Learning by Linear Anticipation in Multi-Agent Systems
ECAI '96 Selected papers from the Workshop on Distributed Artificial Intelligence Meets Machine Learning, Learning in Multi-Agent Environments
SAB'10 Proceedings of the 11th international conference on Simulation of adaptive behavior: from animals to animats
| Hi-index | 0.00 |
We investigated the role of the length of the future time interval in which an agent predicts what will happen. A number of simulated robot experiments were performed where four thieves try to collect pieces of gold from a house that is guarded by a single robot. The thieves try to anticipate the movement of the guard to select behaviors that will allow them to steel the gold without being seen. This scenario was investigated in four experiments with different visual fields of the guard and different strategies of the thieves. The results show that it is not always better to predict longer into the future and that best behavior would results when the agents match their predictions to the time it will take to perform their tasks.