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README.md

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+<div align="center">
+<h1> Neural Reasoning about Agents' Goals, Preferences, and Actions </h1>
+
+**[Matteo Bortoletto][1], &nbsp; [Constantin Ruhdorfer][5], &nbsp; [Adnen Abdessaied][6], &nbsp; [Lei Shi][2], &nbsp; [Andreas Bulling][3]** <br> <br>
+**ACL 2024, Bangkok, Thailand** <br>
+**[[Paper][4]]**
+
+</div>
+
+# Citation
+
+```bibtex
+@inproceedings{bortoletto24_acl,
+  author = {Bortoletto, Matteo and Ruhdorfer, Constantin and Abdessaied, Adnen and Shi, Lei and Bulling, Andreas},
+  title = {Limits of Theory of Mind Modelling in Dialogue-Based Collaborative Plan Acquisition},
+  booktitle = {Proc. 62nd Annual Meeting of the Association for Computational Linguistics (ACL)},
+  year = {2024},
+  pages = {1--16},
+  doi = {}
+}
+```
+
+## Dataset
+
+[Original Dataset](https://huggingface.co/datasets/sled-umich/MindCraft)
+
+[Extended Dataset](https://huggingface.co/datasets/sled-umich/MindCraft2)
+
+
+## Code overview
+
+The code is based on the [original implementation](https://github.com/sled-group/collab-plan-acquisition/tree/main).
+
+### ToM tasks
+
+Baselines: 
+- training code: `baselines_with_dialogue_moves.py`
+- bash script: `baselines_with_dialogue_moves.sh`
+- model: `src/models/model_with_dialogue_moves.py`
+
+Graph models:
+- training code: `baselines_with_dialogue_moves_graphs.py`
+- bash script: `baselines_with_dialogue_moves_graphs.sh`
+- model: `src/models/model_with_dialogue_moves_graphs.py` 
+
+To extract ToM features, run `intermediate_representations.py`
+
+### CPA tasks
+
+Baselines:
+- training code: `plan_predictor.py`
+- bash script: `run_plan_predictor.sh`
+- model: `src/models/plan_model.py`
+  
+Baselines with ToM ground-truth as input:
+- trainining code: `plan_predictor_oracle.py`
+- bash script: `run_plan_predictor_oracle.sh`
+- model: `src/models/plan_model_oracle.py`
+
+Graph models:
+- training code: `plan_predictor_graphs.py`
+- bash script: `run_plan_predictor_graphs.sh`
+- model: `src/models/plan_model_graphs.py`
+  
+Graph models with ToM ground-truth as input:
+- training code: `plan_predictor_graphs_oracle.py`
+- bash script: `run_plan_predictor_graphs_oracle.sh` 
+- model: `src/models/plan_model_graphs_oracle.py`
+
+Graph models with full candidate sampling (i.e. considering all possible candidate edges):
+- training code: `plan_predictor_graphs_allcand.py`
+- bash script: `run_plan_predictor_graphs_allcand.sh` 
+- model: `src/models/plan_model_graphs.py` (same as the constrained selection)
+
+Train graph model with int0 features as input: `run_plan_predictor_graphs_int0.sh`
+
+### Analysis
+- correlation analysis: `compare_tom_cpa.ipynb`
+- diagnostic probing: `logistic_regression_tom_feats.py`. To extract CPA features, use `plan_predictor_graphs_test.py`
+
+[1]: https://mattbortoletto.github.io/
+[2]: https://perceptualui.org/people/shi/
+[3]: https://perceptualui.org/people/bulling/
+[4]: https://arxiv.org/pdf/2405.12621
+[5]: https://perceptualui.org/people/ruhdorfer/
+[6]: https://perceptualui.org/people/abdessaied/