Incremental Weight Elicitation for Multiobjective State Space Search
Authors: Nawal Benabbou, Patrice Perny
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we report various numerical tests aiming at evaluating the performance of elicitation procedures S1 and S2 within Algorithm 2, both in terms of computation time and number of preference queries. |
| Researcher Affiliation | Academia | Sorbonne Universites, UPMC Univ Paris 06, UMR 7606, LIP6 CNRS, UMR 7606, LIP6, F-75005, Paris, France |
| Pseudocode | Yes | Algorithm 1: Ω-Filter(X) and Algorithm 2: POΩ(X) computation |
| Open Source Code | No | The paper does not contain any statement about releasing source code for the methodology, nor does it provide links to any code repositories. |
| Open Datasets | No | We consider instances of G = (N, A) with one goal node γ and generated as follows: nodes in N are uniformly drawn in the two dimension grid {1, . . . , 1000} {1, . . . , 1000}, except γ and the source node s which are respectively located in (1000, 500) and (1, 500). The paper does not provide access information for this generated data. |
| Dataset Splits | No | The paper describes generating instances for numerical tests but does not provide explicit training, validation, or test dataset splits or cross-validation details. |
| Hardware Specification | No | The paper states 'Linear optimizations are performed using the Gurobi library of Java' but provides no specific details about the hardware (e.g., CPU, GPU models, memory) used for the experiments. |
| Software Dependencies | No | The paper mentions 'Linear optimizations are performed using the Gurobi library of Java' but does not specify version numbers for either Gurobi or Java. |
| Experiment Setup | Yes | These tests are used to determine a necessarily near-optimal path for the approximation threshold λ = 0.01 (meaning that the MMR value will be below 0.01). We consider instances of G = (N, A) with one goal node γ and generated as follows: nodes in N are uniformly drawn in the two dimension grid {1, . . . , 1000} {1, . . . , 1000}... Simulated DMs answer to queries according to a linear scalarizing function fω, where ω is randomly chosen in {int(Rq +):P i N ωi =1}. |