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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Interplanetary Trajectory Planning with Monte Carlo Tree Search
Authors: Daniel Hennes, Dario Izzo
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We discuss our experimental set-up, in particular parameter search and runtime analysis. We evaluate our approach on two well-known missions: Cassini-Huygens and Rosetta. |
| Researcher Affiliation | Academia | Daniel Hennes and Dario Izzo European Space Agency Advanced Concepts Team Noordwijk, The Netherlands EMAIL, EMAIL |
| Pseudocode | No | The paper describes the MCTS steps in paragraph text and provides equations, but does not include formal pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not provide any explicit statement or link for open-source code for the described methodology. |
| Open Datasets | Yes | For this computation we use the analytical planet ephemerides defined by NASA / Jet Propulsion Laboratory1. 1The approximated ephemerides were used as defined in http: //ssd.jpl.nasa.gov/?planet pos [accessed November 2014] |
| Dataset Splits | No | The paper describes a parameter search to tune the algorithm's parameters but does not specify training/test/validation dataset splits in the conventional sense for model evaluation. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers needed to replicate the experiment. |
| Experiment Setup | Yes | The UCB-1 (see Equation (1)) and ϵ-greedy (see Equation 2) selection policies require one parameter choice each. We sample 4000 parameter instances uniformly on a logarithmic scale. For each parameter instance, one run of UCT with the selected policy is performed until the computational budget of N Lambert legs is depleted. |