An agent architecture with adaptive and learning capability

The decoder of AHC uses the BOXES algorithm to divide the input state variables into several regions (or boxes). The drawback of this division is that it strongly depends on an engineer's expertise. In addition, the region of each box cannot be modified even when the working environment changes or the resolution is unsatisfactory. To solve this problem, we use the ART theory to improve the decoding mechanism of the AHC architecture to propose and ART-based AHC architecture. We use this architecture to construct three agents. Each agent can control the mobile robot individually, and they perform well in simulations.