A Polynomial Time Optimal Algorithm for Robot-Human Search under Uncertainty
Authors: Shaofei Chen, Tim Baarslag, Dengji Zhao, Jing Chen, Lincheng Shen
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Furthermore, we empirically evaluate our solution with simulations and show that it significantly outperforms several benchmark approaches. To evaluate the performance of our Search Rule (called Optimal for short in this section) for RHS, we design five baseline benchmark strategies for comparison in terms of average utilities and interactions of robot, human and environment. |
| Researcher Affiliation | Academia | 1 College of Mechatronics and Automation, National University of Defense Technology, China. 2 Electronics and Computer Science, University of Southampton, UK. |
| Pseudocode | Yes | Algorithm 1 Executing search policy |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | No | The paper describes generating synthetic scenarios with uniform distributions for rewards, but does not refer to a publicly available or open dataset with access information. "In each scenario, the reward probability function of each item is setted a uniform distribution U(a, b), with a < b uniformly sampled from U(0, 1)." |
| Dataset Splits | No | The paper describes conducting simulations with varying parameters (e.g., human availability, reveal costs) and running 1000 simulations for each setting, but it does not specify explicit train/validation/test dataset splits for model development or evaluation. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as CPU or GPU models, or cloud computing specifications. |
| Software Dependencies | No | The paper does not mention any specific software dependencies or their version numbers, such as programming languages, libraries, or solvers used for implementation or experimentation. |
| Experiment Setup | Yes | Experiment A: We vary the human availability p as a parameter of the experiments, choosing values between 0 and 1 with 0.05 increments. Experiment B: In each scenario, the reward probability function of each item is setted a uniform distribution U(a, b), with a < b uniformly sampled from U(0, 1). Other parameters are setted as: p = 0.75, cask = 0.02. We vary the reveal costs creveal_i as a parameter of the experiments, choosing values between 0 and 0.2 with 0.02 increments. for each human availability, we make 1000 simulations. |