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Dive into the research topics where Barry Brian Werger is active.

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Featured researches published by Barry Brian Werger.


distributed autonomous robotic systems | 2000

Broadcast of Local Eligibility for Multi-Target Observation

Barry Brian Werger; Maja J. Matarić

While various researchers have investigated group behavior of robots which are each controlled in a behavior-based manner, none have yet thoroughly investigated the possibilities of extending the port-arbitrated behavior (PAB) paradigm across networks of robots. We present an extension to the well-defined PAB techniques of behavioral interaction which provides standard abstractions for messaging, inhibition, and suppression over IP networks. The Broadcast of Local Eligibility is a general technique built from these abstractions that allows fully-distributed, flexible team coordination. We present a BLE approach to the CMOMMT multi-target observation problem, implemented on a team of physical robots.


Artificial Intelligence | 1999

Cooperation without deliberation: a minimal behavior-based approach to multi-robot teams

Barry Brian Werger

While terminology and some concepts of behavior-based robotics have become widespread, the central ideas are often lost as researchers try to scale behavior to higher levels of complexity. “Hybrid systems” with model-based strategies that plan in terms of behaviors rather than simple actions have become common for higher-level behavior. We claim that a strict behavior-based approach can scale to higher levels of complexity than many robotics researchers assume, and that the resulting systems are in many cases more efficient and robust than those that rely on “classical AI” deliberative approaches. Our focus is on systems of cooperative autonomous robots in dynamic environments. We will discuss both claims that deliberation and explicit communication are necessary to cooperation and systems that cooperate only through environmental interaction. In this context we introduce three design principles for complex cooperative behavior—minimalism, statelessness and tolerance—and present a RoboCup soccer system that matches the sophistication of many deliberative soccer systems while exceeding their robustness, through the use of strict behavior-based techniques with no explicit communication.


robot soccer world cup | 1998

The Spirit of Bolivia: Complex Behavior Through Minimal Control

Barry Brian Werger; Pablo Funes; Miguel Schneider Fontan; Randy Sargent; Carl Witty; Tim Witty

The “Spirit of Bolivia” is a robotic soccer team which demonstrates minimally comprehensive team behavior. By this we mean that each member of the team makes progress towards team goals, and obstructs progress of the opponent, by interacting constructively with team-mates and in a sportsmanlike manner with opposing players. This complex behavior is achieved with simple on-board processors running very small behavior-based control programs; team behaviors are achieved without explicit communication. Externalization — the use of the environment as its own best model — and tolerance — a bias towards reducing the need for accurate information rather than attempting to recognize or correct noisy information — are the keys to robustness and sophistication of team behavior.


adaptive agents and multi-agents systems | 2000

Broadcast of local eligibility: behavior-based control for strongly cooperative robot teams

Barry Brian Werger; Maja J. Matarić


Archive | 1999

Exploiting Embodiment in Multi-Robot Teams

Barry Brian Werger; Maja J. Matarić


Archive | 2000

Distributed Autonomous Robotic Systems 4

Barry Brian Werger; Maja J. Matarić


national conference on artificial intelligence | 1997

Multiple Agents From the Bottom Up

Barry Brian Werger


Encyclopedia of Cognitive Science | 2006

Cognitive Processing through the Interaction of Many Agents

Chris V. Jones; Maja J. Matarić; Barry Brian Werger


distributed autonomous robotic systems | 2000

Ayllu: Distributed Port-attributed Behavior-based Control.

Barry Brian Werger


international joint conference on artificial intelligence | 1997

Quick'n'Dirty Generalization for Mobile Robot Learning

Barry Brian Werger; Maja J. Matarić

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Maja J. Matarić

University of Southern California

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Chris V. Jones

University of Southern California

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Randy Sargent

Carnegie Mellon University

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