%0 Journal Article %1 martinsequential %A Butz, Martin %A Lanzi, Pier %D 2009 %J Evolutionary Intelligence %K MVButz MVButzJ Reinforcement XCS learning machine %P -- %T Sequential problems that test generalization in learning classifier systems %U http://dx.doi.org/10.1007/s12065-009-0019-y %X We present an approach to build sequential decision making problems which can test the generalization capabilities of classifier systems. The approach can be applied to any sequential problem defined over a binary domain and it generates a new problemwith bounded sequential difficulty and bounded generalization difficulty. As an example, we applied the approach to generatetwo problems with simple sequential structure, huge number of states (more than a million), and many generalizations. Theseproblems are used to compare a classifier system with effective generalization (XCS) and a learner without generalization(Q-learning). The experimental results confirm what was previously found mainly using single-step problems: also in sequentialproblems with huge state spaces, XCS can generalize effectively by detecting those substructures that are necessary for optimalsequential behavior.

@article{martinsequential, abstract = {We present an approach to build sequential decision making problems which can test the generalization capabilities of classifier systems. The approach can be applied to any sequential problem defined over a binary domain and it generates a new problemwith bounded sequential difficulty and bounded generalization difficulty. As an example, we applied the approach to generatetwo problems with simple sequential structure, huge number of states (more than a million), and many generalizations. Theseproblems are used to compare a classifier system with effective generalization (XCS) and a learner without generalization(Q-learning). The experimental results confirm what was previously found mainly using single-step problems: also in sequentialproblems with huge state spaces, XCS can generalize effectively by detecting those substructures that are necessary for optimalsequential behavior.}, added-at = {2009-10-06T20:58:50.000+0200}, author = {Butz, Martin and Lanzi, Pier}, biburl = {https://www.bibsonomy.org/bibtex/2489adb139d5f4d25f2cb32eca668906a/butz}, description = {SpringerLink - Zeitschriftenbeitrag}, interhash = {cae6485e7a104e50083dab7937d90fe0}, intrahash = {489adb139d5f4d25f2cb32eca668906a}, journal = {Evolutionary Intelligence}, keywords = {MVButz MVButzJ Reinforcement XCS learning machine}, pages = {--}, timestamp = {2009-10-06T20:58:50.000+0200}, title = {Sequential problems that test generalization in learning classifier systems}, url = {http://dx.doi.org/10.1007/s12065-009-0019-y}, year = 2009 }