TY - GEN
T1 - The development of normative autonomous agents
T2 - 2015 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology Workshops, WI-IAT Workshops 2015
AU - Viana, Marx
AU - Alencar, Paulo
AU - Cowan, Donald
AU - Guimarães, Everton
AU - Cunha, Francisco
AU - Lucena, Carlos
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/2/2
Y1 - 2016/2/2
N2 - Open multi-agent systems (MASs) act as societies in which autonomous and heterogeneous agents can work towards similar or different goals. In order to cope with the heterogeneity, autonomy and diversity of interests among the different agents in the society, open MASs establish a set of behavioral norms that is used as a mechanism to ensure a state of cooperation among agents. Such norms regulate the behavior of the agents by defining obligations, permissions and prohibitions. Fulfillment of a norm may be encouraged through a reward while violation of a norm may be discouraged through punishment. Although norms are promising mechanisms to regulate an agent's behavior, we should note that each agent is an autonomous entity that is free to fulfill or violate each associated norm. Thus, agents can use different strategies when deciding to achieve their goals including whether to comply with their associated norms. Agents might choose to achieve their goals while ignoring their norms, thus overlooking the rewards or punishments they may receive. In contrast agents may choose to comply with all the norms although some of their goals may not be achieved. In this context, this paper proposes a framework for simulation of normative agents providing a basis for understanding the impacts of norms on agents.
AB - Open multi-agent systems (MASs) act as societies in which autonomous and heterogeneous agents can work towards similar or different goals. In order to cope with the heterogeneity, autonomy and diversity of interests among the different agents in the society, open MASs establish a set of behavioral norms that is used as a mechanism to ensure a state of cooperation among agents. Such norms regulate the behavior of the agents by defining obligations, permissions and prohibitions. Fulfillment of a norm may be encouraged through a reward while violation of a norm may be discouraged through punishment. Although norms are promising mechanisms to regulate an agent's behavior, we should note that each agent is an autonomous entity that is free to fulfill or violate each associated norm. Thus, agents can use different strategies when deciding to achieve their goals including whether to comply with their associated norms. Agents might choose to achieve their goals while ignoring their norms, thus overlooking the rewards or punishments they may receive. In contrast agents may choose to comply with all the norms although some of their goals may not be achieved. In this context, this paper proposes a framework for simulation of normative agents providing a basis for understanding the impacts of norms on agents.
UR - http://www.scopus.com/inward/record.url?scp=85028359819&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85028359819&partnerID=8YFLogxK
U2 - 10.1109/WI-IAT.2015.197
DO - 10.1109/WI-IAT.2015.197
M3 - Conference contribution
AN - SCOPUS:85028359819
T3 - Proceedings - 2015 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2015
SP - 9
EP - 16
BT - Proceedings - 2015 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2015
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 6 December 2015 through 9 December 2015
ER -