## 5 Discussions and Conclusions  
We have reviewed different agent based routing protocols used in MANETs. Any routing protocols used in MANET share common shortcomings. The methods based on the reactive ant-like agents have to wait until the communication routes from the source to the destination have been established [41; 42; 43]. For example, DSR and AODV, like the reactive ant-like agent approaches, take some time to discover the route, which delays the establishment of the connections in a large scale network [7]. On the other hand, with the methods based on the proactive ant-like agents, since the route from a source node to the destination may have been already found, the source node can transmit data packets without the delay. The methods based on the proactive ant-like agents, however, may not function well because the network topology of a MANET is dynamic and lives of the routes are rather short [8; 9; 25; 26; 27]. They also have too much overhead for relatively the small bandwidth used in MANETs. In order to mitigate the overhead of _backward ants_, [PERSON] has proposed a routing algorithm that uses only _forward ants_ to update the routing table of intermediate nodes [28; 29]. The _forward ants_ of this algorithm keep the path length from the source node, and update the routing table based on the information. The pheromone value at each intermediate node is given by the _forward ant_ that comes through the shortest path from the source node. Whenever a node needs to send data packets to the source node, it can use the shortest path for the connection. In traditional communication scenarios, the source node and destination node act symmetrically. Therefore, when a network uses only _forward ants_, other _forward ants_ may go back to the destination to the source node resulting in the same situation using the _forward ants_ and _backward ants_. In multi-agent systems, however, the communication is asymmetrical. A message, or even a complete task, is conveyed by an agent. Therefore, using only _forward ants_ to explore the network is then a good idea. In addition, unlike in AntNet or AntHocNet where the nodes have learning ability to cope with the changing network topology (by sending _hello messages_ to each other), in the multi-agent environment, mobile agents keep the information about the network and learn as they move onto the network so that the _backward ants_ that update the routing tables are not necessary. Mobile agents utilize the routing tables as shared data structures by which they exchange information with each other. Routing tables are updated by the mobile agents only for exchanging routing information between mobile agents. The author has conducted resource discovery algorithms based on the ant colony optimization in peer-to-peer networks [50; 51]. The idea that ant-like agents roam in the network to look for a particular resource has some similarities to finding topology of a network. We have recently expanded the idea to not only data traffic but also human refugees' flow [52]. Each node, _i.e._, human refugee, has a smartphone with GPS so that the smartphone senses the flow of the people and guides him to the most promising evacuation route. In such situations, we cannot rely on a base station, and have to depend on _ad hoc_ networks. The _ad hoc_ network has many promising applications. We are witnessing the coming era of ubiquitous computing through MANETs, and the more the applications that are developed the more efficient routing protocols are required. The Ant-AODV like hybrid routing protocol may provide high connectivity with minimum overhead for processing ant-like agents. In multi-agent environments, not only route exploration ants but also data packets, or even complete tasks may be incorporated into mobile agents. Therefore establishing a communication route from thesource node to the destination node before dispatching mobile agents is not necessary and it is natural not to use backward ants to update routing tables to establish the best route.