AI GYM, 2023 - 2026. The goal of this libray is provide a set of tools to help you to learn the
basics of Artificial Intelligence. In other words, the goal of this library is help you to learn how
to build agents that solve problems by searching. This library implements several search algorithms
and has a common interface for agents, allowing you to easily create and deploy agents
that solve problems by searching.
CARRETE, L. F.; CASTANARES, M.; DAMIANI, E.; BARTH, F.. DSSE: Drone Swarm Search Environment. 2023.
The Drone Swarm Search project is an environment, based on PettingZoo, that is to be used in conjunction with multi-agent (or single-agent)
reinforcement learning algorithms. It is an environment in which the agents (drones), have to find the targets (shipwrecked people). The agents
do not know the position of the target, and do not receive rewards related to their own distance to the target(s). However, the agents receive the
probabilities of the target(s) being in a certain cell of the map. The aim of this project is to aid in the study of reinforcement learning algorithms
that require dynamic probabilities as inputs.
ANDRADE, J.; SILVA, W.; SANTOS, G.; BARTH, F.. MLBugDetection. 2022.
Machine learning explainability and unexpectated behaviors detection.
Most machine learning explainability packages requires both trained models and the training data to create Explainer objects that explain
the model's behavior. This package allows ceteris paribus analysis of features using only the trained model and one or more
input samples.
