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..
α-min: A Compact Approximate Solver For Finite-Horizon POMDPs
Authors: Yann Dujardin, Tom Dietterich, Iadine Chades
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that α-min provides good approximate solutions given a fixed number of α-vectors on small benchmark problems, on a larger randomly generated problem, as well as on a computational sustainability problem to best manage the endangered Sumatran tiger.5 Experiments We first assess the performance of α-min on four small finitehorizon POMDP problems from the literature2and a larger randomly generated problem (random30). We compare its performance to a leading infinite-horizon POMDP solver Sarsop [Kurniawati et al., 2008]. ... Overall, the performance of α-min is encouraging with surprisingly good gaps obtained considering the small quantity of α-vectors (Table 1). |
| Researcher Affiliation | Collaboration | Yann Dujardin CSIRO EMAIL Tom Dietterich School of EECS Oregon State University EMAIL Iadine Chad es CSIRO EMAIL |
| Pseudocode | Yes | Algorithm 1 Find the best belief for expanding Bt according to Equation 6 to within specified precision ϵp, Algorithm 2 ϵ-min: Solve POMDP with a maximum gap ϵ, to within precision ϵp, Algorithm 3 α-min: Solve POMDP with a maximum number N of α-vectors, to within precision ϵp |
| Open Source Code | No | No explicit statement or clear link to the source code for the methodology described in this paper was found. Footnote 1 and 3 link to 'https://sites.google.com/site/ijcaialphamin/home' and state 'POMDP files corresponding to this problem', which does not explicitly indicate the algorithms' source code. |
| Open Datasets | Yes | We first assess the performance of α-min on four small finitehorizon POMDP problems from the literature2 and a larger randomly generated problem (random30). [Footnote 2: http://pomdp.org/examples/index.shtml]Interested readers can refer to the supplementary material3 for the POMDP files corresponding to this problem. |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits. It discusses POMDP problems and a finite-horizon setup, but not in terms of data splitting for model training/evaluation. |
| Hardware Specification | Yes | α-min results were obtained using a fixed number of α-vectors set arbitrarily with a maximum computational time of 1000s per time-step on a 94.4 GB, 3.47GHz, 19 cores computer and CPLEX 12.5. |
| Software Dependencies | Yes | α-min results were obtained using a fixed number of α-vectors set arbitrarily with a maximum computational time of 1000s per time-step on a 94.4 GB, 3.47GHz, 19 cores computer and CPLEX 12.5. |
| Experiment Setup | Yes | α-min results were obtained using a fixed number of α-vectors set arbitrarily with a maximum computational time of 1000s per time-step on a 94.4 GB, 3.47GHz, 19 cores computer and CPLEX 12.5.Sarsop results were obtained by solving the POMDPs over an infinite-horizon with γ = 0.999 and a maximum computational time of 1000s. Sarsop lower bounds (LB) were calculated as the expected sum of rewards cumulated over T time-steps by simulation of the infinite policy using a γ=1. |