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Robot control with artificial immune systems

Within this structure several RLAs are connected within a network. Each RLA consists of a sensory condition C, a robot action command A and an expectation of the forthcoming sensory input E.

Therefore each RLA represents a possible state of the robot within its environment described by the current sensory input C. The robot state directly results in a possible action A connected to the sensory input C within the same RLA node.

To generate RLAs a short time memory is needed to store short sequences of recent and interesting (i.e. significantly changing) sensory and motor events. This short term memory contains raw material to built new RLAs. But since a RLA node contains an expectation E concerning the consequences of the action A RLA nodes can only be
built at certain moments, when the system got the information about the consequence E of its action A.

Each RLA node contains short term knowledge of the robots environment.
This knowledge contains the actual robot state C and a possible action A and the sensor expectation E.

Transferring the RLA nodes from the RLA pool into the RLA network and connecting them in a meaningful way extends the knowledge the robot has about its environment. After the learning process of the RLA network has finished, the network stores the long term knowledge of the environment in the whole network distributed as short term knowledge in the nodes.

Depending on the training method the RLA structure can show different kind of behaviours like wall following or obstacle avoidance. Figure 45 shows the result of such a training process done with a Khepera simulator. Based on such basic first level movements the RLA structure can be arranged in a hierarchical manner where second level RLAs can switch the behaviour or extract more complex information about the robots environment.

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Napier University
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