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