Combining Preference Elicitation and Search in Multiobjective State-Space Graphs
Authors: Nawal Benabbou, Patrice Perny
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Numerical Tests We have evaluated the performance of the two algorithms respectively presented in Section 3 and 4 in terms of computation times (in seconds) and number of queries. Results are obtained by averaging over 30 runs and linear optimizations are performed using the Gurobi library of Java. The algorithm based on MR minimization is denoted R hereafter whereas the one based on MRε minimization is denoted R ε. |
| Researcher Affiliation | Academia | Nawal Benabbou and Patrice Perny Sorbonne Universites, UPMC Univ Paris 06, UMR 7606, LIP6 CNRS, UMR 7606, LIP6, F-75005, Paris, France 4 Place Jussieu, 75005 Paris, France nawal.benabbou@lip6.fr, patrice.perny@lip6.fr |
| Pseudocode | No | The paper describes algorithmic steps in narrative text but does not include formally labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention the availability of open-source code or provide any links to a code repository. |
| Open Datasets | No | The paper describes a synthetically generated dataset: 'all nodes in N are uniformly drawn in the two dimension grid {1, . . . , 1000} {1, . . . , 1000}, but source node s and goal node γ are respectively located in (1, 500) and (1000, 500). Each node is linked to 30 randomly chosen nodes and the associated cost vectors are randomly drawn using Gaussian distributions parametrized according to Euclidean distances.' There is no mention of a publicly available dataset with access information. |
| Dataset Splits | No | The paper describes how graph instances were generated but does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper states, 'linear optimizations are performed using the Gurobi library of Java,' but it does not specify version numbers for Gurobi or Java, which is required for reproducibility. |
| Experiment Setup | Yes | For each node n N, we set H(n) = {I(n)} where I(n)=(I1(n), . . . , Iq(n)) is the ideal point defined by Ii(n) = minp P (n,γ) gi(p) for all i Q. We consider S-shaped disutility functions vi, i Q, of the form: vi(xi) = 1 / (1 + e^(-ai(xi - bi))) where xi is the ith component of cost vector x; ai and bi are parameters enabling respectively to control the amplitude of the S and the position of the S along the i-th criterion. Results are obtained by averaging over 30 runs. |