Anticipation mappings for learning classifier systems

In this paper, we study the use of anticipation mappings in learning classifier systems. At first, we enrich the extended classifier system (XCS) with two types of anticipation mappings: one based on array of perceptrons array, one based on neural networks. We apply XCS with anticipation mappings (XCSAM) to several multistep problems taken from the literature and compare its anticipatory performance with that of the neural classifier system X-NCS which is based on a similar approach. Our results show that, although XCSAM is not a "true" anticipatory classifier system like ACS, MACS, or X-NCS, nevertheless XCSAM can provide accurate anticipatory predictions while requiring smaller populations than those needed by X-NCS.