@inproceedings{01e82c1c382d47faaf7fba1582dabfd1,
title = "Towards social complexity reduction in multiagent learning: the adhoc approach",
abstract = "This paper presents a novel method for classifying adversaries that is designed to achieve social complexity reduction in large-scale, open multiagent systems. In contrast to previous work on opponent modelling, we seek to generalise from individuals and to identify suitable opponent classes. To validate the adequacy of our approach, we present initial experiments ina multiagent Iterated Prisoner{\textquoteright}s Dilemma seenario and we discuss directions for future work on the subject",
author = "Michael Rovatsos and Marco Wolf",
year = "2002",
language = "English",
series = " AAAI Technical Report ",
publisher = "AAAI Press",
number = "SS-02-02",
pages = "90--97",
booktitle = "Collaborative Learning Agents. Papers from 2002 AAAI Spring Symposium on",
}