Heuristic Search for Multi-Objective Probabilistic Planning
Authors: Dillon Z. Chen, Felipe Trevizan, Sylvie Thiébaux
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | 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 {Dillon.Chen, Felipe.Trevizan, Sylvie.Thiebaux}@anu.edu.au |
| 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. |