Resource-bounded Norm Monitoring In Multi-agent Systems
Authors: Natalia Criado
JAIR 2018 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also demonstrate in randomised simulations and benchmark experiments that our monitor can select monitored resources effectively and efficiently, detecting more norm violations and fulfilments than other tractable optimization approaches and obtaining slightly worse results than intractable optimal approaches. |
| Researcher Affiliation | Academia | Natalia Criado EMAIL King s College London Bush House, WC2B 4BG, London, UK |
| Pseudocode | Yes | Algorithm 1 contains the pseudocode executed by the norm monitor. ... Function 2 contains the pseudocode for the resource selection function. |
| Open Source Code | No | The paper does not contain any explicit statement about providing open-source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | To conduct a more comparable experimentation, we used four domains from a benchmark dataset provided by Ram ırez and Geffner (2009), comprising of hundreds of problem descriptions. In particular, we used the domains: blocks-words, easy-grid-navigation, intrusion-detection, and logistics. |
| Dataset Splits | No | The paper describes generating environments randomly or selecting problem descriptions from a benchmark dataset, but it does not specify fixed training, test, or validation splits with percentages or sample counts for any dataset. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory amounts used for running the experiments. |
| Software Dependencies | No | The paper mentions implementing a simulator and using algorithms like CELF and a Heuristic Search Planner, but it does not provide specific version numbers for any programming languages, libraries, or solvers used. |
| Experiment Setup | Yes | We conducted experiments in which the number of resources R took a random value within the J10, 50K interval and the number of agents G is set to 100. ... The number of propositions P takes a random value within the J2R, 10RK interval. ... the number of literals in pre and post follows a Poisson distribution with λ set to 1. The number of actions A takes a random value within the JR, 2PK interval... The number of norms takes random value within the JA/2, AK... The cost of each resource is randomly defined as 1 plus a random noise generated by a Poisson distribution... Finally, we randomly set the budget ratio... to a random value within the [0, 1] interval. The simulation is executed 50 steps |