Intention Progression under Uncertainty
Authors: Yuan Yao, Natasha Alechina, Brian Logan, John Thangarajah
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the performance of our approach experimentally by varying the degree of uncertainty in the agent s beliefs. The results suggest that SAU is able to successfully achieve the agent s goals even in settings where there is significant uncertainty in the agent s beliefs. |
| Researcher Affiliation | Academia | Yuan Yao1 , Natasha Alechina2 , Brian Logan3 and John Thangarajah4 1 College of Computer Science and Technology, Zhejiang University of Technology 2 Department of Information and Computing Sciences, University of Utrecht 3 School of Computer Science, University of Nottingham 4 School of Science, RMIT University |
| Pseudocode | Yes | Algorithm 1 Return the best action at this cycle |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the SAU methodology described in this paper. It only links to the synthetic goal-plan trees used in experiments, which are data. |
| Open Datasets | Yes | We generated 50 sets of 10 goal-plan trees... The trees are available at: https://bit.ly/35jxkt2. |
| Dataset Splits | No | The paper describes generating and evaluating on '50 sets of 10 goal-plan trees' but does not specify any training, validation, or test dataset splits for model training or hyperparameter tuning. |
| Hardware Specification | No | The paper mentions computational overhead in milliseconds but does not provide specific hardware details such as GPU/CPU models, memory, or processor types used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | SA and SAU were configured to perform 100 iterations (α = 100) and 10 simulations per iteration (β = 10) |