TY - GEN
T1 - Adaptive advice in automobile climate control systems
AU - Rosenfeld, Ariel
AU - Azaria, Amos
AU - Kraus, Sarit
AU - Goldman, Claudia V.
AU - Tsimhoni, Omer
N1 - Publisher Copyright:
Copyright © 2015, International Foundation for Autonomous Agents and Multiagent Systems.
PY - 2015
Y1 - 2015
N2 - Reducing an automobile's energy consumption will lower its dependency on fossil fuel and extend the travel range of electric vehicles. Automobile Climate Control Systems (CCS) are known to be heavy energy consumers. To help reduce CCS energy consumption, this paper presents an adaptive automated agent, MDP Agent for Climate control Systems (MACS), which provides drivers with advice as to how to set their CCS. First, we present a model which has 78% accuracy in predicting drivers' reactions to different advice in different situations. Using the prediction model, we designed a Markov Decision Process which solution provided the advising policy for MACS. Through empirical evaluation using an electric car, with 83 human subjects, we show that MACS successfully reduced the energy consumption of the subjects by 33% compared to subjects who were not equipped with MACS. MACS also outperformed the state-of-the-art Social agent for Advice Provision (SAP).
AB - Reducing an automobile's energy consumption will lower its dependency on fossil fuel and extend the travel range of electric vehicles. Automobile Climate Control Systems (CCS) are known to be heavy energy consumers. To help reduce CCS energy consumption, this paper presents an adaptive automated agent, MDP Agent for Climate control Systems (MACS), which provides drivers with advice as to how to set their CCS. First, we present a model which has 78% accuracy in predicting drivers' reactions to different advice in different situations. Using the prediction model, we designed a Markov Decision Process which solution provided the advising policy for MACS. Through empirical evaluation using an electric car, with 83 human subjects, we show that MACS successfully reduced the energy consumption of the subjects by 33% compared to subjects who were not equipped with MACS. MACS also outperformed the state-of-the-art Social agent for Advice Provision (SAP).
KW - Advice Provision
KW - Energy Aware Systems
KW - Human-Agent Interaction
UR - http://www.scopus.com/inward/record.url?scp=84944676630&partnerID=8YFLogxK
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AN - SCOPUS:84944676630
T3 - Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
SP - 543
EP - 551
BT - AAMAS 2015 - Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems
A2 - Elkind, Edith
A2 - Bordini, Rafael H.
A2 - Weiss, Gerhard
A2 - Yolum, Pinar
T2 - 14th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2015
Y2 - 4 May 2015 through 8 May 2015
ER -