Integrating Recognition and Decision Making for Autonomous Systems to Close the Interaction Loop

06 Dec
Wednesday, 12/06/2017 9:00am to 11:00am
Computer Science Building, Room 151
Ph.D. Dissertation Proposal Defense
Speaker: Rick Freedman

"Integrating Recognition and Decision Making for Autonomous Systems to Close the Interaction Loop"

Intelligent systems are becoming increasingly ubiquitous in daily life.  Mobile devices are providing machine-generated support to users, robots are "coming out of their cages" in manufacturing to interact with co-workers, and cars with various degrees of self-driving capabilities operate amongst pedestrians and the driver.  However, these interactive intelligent systems' effectiveness depends on their understanding and recognition of human activities and goals, as well as their responses to people in a timely manner.  The average person does not follow instructions step-by-step or act in a formulaic manner, but instead varies the order of actions and timing when performing a given task.  People explore their surroundings, make mistakes, and may interrupt an activity to handle more urgent matters.  The decisions that an autonomous intelligent system makes should account for such noise and variance regardless of the form of interaction, which includes adapting action choices and possibly its own goals.

While most people take these aspects of interaction for granted, they are complex and involve many specific tasks that have primarily been studied independently within artificial intelligence.  This results in open-loop interaction experiences where the user must perform a fixed input command or the intelligent system performs a hard-coded output response - one of the components of the interaction cannot adapt with respect to the other for longer-term back-and-forth interactions.  We will analyze what has been accomplished in each of the areas of plan recognition, activity recognition, intent recognition, and autonomous planning; then we will explore how these developments can work together to develop more adaptive interactive experiences between autonomous intelligent systems and the people around them.  The proposed framework and approaches will serve as a preliminary step that explains how we may begin to address the problem of closing the interaction loop using what is currently available in addition to new questions that need to be considered.

Advisor: Shlomo Zilberstein