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Gilad Armon-Kest. Supporting Collaborative Activity. Master's Thesis,
Bar Ilan University,2007.
This thesis has two parts. The first part presents SharedActivity, a model forcollaborative agents acting in a group. The model suggests mental attitudes foragents with different levels of cooperation and allows modelling of groups inwhich members are motivated to increase individual benefits. Unlike previousmodels, SharedActivity is suitable also for groups that do not have a joint goal.The model defines key components of loosely cooperative activity and providesa platform for developing tools to support such activity. We have studied thebehavior of the model in a simulation environment. Results show how the benefit attained by cooperation is influenced by the complexity of the environment,the number of group members as well as the social dependencies between themembers. The results demonstrate that the model covers social behavior both insettings previously addressed, as well as novel settings. The second part presents an algorithm for solving the problem of iterativesearch in a closed group. Our solution takes into account the load on agents andthe agents’ willingness to help other agents. It also manages reciprocity betweenagents. The proposed algorithm supports limited-resource platforms. We evaluatethe behavior of our algorithm in a simulation environment and compare it to therandom walk algorithm. Results show an advantage for our algorithm regardingthe random walk algorithm in most environmental settings. These advantages areexpressed in retrieving times of wanted objects and fairness in task distribution.
@MastersThesis{gilad-msc,
author = {Gilad Armon-Kest},
title = {Supporting Collaborative Activity},
school = {{B}ar {I}lan {U}niversity},
year = {2007},
OPTkey = {},
OPTtype = {},
OPTaddress = {},
OPTmonth = {},
OPTnote = {},
OPTannote = {},
wwwnote = {},
abstract = {This thesis has two parts. The first part presents SharedActivity, a model for
collaborative agents acting in a group. The model suggests mental attitudes for
agents with different levels of cooperation and allows modelling of groups in
which members are motivated to increase individual benefits. Unlike previous
models, SharedActivity is suitable also for groups that do not have a joint goal.
The model defines key components of loosely cooperative activity and provides
a platform for developing tools to support such activity. We have studied the
behavior of the model in a simulation environment. Results show how the benefit attained by cooperation is influenced by the complexity of the environment,
the number of group members as well as the social dependencies between the
members. The results demonstrate that the model covers social behavior both in
settings previously addressed, as well as novel settings.
The second part presents an algorithm for solving the problem of iterative
search in a closed group. Our solution takes into account the load on agents and
the agents’ willingness to help other agents. It also manages reciprocity between
agents. The proposed algorithm supports limited-resource platforms. We evaluate
the behavior of our algorithm in a simulation environment and compare it to the
random walk algorithm. Results show an advantage for our algorithm regarding
the random walk algorithm in most environmental settings. These advantages are
expressed in retrieving times of wanted objects and fairness in task distribution.
},
}
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