Enabling Robots to Communicate their Objectives

The overarching goal of this work is to efficiently enable end-users to correctly anticipate a robot's behavior in novel situations. Since a robot's behavior is often a direct result of its underlying objective function, our insight is that end-users need to have an accurate mental model of this objective function in order to understand and predict what the robot will do. While people naturally develop such a mental model over time through observing the robot act, this familiarization process may be lengthy. Our approach reduces this time by having the robot model how people infer objectives from observed behavior, and then it selects those behaviors that are maximally informative. The problem of computing a posterior over objectives from observed behavior is known as Inverse Reinforcement Learning (IRL), and has been applied to robots learning human objectives. We consider the problem where the roles of human and robot are swapped. Our main contribution is to recognize that unlike robots, humans will not be exact in their IRL inference. We thus introduce two factors to define candidate approximate-inference models for human learning in this setting, and analyze them in a user study in the autonomous driving domain. We show that certain approximate-inference models lead to the robot generating example behaviors that better enable users to anticipate what it will do in novel situations. Our results also suggest, however, that additional research is needed in modeling how humans extrapolate from examples of robot behavior.Comment: RSS 201

How should the Fed communicate?

Presentation to the "The Future of the Federal Reserve", Center for Economic Policy Studies (CEPS), Princeton University, Princeton , N.J., April 02, 2005Banks and banking, Central

How should central banks communicate?

The paper shows that central bank communication is a key determinant of the market’s ability to anticipate monetary policy decisions and the future path of interest rates. Comparing communication policies by the Federal Reserve, the Bank of England and the ECB since 1999, we find that communicating the diversity of views among committee members about monetary policy lowers the market’s ability to anticipate policy decisions as well as the future path of interest rates. This effect is sizeable, accounting for instance for one third to half of the prediction errors of FOMC policy decisions. By contrast, individualistic communication regarding the economic outlook is found to be beneficial for the Federal Reserve, enabling market participants to better anticipate the future path of interest rates. Thus, it is the collegiality of views on monetary policy but the diversity of views on the economic outlook that enhance the effectiveness of central bank communication. JEL Classification: E43, E52, E58, G12Bank of England, committee, communication, economic outlook, effectiveness, European Central Bank, Federal Reserve, monetary policy

Might EPR particles communicate through a wormhole?

We consider the two-particle wave function of an Einstein-Podolsky-Rosen system, given by a two dimensional relativistic scalar field model. The Bohm-de Broglie interpretation is applied and the quantum potential is viewed as modifying the Minkowski geometry. In this way an effective metric, which is analogous to a black hole metric in some limited region, is obtained in one case and a particular metric with singularities appears in the other case, opening the possibility, following Holland, of interpreting the EPR correlations as being originated by an effective wormhole geometry, through which the physical signals can propagate.Comment: Corrected version, to appears in EP

Leadership Doctorates Newsletter: Volume 6, Number 1 (Special Issue)

In this Issue: Community Wicked Problem Jefferson Containing System Leadership Doctorates Strategic Approach Continuation of Learning Your Stakeholder Contributions Attending Class Communicate, Communicate, Communicate Going Forward Leading Idea

Agreeing to Cross: How Drivers and Pedestrians Communicate

The contribution of this paper is twofold. The first is a novel dataset for studying behaviors of traffic participants while crossing. Our dataset contains more than 650 samples of pedestrian behaviors in various street configurations and weather conditions. These examples were selected from approx. 240 hours of driving in the city, suburban and urban roads. The second contribution is an analysis of our data from the point of view of joint attention. We identify what types of non-verbal communication cues road users use at the point of crossing, their responses, and under what circumstances the crossing event takes place. It was found that in more than 90% of the cases pedestrians gaze at the approaching cars prior to crossing in non-signalized crosswalks. The crossing action, however, depends on additional factors such as time to collision (TTC), explicit driver's reaction or structure of the crosswalk.Comment: 6 pages, 6 figure

Decentralization of Multiagent Policies by Learning What to Communicate

Effective communication is required for teams of robots to solve sophisticated collaborative tasks. In practice it is typical for both the encoding and semantics of communication to be manually defined by an expert; this is true regardless of whether the behaviors themselves are bespoke, optimization based, or learned. We present an agent architecture and training methodology using neural networks to learn task-oriented communication semantics based on the example of a communication-unaware expert policy. A perimeter defense game illustrates the system's ability to handle dynamically changing numbers of agents and its graceful degradation in performance as communication constraints are tightened or the expert's observability assumptions are broken.Comment: 7 page