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..
Heuristic Search for Multi-Objective Probabilistic Planning
Authors: Dillon Z. Chen, Felipe Trevizan, Sylvie Thiébaux
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments In this section we empirically evaluate the different algorithms and heuristics for MOSSPs in several domains. |
| Researcher Affiliation | Academia | Dillon Z. Chen1, Felipe Trevizan1, Sylvie Thi ebaux1,2 1School of Computing, The Australian National University 2LAAS-CNRS, ANITI, Universit e de Toulouse EMAIL |
| Pseudocode | Yes | Algorithm 1: MOVI, Algorithm 2: MOVI under Assumption 1, Algorithm 3: MOLAO, Algorithm 4: IMOLAO, Algorithm 5: MOLRTDP |
| Open Source Code | Yes | We implemented the MO versions of the VI, TVI, (i)LAO and LRTDP algorithms and the MO version of the PDB abstraction heuristics (Hpdb mossp) in C++.2 PDB heuristics are computed using TVI, ε = 0.001 and b = 100.2 Code at https://github.com/Dillon ZChen/cpp-mossp-planner |
| Open Datasets | No | Since no benchmark domains for MOSSPs exist, we adapt domains from a variety of sources to capture challenging features of both SSPs and MO deterministic planning. (No direct public access information for the adapted datasets is provided.) |
| Dataset Splits | No | No specific dataset split information (like percentages or sample counts) for training, validation, or testing is provided. The paper discusses problem domains rather than traditional datasets with splits. |
| Hardware Specification | Yes | The experiments are run on a cluster with Intel Xeon 3.2 GHz CPUs. |
| Software Dependencies | Yes | We used CPLEX version 22.1 as the LP solver for computing CCS. |
| Experiment Setup | Yes | The consistency threshold is set to ε = 0.001 and we set b = 100. |