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Reasoning about Hypothetical Agent Behaviours and their Parameters.
Stefano Albrecht
and Peter Stone.
In Proceedings of the 16th International Conference
on Autonomous Agents and Multiagent Systems (AAMAS-17), May 2017.
Available from IFAAMAS
and from ACM
[PDF]608.2kB [slides.pdf]1.2MB
Agents can achieve effective interaction with previously unknown other agents by maintaining beliefs over a set of hypothetical behaviours, or types, that these agents may have. A current limitation in this method is that it does not recognise parameters within type specifications, because types are viewed as blackbox mappings from interaction histories to probability distributions over actions. In this work, we propose a general method which allows an agent to reason about both the relative likelihood of types and the values of any bounded continuous parameters within types. The method maintains individual parameter estimates for each type and selectively updates the estimates for some types after each observation. We propose different methods for the selection of types and the estimation of parameter values. The proposed methods are evaluated in detailed experiments, showing that updating the parameter estimates of a single type after each observation can be sufficient to achieve good performance.
@inproceedings{AAMAS17-Albrecht,
title = {Reasoning about Hypothetical Agent Behaviours and their Parameters},
author = {Stefano Albrecht and Peter Stone},
booktitle = {Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS-17)},
location = {S\~ao Paulo, Brazil},
month = {May},
year = {2017},
abstract = {
Agents can achieve effective interaction with previously unknown other agents by
maintaining beliefs over a set of hypothetical behaviours, or types, that these
agents may have. A current limitation in this method is that it does not
recognise parameters within type specifications, because types are viewed as
blackbox mappings from interaction histories to probability distributions over
actions. In this work, we propose a general method which allows an agent to
reason about both the relative likelihood of types and the values of any bounded
continuous parameters within types. The method maintains individual parameter
estimates for each type and selectively updates the estimates for some types
after each observation. We propose different methods for the selection of types
and the estimation of parameter values. The proposed methods are evaluated in
detailed experiments, showing that updating the parameter estimates of a single
type after each observation can be sufficient to achieve good performance.},
wwwnote={Available from <a href="http://www.ifaamas.org/Proceedings/aamas2017/pdfs/p547.pdf">IFAAMAS</a> and from <a href="https://dl.acm.org/citation.cfm?id=3091206&dl=ACM&coll=DL">ACM</a>},
}
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