TY - GEN
T1 - AutoDOViz
T2 - 28th International Conference on Intelligent User Interfaces, IUI 2023
AU - Weidele, Daniel Karl I.
AU - Afzal, Shazia
AU - Valente, Abel N.
AU - Makuch, Cole
AU - Cornec, Owen
AU - Vu, Long
AU - Subramanian, Dharmashankar
AU - Geyer, Werner
AU - Nair, Rahul
AU - Vejsbjerg, Inge
AU - Marinescu, Radu
AU - Palmes, Paulito
AU - Daly, Elizabeth M.
AU - Franke, Loraine
AU - Haehn, Daniel
N1 - Publisher Copyright:
© 2023 Owner/Author.
PY - 2023/3/27
Y1 - 2023/3/27
N2 - We present AutoDOViz, an interactive user interface for automated decision optimization (AutoDO) using reinforcement learning (RL). Decision optimization (DO) has classically being practiced by dedicated DO researchers [43] where experts need to spend long periods of time fine tuning a solution through trial-and-error. AutoML pipeline search has sought to make it easier for a data scientist to find the best machine learning pipeline by leveraging automation to search and tune the solution. More recently, these advances have been applied to the domain of AutoDO [36], with a similar goal to find the best reinforcement learning pipeline through algorithm selection and parameter tuning. However, Decision Optimization requires significantly more complex problem specification when compared to an ML problem. AutoDOViz seeks to lower the barrier of entry for data scientists in problem specification for reinforcement learning problems, leverage the benefits of AutoDO algorithms for RL pipeline search and finally, create visualizations and policy insights in order to facilitate the typical interactive nature when communicating problem formulation and solution proposals between DO experts and domain experts. In this paper, we report our findings from semi-structured expert interviews with DO practitioners as well as business consultants, leading to design requirements for human-centered automation for DO with RL. We evaluate a system implementation with data scientists and find that they are significantly more open to engage in DO after using our proposed solution. AutoDOViz further increases trust in RL agent models and makes the automated training and evaluation process more comprehensible. As shown for other automation in ML tasks [33, 59], we also conclude automation of RL for DO can benefit from user and vice-versa when the interface promotes human-in-the-loop.
AB - We present AutoDOViz, an interactive user interface for automated decision optimization (AutoDO) using reinforcement learning (RL). Decision optimization (DO) has classically being practiced by dedicated DO researchers [43] where experts need to spend long periods of time fine tuning a solution through trial-and-error. AutoML pipeline search has sought to make it easier for a data scientist to find the best machine learning pipeline by leveraging automation to search and tune the solution. More recently, these advances have been applied to the domain of AutoDO [36], with a similar goal to find the best reinforcement learning pipeline through algorithm selection and parameter tuning. However, Decision Optimization requires significantly more complex problem specification when compared to an ML problem. AutoDOViz seeks to lower the barrier of entry for data scientists in problem specification for reinforcement learning problems, leverage the benefits of AutoDO algorithms for RL pipeline search and finally, create visualizations and policy insights in order to facilitate the typical interactive nature when communicating problem formulation and solution proposals between DO experts and domain experts. In this paper, we report our findings from semi-structured expert interviews with DO practitioners as well as business consultants, leading to design requirements for human-centered automation for DO with RL. We evaluate a system implementation with data scientists and find that they are significantly more open to engage in DO after using our proposed solution. AutoDOViz further increases trust in RL agent models and makes the automated training and evaluation process more comprehensible. As shown for other automation in ML tasks [33, 59], we also conclude automation of RL for DO can benefit from user and vice-versa when the interface promotes human-in-the-loop.
KW - Automation
KW - Decision optimization
KW - Reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85152139440&partnerID=8YFLogxK
U2 - 10.1145/3581641.3584094
DO - 10.1145/3581641.3584094
M3 - Conference contribution
AN - SCOPUS:85152139440
T3 - International Conference on Intelligent User Interfaces, Proceedings IUI
SP - 664
EP - 680
BT - Proceedings Of 2023 28th Annual Conference On Intelligent User Interfaces, Iui 2023
PB - Association for Computing Machinery
Y2 - 27 March 2023 through 31 March 2023
ER -