TY - JOUR
T1 - RADAR
T2 - automated task planning for proactive decision support
AU - Grover, Sachin
AU - Sengupta, Sailik
AU - Chakraborti, Tathagata
AU - Mishra, Aditya Prasad
AU - Kambhampati, Subbarao
N1 - Funding Information:
This research is supported in part by ONR grants N00014-16-1-2892, N00014-18-1-2442, N00014-18-1-2840, N00014-9-1-2119, AFOSR grant FA9550-18-1-0067, DARPA SAIL-ON grant W911NF-19-2-0006, NSF grants 1936997 (C-ACCEL), 1844325, NASA grant NNX17AD06G, and a JP Morgan AI Faculty Research grant;Air Force Office of Scientific Research [FA9550-18-1-0067];Defense Advanced Research Projects Agency [W911NF-19-2-0006];National Aeronautics and Space Administration [NNX17AD06G];National Science Foundation [1844325,1936997];Office of Naval Research [N00014-9-1-2119, N00014-16-1-2892, N00014-18-1-2442, N00014-18-1-2840];JP Morgan AI Faculty Research grant; We acknowledge the help of Dr. Satya Gautam Vadlamudi in the initial design of the RADAR framework and Sarath Sreedharan for sharing his code and engaging in useful discussions in regards to generating explanations.
Publisher Copyright:
© 2020 Taylor & Francis Group, LLC.
PY - 2020/11/1
Y1 - 2020/11/1
N2 - Proactive Decision Support aims at improving the decision making experience of human decision-makers by enhancing the quality of the decisions and the ease of making them. Given that AI techniques are efficient in searching over a potentially large solution space (of decision) and finding good solutions, it can be used for human-in-the-loop scenarios such as disaster response that demand naturalistic decision making. A human decision-maker, in such scenarios, may experience high-cognitive overload leading to a loss of situational awareness. In this paper, we propose the use of automated task-planning techniques coupled with design principles laid out in the Human-Computer Interaction (HCI) community for developing a proactive decision support system. To this extent, we highlight the capabilities of such a system RADAR and briefly, describe how automated planning techniques help us in providing the varying degrees of assistance. To evaluate the effectiveness of the different capabilities, we conduct ablation studies with human subjects on a synthetic environment for making an interactive plan of study. We found that planning techniques like plan validation and suggestions help to reduce planning time (objective metrics) and improves user satisfaction (subjective metrics) compared to expert human planners without any support.
AB - Proactive Decision Support aims at improving the decision making experience of human decision-makers by enhancing the quality of the decisions and the ease of making them. Given that AI techniques are efficient in searching over a potentially large solution space (of decision) and finding good solutions, it can be used for human-in-the-loop scenarios such as disaster response that demand naturalistic decision making. A human decision-maker, in such scenarios, may experience high-cognitive overload leading to a loss of situational awareness. In this paper, we propose the use of automated task-planning techniques coupled with design principles laid out in the Human-Computer Interaction (HCI) community for developing a proactive decision support system. To this extent, we highlight the capabilities of such a system RADAR and briefly, describe how automated planning techniques help us in providing the varying degrees of assistance. To evaluate the effectiveness of the different capabilities, we conduct ablation studies with human subjects on a synthetic environment for making an interactive plan of study. We found that planning techniques like plan validation and suggestions help to reduce planning time (objective metrics) and improves user satisfaction (subjective metrics) compared to expert human planners without any support.
KW - Automated Task Planning
KW - HCI Design Theory
KW - Proactive Decision Support
UR - http://www.scopus.com/inward/record.url?scp=85085573947&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85085573947&partnerID=8YFLogxK
U2 - 10.1080/07370024.2020.1726751
DO - 10.1080/07370024.2020.1726751
M3 - Article
AN - SCOPUS:85085573947
SN - 0737-0024
VL - 35
SP - 387
EP - 412
JO - Human-Computer Interaction
JF - Human-Computer Interaction
IS - 5-6
ER -