89dab7 · outbound

This paper cites Trevizan, Sylvie Thi \' e baux, and Lexing Xie.

Graph Learning for Planning: The Story Thus Far and Open Challenges Trevizan, Sylvie Thi \' e baux, and Lexing Xie

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.291497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bfae8466-9ef3-4095-8a6c-3aac4351a885 · outbound

This paper cites Asnets: Deep learning for generalised planning.

Graph Learning for Planning: The Story Thus Far and Open Challenges Asnets: Deep learning for generalised planning

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.277552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 61a40a98-ce05-41a9-a976-1ca3b2acaf79 · outbound

This paper cites Statistical learning theory.

Graph Learning for Planning: The Story Thus Far and Open Challenges Statistical learning theory

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.263289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5a78491f-6ccd-41ad-83d7-650e595b6202 · outbound

This paper cites an unresolved cited work.

Graph Learning for Planning: The Story Thus Far and Open Challenges Unresolved cited work

Reference 75

Resolution: unresolved; raw_fallback, observed 2026-08-11T23:51:54.248674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7c690d73-4b86-491d-8e23-1ec64e9eb0b1 · outbound

This paper cites Planbench: An extensible benchmark for evaluating large language models on planning and reasoning about change.

Graph Learning for Planning: The Story Thus Far and Open Challenges Planbench: An extensible benchmark for evaluating large language models on planning and reasoning about change

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.233836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2f53a060-2001-4fb0-9b3c-3e1c2d3dd8bd · outbound

This paper cites On the planning abilities of large language models - A critical investigation.

Graph Learning for Planning: The Story Thus Far and Open Challenges On the planning abilities of large language models - A critical investigation

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.218803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 35243b79-c6c3-4935-b2ed-79fec76bb65d · outbound

This paper cites LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench.

Graph Learning for Planning: The Story Thus Far and Open Challenges LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-11T23:51:53.839149Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9cba6df6-99a0-4b63-a460-b2c6efe8f610 · outbound

This paper cites N -wl: A new hierarchy of expressivity for graph neural networks.

Graph Learning for Planning: The Story Thus Far and Open Challenges N -wl: A new hierarchy of expressivity for graph neural networks

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.202966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 255188d1-e018-4791-b111-a18e0765c08e · outbound

This paper cites Wang and Sylvie Thi \' e baux.

Graph Learning for Planning: The Story Thus Far and Open Challenges Wang and Sylvie Thi \' e baux

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.189655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 64123d33-8b49-43fd-bb0e-db9639cf15ff · outbound

This paper cites How powerful are graph neural networks? In ICLR , 2019.

Graph Learning for Planning: The Story Thus Far and Open Challenges How powerful are graph neural networks? In ICLR , 2019

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.176830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1587f8b5-0e21-4fe4-bc2a-c9e3d690a60a · outbound

This paper cites From stars to subgraphs: Uplifting any GNN with local structure awareness.

Graph Learning for Planning: The Story Thus Far and Open Challenges From stars to subgraphs: Uplifting any GNN with local structure awareness

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.162602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fd81bb87-f2da-444a-b01b-a3444dad58ab · outbound

This paper cites A practical, progressively-expressive GNN.

Graph Learning for Planning: The Story Thus Far and Open Challenges A practical, progressively-expressive GNN

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:54.147224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 15fc9636-e73e-4a6b-837a-0b8bb7a69b15 · outbound

This paper cites @esa ( ) , n @biblabelnum##1 ##1.

Graph Learning for Planning: The Story Thus Far and Open Challenges @esa ( ) , n @biblabelnum##1 ##1

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-11T23:51:53.868235Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b80f381-c5fd-44e2-af58-6dbab4b9d915 · outbound

This paper cites an unresolved cited work.

Graph Learning for Planning: The Story Thus Far and Open Challenges Unresolved cited work

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-11T23:51:53.873564Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0aa6c394-050c-4f2f-8a9b-0241dddb8591 · outbound

This paper cites THr_v e 1G :۲Q;;Z ^׎ h&@i pWzf D p@cgYd >i 3 )b!VJV 6i@ '1`v0 :x 3 7_#:G['z ;/` Yf97' Pd VU`T r#. 2/BP ùĵC0ҼC b78 @SK.

Graph Learning for Planning: The Story Thus Far and Open Challenges THr_v e 1G :۲Q;;Z ^׎ h&@i pWzf D p@cgYd >i 3 )b!VJV 6i@ '1`v0 :x 3 7_#:G['z ;/` Yf97' Pd VU`T r#. 2/BP ùĵC0ҼC b78 @SK

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-11T23:51:53.878489Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers