RADAR - A Proactive Decision Support system for human-in-the-loop planning

Sailik Sengupta, Tathagata Chakraborti, Sarath Sreedharan, Satya Gautam Vadlamudi, Subbarao Kambhampati

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

Proactive Decision Support (PDS) aims at improving the decision making experience of human decision makers by enhancing both the quality of the decisions and the ease of making them. In this paper, we ask the question what role automated decision making technologies can play in the deliberative process of the human decision maker. Specifically, we focus on expert humans in the loop who now share a detailed, if not complete, model of the domain with the assistant, but may still be unable to compute plans due to cognitive overload. To this end, we propose a PDS framework RADAR based on research in the automated planning community that aids the human decision maker in constructing plans. We will situate our discussion on principles of interface design laid out in the literature on the degrees of automation and its effect on the collaborative decision making process. Also, at the heart of our design is the principle of naturalistic decision making which has been shown to be a necessary requirement of such systems, thus focusing more on providing suggestions rather than enforcing decisions and executing actions. We will demonstrate the different properties of such a system through examples in a fire-fighting domain, where human commanders are involved in building response strategies to mitigate a fire outbreak. The paper is written to serve both as a position paper by motivating requirements of an effective proactive decision support system, and also an emerging application of these ideas in the context of the role of an automated planner in human decision making, in a platform that can prove to be a valuable test bed for research on the same.

Original languageEnglish (US)
Title of host publicationFS-17-01
Subtitle of host publicationArtificial Intelligence for Human-Robot Interaction; FS-17-02: Cognitive Assistance in Government and Public Sector Applications; FS-17-03: Deep Models and Artificial Intelligence for Military Applications: Potentials, Theories, Practices, Tools and Risks; FS-17-04: Human-Agent Groups: Studies, Algorithms and Challenges; FS-17-05: A Standard Model of the Mind
PublisherAI Access Foundation
Pages269-276
Number of pages8
VolumeFS-17-01 - FS-17-05
ISBN (Electronic)9781577357940
StatePublished - Jan 1 2017
Event2017 AAAI Fall Symposium - Arlington, United States
Duration: Nov 9 2017Nov 11 2017

Other

Other2017 AAAI Fall Symposium
CountryUnited States
CityArlington
Period11/9/1711/11/17

ASJC Scopus subject areas

  • Engineering(all)

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    Sengupta, S., Chakraborti, T., Sreedharan, S., Vadlamudi, S. G., & Kambhampati, S. (2017). RADAR - A Proactive Decision Support system for human-in-the-loop planning. In FS-17-01: Artificial Intelligence for Human-Robot Interaction; FS-17-02: Cognitive Assistance in Government and Public Sector Applications; FS-17-03: Deep Models and Artificial Intelligence for Military Applications: Potentials, Theories, Practices, Tools and Risks; FS-17-04: Human-Agent Groups: Studies, Algorithms and Challenges; FS-17-05: A Standard Model of the Mind (Vol. FS-17-01 - FS-17-05, pp. 269-276). AI Access Foundation.