Meher T. Shaikh
Michael A. Goodrich
ABSTRACT
An important part of expressing human intent is identifying acceptable tradeoffs among competing performance objectives. We present and evaluate a set of graphical user interfaces (GUIs), that are designed to allow a human to express intent by expressing desirable tradeoffs. The GUIs require an algorithm that identifies the set of Pareto optimal solutions to the multi-objective decision problem, which means that all the solutions are equally good in the sense that there are no other solutions better for every objective. Given the Pareto set, the GUIs provide different ways for a human to express intent by exploring tradeoffs between objectives; once a tradeoff is selected, the solution is chosen. The GUI designs are applied to interactive human-robot path-selection for a robot in an urban environment, but they can be applied to other tradeoff problems. A user study evaluates GUI designs by requiring users to select a tradeoff that satisfies a specified mission intent. Results of the user study suggest that GUIs designed to support an artist's palette-metaphor can be used to express intent without incurring unacceptable levels of human workload.
- J. A. Adams. Critical considerations for human-robot interface development. In Proceedings of 2002 AAAI Fall Symposium, pages 1--8, 2002.Google Scholar
- F. Ahmed and K. Deb. Multi-objective optimal path planning using elitist non-dominated sorting genetic algorithms. Soft Computing, 17(7):1283--1299, 2013. Google ScholarDigital Library
- F. S. Azar. Multiattribute decision-making: use of three scoring methods to compare the performance of imaging techniques for breast cancer detection. 2000.Google Scholar
- M. Baker, R. Casey, B. Keyes, and H. A. Yanco. Improved interfaces for human-robot interaction in urban search and rescue. In SMC (3), pages 2960--2965. Citeseer, 2004.Google Scholar
- K. B. Bennett, S. M. Posey, and L. G. Shattuck. Ecological interface design for military command and control. Journal of Cognitive Engineering and Decision Making, 2(4):349--385, 2008.Google ScholarCross Ref
- J. Bruce and M. Veloso. Real-time randomized path planning for robot navigation. In Intelligent Robots and Systems, 2002. IEEE/RSJ International Conference, volume 3, pages 2383--2388. IEEE, 2002.Google ScholarCross Ref
- A. Bry and N. Roy. Rapidly-exploring random belief trees for motion planning under uncertainty. In Robotics and Automation (ICRA), 2011 IEEE International Conference, pages 723--730. IEEE, 2011.Google ScholarCross Ref
- J. Y. Chen, E. C. Haas, and M. J. Barnes. Human performance issues and user interface design for teleoperated robots. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 37(6):1231--1245, 2007. Google ScholarDigital Library
- F. Driewer, M. Sauer, and K. Schilling. Discussion of challenges for user interfaces in human-robot teams. In EMCR. Citeseer, 2007.Google Scholar
- T. Gal, T. Stewart, and T. Hanne. Multicriteria decision making: Advances in MCDM models, algorithms, theory, and applications, volume 21. Springer Science & Business Media, 2013.Google Scholar
- M. A. Goodrich and A. C. Schultz. Human-robot interaction: A survey. Foundations and trends in human-computer interaction, 1(3):203--275, 2007. Google ScholarDigital Library
- D. S. Hall, L. G. Shattuck, and K. B. Bennett. Evaluation of an ecological interface design for military command and control. Journal of Cognitive Engineering and Decision Making, 6(2):165--193, 2012.Google ScholarCross Ref
- C.-L. Hwang and K. Yoon. Multiple attribute decision making: Methods and applications a state-of-the-art survey, volume 186. Springer Science & Business Media, 2012.Google Scholar
- M. W. Kadous, R. K.-M. Sheh, and C. Sammut. Effective user interface design for rescue robotics. In Proceedings of the 1st ACM SIGCHI/SIGART Conference on Human-robot interaction, pages 250--257. ACM, 2006. Google ScholarDigital Library
- L. E. Kavraki, P.vSvestka, J.-C. Latombe, and M. H. Overmars. Probabilistic roadmaps for path planning in high-dimensional configuration spaces. Robotics and Automation, IEEE Transactions on, 12(4):566--580, 1996.Google Scholar
- S. M. LaValle. Rapidly-exploring random trees: A new tool for path planning. 1998.Google Scholar
- K. Liu, D. Sakamoto, M. Inami, and T. Igarashi. Roboshop: multi-layered sketching interface for robot housework assignment and management. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 647--656. ACM, 2011. Google ScholarDigital Library
- D. Sakamoto, Y. Sugiura, M. Inami, and T. Igarashi. Graphical instruction for home robots. Computer, 49(7):20--25, 2016.Google ScholarCross Ref
- M. T. Shaikh, M. A. Goodrich, D. Yi, and J. Hoehne. Interactive multi-objective path planning through a palette-based user interface. In SPIE DefenseGoogle Scholar
- Security, pages 98370K--98370K. International Society for Optics and Photonics, 2016.Google Scholar
- O. M. Shir, S. Chen, D. Amid, D. Boaz, A. Anaby-Tavor, and D. Moor. Pareto optimization and tradeoff analysis applied to meta-learning of multiple simulation criteria. In Proceedings of the 2013 Winter Simulation Conference: Simulation: Making Decisions in a Complex World, pages 89--100. IEEE Press, 2013. Google ScholarDigital Library
- Y. Sugiura, D. Sakamoto, A. Withana, M. Inami, and T. Igarashi. Cooking with robots: designing a household system working in open environments. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 2427--2430. ACM, 2010. Google ScholarDigital Library
- A. B. Talone, E. Phillips, S. Ososky, and F. Jentsch. An evaluation of human mental models of tactical robot movement. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, volume 59, pages 1558--1562. SAGE Publications, 2015.Google ScholarCross Ref
- K. J. Vicente and J. Rasmussen. Ecological interface design: Theoretical foundations. Systems, Man and Cybernetics, IEEE Transactions on, 22(4):589--606, 1992.Google Scholar
- J. Wallenius and S. Zionts. Multiple criteria decision making: From early history to the 21st century. World Scientific, 2011.Google Scholar
- D. Yi, M. A. Goodrich, and K. D. Seppi. MORRF*: Sampling-based multi-objective motion planning. In Proceedings of the 24th International Conference on Artificial Intelligence, pages 1733--1739. AAAI Press, 2015. Google ScholarDigital Library
Index Terms
- Design and Evaluation of Adverb Palette: A GUI for Selecting Tradeoffs in Multi-objective Optimization Problems
Recommendations
Adverb Palette: GUI-based Support for Human Interaction in Multi-Objective Path-Planning
HRI '16: The Eleventh ACM/IEEE International Conference on Human Robot InteractionMany fields of study involve decision-making where trade-offs between different desirable outcomes are to be made. This paper introduces four designs for a graphical user interface (GUI) called the Adverb Palette (AP) to support interactive robot path-...
Fusion of Many-Objective Non-dominated Solutions Using Reference Points
EMO 2017: 9th International Conference on Evolutionary Multi-Criterion Optimization - Volume 10173With recent advancements of multi- or many-objective optimization algorithms, researchers and decision-makers are increasingly faced with the dilemma of choosing the best algorithm to solve their problems. In this paper, we propose a simple ...
The University of Alberta user interface management system
In this paper the design and implementation of the University of Alberta user interface management system (UIMS) is discussed. This UIMS is based on the Seeheim model of user interfaces, which divides the user interface into three separate components. ...
Comments