A Bayesian-Symbolic Approach to Reasoning and Learning in Intuitive Physics
Authors: Kai Xu, Akash Srivastava, Dan Gutfreund, Felix Sosa, Tomer Ullman, Josh Tenenbaum, Charles Sutton
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that BSP is more sample-efficient compared to neural alternatives on controlled synthetic datasets, demonstrate BSP s applicability to real-world common sense scenes and study BSP s performance on tasks previously used to study human physical reasoning. |
| Researcher Affiliation | Collaboration | Kai Xu University of Edinburgh contact@xuk.ai Akash Srivastava MIT-IBM Watson AI Lab akash.srivastava@ibm.com Dan Gutfreund MIT-IBM Watson AI Lab dgutfre@us.ibm.com Felix A. Sosa Harvard University fsosa@fas.harvard.edu Tomer Ullman Harvard University tomerullman@gmail.com Joshua B. Tenenbaum Massachusetts Institute of Technology jbt@mit.edu Charles Sutton University of Edinburgh & Google AI c.sutton@ed.ac.uk |
| Pseudocode | Yes | Note appendix A.3 also provides all pseudo-code for algorithms introduced in this section. |
| Open Source Code | Yes | Source code as well as training and testing data can be accessed at https://bsp.xuk.ai/. |
| Open Datasets | Yes | Source code as well as training and testing data can be accessed at https://bsp.xuk.ai/.; To demonstrate this, we use the PHYS101 dataset (Wu et al., 2016), a dataset of real world physical scenes.; For this purpose, we use the ULLMAN dataset from this study, which consists of 60 videos in which a set of discs interact with each other and mats within a bounded area, as exemplified in figure 9. |
| Dataset Splits | No | The paper mentions holding out 20 scenes for evaluation (testing) and using the first k scenes for training, but it does not explicitly describe a separate validation set or its split. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'Turing probabilistic programming language' and 'Expr Optimization.jl package' in Julia, but it does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | In our work we consider maximally three forces to be learn in the same time, thus setting K = 3; Finally, we add Gaussian noise to each trajectory... and sigma is the noise level.; lambda is the hyper-parameter that controls the regularization; we use the L-BFGS optimizer to solve the lower-level optimization; All scenes are simulated using a physics engine with a time-discretization of 0.02, for 50 frames. |