Vidal's libraryTitle: | Believing others: Pros and cons |
Author: | Sandip Sen |
Journal: | Artificial Intelligence |
Volume: | 142 |
Number: | 2 |
Pages: | 179--203 |
Month: | December |
Year: | 2002 |
DOI: | 10.1016/S0004-3702(02)00289-8 |
Abstract: | In open environments there is no central control over agent behaviors. On the contrary, agents in such systems can be assumed to be primarily driven by self interests. Under the assumption that agents remain in the system for significant time periods, or that the agent composition changes only slowly, we have previously presented a prescriptive strategy for promoting and sustaining cooperation among self-interested agents. The adaptive, probabilistic policy we have prescribed promotes reciprocative cooperation that improves both individual and group performance in the long run. In the short run, however, selfish agents could still exploit reciprocative agents. In this paper, we evaluate the hypothesis that the exploitative tendencies of selfish agents can be effectively curbed if reciprocative agents share their opinions of other agents. Since the true nature of agents is not known a priori and is learned from experience, believing others can also pose its own hazards. We provide a learned trust-based evaluation function that is shown to resist both individual and concerted deception on the part of selfish agents in a package delivery domain. |
Cited by 45 - Google Scholar
@Article{sen02b,
author = {Sandip Sen},
title = {Believing others: Pros and cons },
googleid = {9o0Ox8094xoJ:scholar.google.com/},
journal = {Artificial Intelligence},
year = 2002,
volume = 142,
number = 2,
pages = {179--203},
month = {December},
abstract = {In open environments there is no central control
over agent behaviors. On the contrary, agents in
such systems can be assumed to be primarily driven
by self interests. Under the assumption that agents
remain in the system for significant time periods,
or that the agent composition changes only slowly,
we have previously presented a prescriptive strategy
for promoting and sustaining cooperation among
self-interested agents. The adaptive, probabilistic
policy we have prescribed promotes reciprocative
cooperation that improves both individual and group
performance in the long run. In the short run,
however, selfish agents could still exploit
reciprocative agents. In this paper, we evaluate the
hypothesis that the exploitative tendencies of
selfish agents can be effectively curbed if
reciprocative agents share their opinions of other
agents. Since the true nature of agents is not known
a priori and is learned from experience, believing
others can also pose its own hazards. We provide a
learned trust-based evaluation function that is
shown to resist both individual and concerted
deception on the part of selfish agents in a package
delivery domain.},
keywords = {game-theory multiagent learning},
url = {http://jmvidal.cse.sc.edu/library/sen02b.pdf},
doi = {10.1016/S0004-3702(02)00289-8},
comment = {masrg},
cluster = {1937460218716655094}
}
Last modified: Wed Mar 9 10:15:32 EST 2011