Multi-Objective Influence Diagrams with Possibly Optimal Policies
Authors: Radu Marinescu, Abdul Razak, Nic Wilson
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare variants of the algorithm experimentally. |
| Researcher Affiliation | Collaboration | Radu Marinescu IBM Research Ireland radu.marinescu@ie.ibm.com Abdul Razak and Nic Wilson Insight Centre for Data Analytics University College Cork, Ireland {abdul.razak,nic.wilson}@insight-centre.org |
| Pseudocode | Yes | Algorithm 1: MOVE(OPT) |
| Open Source Code | No | The paper does not contain any explicit statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | No | The problem instances were generated using the random problem generator from (Marinescu, Razak, and Wilson 2012) using 2, 3 and 5 objectives, respectively. (No specific public access details for the generated instances or the generator were provided for reproducibility.) |
| Dataset Splits | No | The paper uses generated problem instances but does not specify any training/validation/test splits. |
| Hardware Specification | Yes | All algorithms were written in C++ and the experiments were run on a 2.6GHz processor with 4GB of RAM. |
| Software Dependencies | No | The paper states 'All algorithms were written in C++', but does not provide specific version numbers for compilers, libraries, or other software dependencies. |
| Experiment Setup | Yes | All algorithms were allotted a 20 minute time limit per problem instance. For each problem size (characterized by the number of chance variables C, number of decision variables D, number of objectives O), we generated 100 random instances. The random tradeoffs are mainly characterized by the parameters K and T, where K is the number of pairwise or binary tradeoffs and T is the number of 3-way tradeoffs, respectively. |