Should Robots be Obedient?
Authors: Smitha Milli, Dylan Hadfield-Menell, Anca Dragan, Stuart Russell
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Figure 2: Autonomy advantage (left) and obedience O (right) over time. and All experiments in this paper use the following parameters unless otherwise noted. At the start of each episode θ N(0, I) and at each step φn(a) N(0, I). There are 10 actions, 10 features, and β = 2. 2 Finally, even with good approximations we may still have good reason for feeling hesitation about disobedient robots. |
| Researcher Affiliation | Academia | Smitha Milli, Dylan Hadfield-Menell, Anca Dragan, Stuart Russell University of California, Berkeley {smilli,dhm,anca,russell}@berkeley.edu |
| Pseudocode | No | No pseudocode or algorithm block is present in the paper. |
| Open Source Code | Yes | All experiments can be replicated using the Jupyter notebook available at http://github.com/smilli/obedience |
| Open Datasets | No | All experiments in this paper use the following parameters unless otherwise noted. At the start of each episode θ N(0, I) and at each step φn(a) N(0, I). There are 10 actions, 10 features, and β = 2. |
| Dataset Splits | No | The paper uses a “simpler repeated game” where “each state is independent of the next”, but no explicit training/validation/test splits, percentages, or sample counts are mentioned. |
| Hardware Specification | No | No specific hardware details (such as GPU or CPU models, memory, or cloud instances) are mentioned for the experimental setup. |
| Software Dependencies | No | The paper mentions a 'Jupyter notebook' but does not provide specific version numbers for software dependencies such as programming languages, libraries, or frameworks (e.g., Python version, PyTorch version). |
| Experiment Setup | Yes | All experiments in this paper use the following parameters unless otherwise noted. At the start of each episode θ N(0, I) and at each step φn(a) N(0, I). There are 10 actions, 10 features, and β = 2. |