How Can We Trust a Robot?

Speaker: Benjamin Kuipers

Date: Monday, January 30, 2017

Time: 11:00 AM to 12:00 PM Note: all times are in the Eastern Time Zone

Public: Yes

Location: Seminar Room D463 (Star)

Event Type:

Room Description:

Host: Nick Roy

Contact: Nick Roy,

Relevant URL:

Speaker URL: None

Speaker Photo:

Reminders to:,

Reminder Subject: TALK: How Can We Trust a Robot?

Intelligent robots may increasingly participate in our society, driving on our roads, caring for our children and our elderly, and in many other ways. Can we trust them?

Society depends on cooperation, which requires partners to trust each other, and to refrain from exploiting the vulnerability trust entails. Straight-forward applications of game theory, where each partner acts to maximize his own expected reward, often lead to very poor outcomes. Societies impose social norms to lead individuals away from attractive selfish decisions, toward better outcomes for everyone. Robots will need to explicitly establish their trustworthiness as potential collaborative partners by following the social norms of society.

Philosophical theories of ethics (utilitarianism, deontology, virtue ethics) provide useful ideas toward designing robots that can follow social norms, but no one theory is adequate. A hybrid architecture combining multiple methods at different time-scales will be required to ensure that a robot behaves well in the complex human physical and social environment.

I conclude by applying these ideas to the Deadly Dilemma, a frequently-raised challenge for intelligent robots acting as self-driving cars.

Bio sketch

Benjamin Kuipers is a Professor of Computer Science and Engineering at the University of Michigan. He was previously a Professor of Computer Sciences at the University of Texas at Austin. He received his B.A. from Swarthmore College, and his Ph.D. from MIT. He served as Department Chair at UT Austin, and is a Fellow of AAAI, IEEE, and AAAS. He investigates the representation of commonsense and expert knowledge, with particular emphasis on the effective use of incomplete knowledge. His research accomplishments include developing the QSIM algorithm for qualitative simulation, the Spatial Semantic Hierarchy models of knowledge for robot exploration and mapping, and methods whereby an agent without prior knowledge of its sensors, effectors, or environment can learn its own sensorimotor structure, the spatial structure of its environment, and its own object and action abstractions for higher-level interactions with its world.

Research Areas:

Impact Areas:

See other events that are part of the Robotics@MIT Seminar Series 2016.

Created by Nick Roy Email at Wednesday, January 25, 2017 at 9:58 AM.