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

Speaker: Rick Freedman , University of Massachusetts Amherst

Date: Thursday, January 18, 2018

Time: 3:00 PM to 4:00 PM Note: all times are in the Eastern Time Zone

Public: Yes

Location: Seminar Room G882 (Hewlett Room)

Event Type: Seminar

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Contact: Julie A Shah,

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Reminder Subject: TALK: Integrating Recognition and Decision Making for Autonomous Systems to Close the Interaction Loop

Interactive Robotics Group Seminar

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.

Richard (Rick) Freedman is a Ph.D. Candidate in the College of Information and Computer Sciences at the University of Massachusetts Amherst, a NSF EAPSI/JSPS Summer Fellow at the University of Tokyo for the summer of 2015, and an EAAI New and Future AI Educator Program Award Recipient. His research interests lie at the intersection of various areas including: artificial intelligence planning; plan, activity, and intent recognition; human-computer/robot interaction; topic modeling; knowledge representation; and statistical-relational methods. He uses interdisciplinary approaches to develop systems that adaptively interact with human users through understanding their actions in the environment. Rick is also a lead organizer of the ICAPS Workshops on User Interfaces and Scheduling and Planning and a member of the 2016 organizing committee for the Artificial Intelligence for Human-Robot Interaction Symposium. In addition to research, he supports developing science, technology, engineering, and mathematics (STEM) activities for K-12 students and providing undergraduate students with opportunities to participate in research activities. As part of these efforts, he is developing a team-based learning variation of the introduction to artificial intelligence course.

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Created by Julie A Shah Email at Monday, January 15, 2018 at 4:57 PM.