Foundations of Human-Aware Planning

A Tale of Three Models

A Dissertation Presented by Tathagata Chakraborti in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy at Arizona State University, USA · November, 2018

Link to Dissertation

Abstract

A critical challenge in the design of AI systems that operate with humans in the loop is to be able to model the intentions and capabilities of the humans, as well as their beliefs and expectations of the AI system itself. This allows the AI system to be human-aware -- i.e. the human task model enables it to envisage desired roles of the human in joint action, while the human mental model allows it to anticipate how its own actions are perceived from the point of view of the human. In my research, I explore how these concepts of human-awareness manifest themselves in the scope of planning or sequential decision making with humans in the loop. To this end, I show:

  1. How an AI agent can leverage the human task model to generate symbiotic behavior;
  2. How the introduction of the human mental model in the deliberative process of the AI agent allows it to generate explanations for a plan or resort to explicable plans when explanations are not desired.
The latter is in addition to traditional notions of human-aware planning which typically use the human task model alone and thus enables a new suite of capabilities of a human-aware AI agent. Finally, I will explore how the AI agent can leverage emerging mixed-reality interfaces to realize effective channels of communication with the human in the loop.

November 2018
  • Runner-up for ICAPS 2019 Best Dissertation Award · 4/20/2019
Dissertation Supervisor
  • Subbarao Kambhampati (ASU)

Committee Members
  • Kartik Talamadupula (IBM)
  • Matthias Scheutz (Tufts)
  • Heni Ben Amor (ASU)
  • Yu Zhang (ASU)