Online Learning and Teaching of Emergent Behaviors in Multi-Robot Teams

Abstract

In this manuscript, we propose an approach that allows a team of robots to create new (emergent) behaviors at execution time. Basically, we improve the approach called N-Learning used for selfprogramming of robots in a team, by modifying and extending its functioning structure. The basic capability of behavior sharing is increased by the catching of emergent behaviors at run time. With this, all robots are able not only to share existing knowledge, here represented by blocks of codes containing desired behaviors but also to creating new behaviors as well. Experiments with real robots are presented in order to validate our approach. The experiments demonstrate that after the human-robot interaction with one robot using Program by Demonstration, this robot generates a new behavior at run time and teaches a second robot that performs the same learned behavior through this improved version of the N-learning system.

Classification Method of Aerobics Course Online Teaching Resources Based on Artificial Intelligence Technology

“…e specific calculation principle follows the following formula, where a represents the threshold value: Memory based collaborative filtering technology encompasses both user-based collaborative filtering algorithms and project-based collaborative filtering algorithms. eir most common drawback is that they have sparse data and find it difficult to deal with timely results when dealing with large amounts of data [14]. As a result, developing a modelbased collaborative filtering algorithm is essential.…”