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..
Saturated Path-Constrained MDP: Planning under Uncertainty and Deterministic Model-Checking Constraints
Authors: Jonathan Sprauel, Andrey Kolobov, Florent Teichteil-Königsbuch
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We propose a dynamic programming-based algorithm for finding such policies, and empirically demonstrate this algorithm to be orders of magnitude faster than its next-best alternative. ... The objectives of our experiments are two-fold: (1) to compare SPC VI with the algorithm for PC MDPs, PC MDP-ILP (Teichteil-Konigsbuch 2012), in terms of efficiency, and (2) to validate SPC MDPs as an efficient modeling tool. The experiments were run with 5.8 GB of RAM on a 2.80GHz CPU. |
| Researcher Affiliation | Collaboration | Jonathan Sprauel and Florent Teichteil-K onigsbuch EMAIL ONERA The French Aerospace Lab 2 Avenue Edouard-Belin, F-31055 Toulouse, France Andrey Kolobov EMAIL Microsoft Research Redmond, WA-98052, USA |
| Pseudocode | Yes | Algorithm 1: SPC MDP Value Iteration, Algorithm 2: explore(S, Tip) function, Algorithm 3: update Reachability(S, ξi, θ) function |
| Open Source Code | No | The paper does not provide a direct link or an explicit statement about the availability of its source code. |
| Open Datasets | No | We randomly generated instances with different grid dimensions (between 10x10 and 100x100, with a total number of states between 200 and 160 000), time parameter values, and numbers of zones of each type (between 1 and 5 per type). ... We tested instances having from 1 computer (1246 states) to 3 computers (1 014 013 states). The paper describes generating its own instances rather than using or providing concrete access to a publicly available dataset. |
| Dataset Splits | No | The paper does not explicitly describe training, validation, and test dataset splits. |
| Hardware Specification | Yes | The experiments were run with 5.8 GB of RAM on a 2.80GHz CPU. |
| Software Dependencies | No | The paper mentions using the "PPDDL language" but does not specify any software names with version numbers or other key software dependencies. |
| Experiment Setup | Yes | In all experiments, the SPC MDP discount factor was γ = 0.9. ... For ϵoptimality we set ϵ = 0.1, with a corresponding ω = 0.001, since we fixed the penalty of a fire occurrence to -1. ... Since the reward function ranges from -50 to 50, we chose a parameter ϵ 10 for the ϵoptimality; the corresponding ω parameter is 0.001. |