In this paper we present AntNet, a novel adaptive approach to routing tables learning in packet-switched communications networks. AntNet is inspired by the stigmergy model of communication observed in ant colonies. We present compelling evidence that AntNet, when measuring performance by standard measures such as network throughput and average packet delay, outperforms the current Internet routing algorithm (OSPF), some old Internet routing algorithms (SPF and distributed adaptive Bellman-Ford), and recently proposed forms of asynchronous online Bellman-Ford (Q-routing and Predictive Q-routing).