Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Intention Progression with Temporally Extended Goals
Authors: Yuan Yao, Natasha Alechina, Brian Logan
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate our approach using a version of the Mars Rover domain [Duff et al., 2006], in which the tasks assigned to the Rover are specified using temporally extended goals. |
| Researcher Affiliation | Academia | Yuan Yao1 , Natasha Alechina2,3 , Brian Logan4,3 1University of Nottingham Ningbo China 2 Open University Netherlands 3Utrecht University 4University of Aberdeen EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Return the step to be executed at this cycle |
| Open Source Code | Yes | The software used in the experiments is freely available at https: //github.com/yvy714/TEG-MCTS. |
| Open Datasets | No | The Mars Rover domain is a two-dimensional grid environment where the agent (the rover) can move around and conduct soil experiments at different locations (see Figure 5)... The locations for conducting soil experiments and the holes are randomly generated. However, the base and the experiment locations are not located in a hole. |
| Dataset Splits | No | The paper describes experimental runs in a simulated environment ('average performance over 50 runs') but does not specify train/validation/test dataset splits as it's not a traditional dataset-based machine learning task. |
| Hardware Specification | Yes | our prototype implementation requires about 50 milliseconds to return the best next plan for 15 concurrent intentions on a 1.4 GHz Quad-Core Intel Core i5 |
| Software Dependencies | No | The paper mentions 'The software used in the experiments is freely available at https: //github.com/yvy714/TEG-MCTS' but does not specify any particular software components with version numbers. |
| Experiment Setup | Yes | In each deliberation cycle, the scheduler performs 100 iterations (α = 100) and 10 simulations per iteration (β = 10). |