Gal Kaminka: Research

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We live in exciting times. For the first time in history, humanity has reached a technological level that allows it to systematically and empirically explore one of the most fundamental questions of science: The nature of the intelligent mind. While philosophers have theorized about this question for thousands of years, this century is the first in which technological achievement is allowing us to now explore it empirically. Artificial Intelligence, the scientific discipline which investigates Intelligence, is slowly but surely emerging as an exact, rigorous scientific field, much like physics, chemistry, and biology have made this transition in history.

Research Overview

I am interested in the study of Intelligence, a naturally-occurring phenomenon that can be synthesized and replicated (with varying degrees of success) using computational means. My focus is on social intelligence. I want to understand the transition from a single mind, to many. To build robots that are socially-intelligent; that are able to reason about, manipulate, collaborate with, and coordinate with other robots and humans. To build computational models that explain social intelligence, that allow replication of it, that facilitate predictions of its occurrence, and that enable measurement and quantification.

Almost all robots are autistic; very few humans are. And the best evidence we have so far, in both robotics and psychology, suggests that this gap is a computational challenge. Even given the right mechanics and electronics, robots cannot interact effectively with others, unless their computational brains are made to think in a particular way.

Since 1995, my research in multi-agent and multi-robot systems has had two complementary themes: The control of teams of robots and agents, and its complementary monitoring by the agents themselves. The two are inseparable: One cannot build an effective controller without having a feedback mechanism that monitors the results of the controller's actions. In multi-robot systems, this translates to endowing robots with the ability to take social actions, and to understand those of its peers. For instance, a socially-intelligent robot must know when and what to communicate; and it must understand the intents of others from observations of their actions.

The instantiation of these social control and social monitoring themes takes me back and forth through theory and practice, from proofs of algorithmic complexity and logical models, to their use in robots that move in my lab--and outside of it. And from the lessons learned with real robots, back to amending the theory. My research thus explores a variety of tasks, including detecting and diagnosing coordination failures, multi-robot area coverage, search, and coordinated movement, automated coordination and task allocation, understanding group behavior and the differences in distributed and centralized settings. In all of these, the two themes--control and monitoring--are integrated to create robust and effective social behavior.

Understanding What Others are Doing

One particular aspect of intelligent behavior involves the critical ability of an agent to reason about other agents from observations of their actions (or communications). Based on such observations, a robot must be able to infer their intent and recognize their behavior. There are several inherent challenges to such recognition, in multi-robot tracking. Since 1995, I have been investigating efficient symbolic and probabilistic algorithms for inferring intents, plans, beliefs, and behaviors, in single-robot and multi-robot settings. I've also been doing research about using the inferred information to detect, identify, and diagnose faults in the coordination of agents and robots. Here are a few highlighted contributions, with colleagues. Please check my publications page: Additional information can be found on my agent modeling web page.

Planning and Acting in a Social Context

I have been conducting empirical and theoretical research in several real-world, challenging application domains, in which multiple robots or agents interact. The emphasis of the work has been on finding principles and techniques that generalize across domains. My approach to this research is to go back and forth between applications and general architectures that work across domains (please also see my publications page):