TY - GEN
T1 - Considering agent and task openness in ad hoc team formation
AU - Chen, Bin
AU - Chen, Xi
AU - Timsina, Anish
AU - Soh, Leen Kiat
N1 - Publisher Copyright:
Copyright © 2015, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.
PY - 2015
Y1 - 2015
N2 - When deciding which ad hoc team to join, agents are often required to consider rewards from accomplishing tasks as well as potential benefits from learning when working with others, when solving tasks. We argue that, in order to decide when to learn or when to solve task or both, agents have to consider the existing agents' capabilities and tasks available in the environment, and thus agents have to consider agent and task openness-the rate of new, previously unknown agents (and tasks) that are introduced into the environment. We further assume that agents evolve their capabilities intrinsically through learning by observation or learning by doing when working in a team. Thus, an agent will need to consider which task to do or which team to join would provide the best situation for such learning to occur. In this paper, we develop an auction-based multiagent simulation framework, a mechanism to simulate openness in our environment, and conduct comprehensive experiments. Our results, based on more than 20,000 simulation runs, show that considering environmental openness is beneficial and necessary, and task selection strategies leveraging openness can improve agent learning and performance. We also report on observations of emergent behaviors related to openness.
AB - When deciding which ad hoc team to join, agents are often required to consider rewards from accomplishing tasks as well as potential benefits from learning when working with others, when solving tasks. We argue that, in order to decide when to learn or when to solve task or both, agents have to consider the existing agents' capabilities and tasks available in the environment, and thus agents have to consider agent and task openness-the rate of new, previously unknown agents (and tasks) that are introduced into the environment. We further assume that agents evolve their capabilities intrinsically through learning by observation or learning by doing when working in a team. Thus, an agent will need to consider which task to do or which team to join would provide the best situation for such learning to occur. In this paper, we develop an auction-based multiagent simulation framework, a mechanism to simulate openness in our environment, and conduct comprehensive experiments. Our results, based on more than 20,000 simulation runs, show that considering environmental openness is beneficial and necessary, and task selection strategies leveraging openness can improve agent learning and performance. We also report on observations of emergent behaviors related to openness.
KW - Ad hoc team formation
KW - Agent openness
KW - Learning by doing
KW - Learning by observation
KW - Task openness
UR - http://www.scopus.com/inward/record.url?scp=84944681130&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84944681130&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84944681130
T3 - Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
SP - 1861
EP - 1862
BT - AAMAS 2015 - Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems
A2 - Bordini, Rafael H.
A2 - Yolum, Pinar
A2 - Elkind, Edith
A2 - Weiss, Gerhard
PB - International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
T2 - 14th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2015
Y2 - 4 May 2015 through 8 May 2015
ER -