Carnegie Mellon University
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Control of Agent-Based Systems (CoABS)
This project is no longer active.
Head: Manuela Veloso
Contact: Manuela Veloso
Mailing address:
Carnegie Mellon University
Robotics Institute
5000 Forbes Avenue
Pittsburgh, PA 15213
Associated lab(s) / group(s):
 MultiRobot Lab
Project Homepage
The main focus of our work is the development of teams of intelligent agents that are capable of acting autonomously and collaborating in environments with limited communication, while working towards achieving concrete team objectives. We will demonstrate our approaches and technology in applications of relevance to DARPA, in particular command and control missions by special forces.

We envision teams of intelligent command and control agents with different skills. Teams will be constituted by different types of agents viewed as several subsets of homogeneous agents. Agents in different subsets have different skills. Agents will refine specified objectives, decompose the overall task according to their skills, organize themselves in order to enable collaboration, and learn to collaborate towards the most effective achievement of the team objectives.

The envisioned main integral part of our teams of intelligent agents consists of a pre-agreement on the task decomposition to organize the subteams of homogeneous agents and the collaboration during the autonomous task achievement. Agents will be equipped with techniques for run-time evaluation of the situation to decide between collaborating with other agents achieving the task individually. Our research will build upon the following main directions:

Development of a team of skilled individual agents capable of team strategic reasoning. Our work is focused in domains in which agents in a team alternate between periods of very low and very high communication. This leads into our novel introduction of the concept of "Periodic Team Synchronization" (PTS) domains. Agents will have an opportunity to form jointly team and individual plans, which will then be carried out autonomously by each agent.

A model of communication between agents in environments with unreliable, high-cost communication. In most multiagent systems with communicating agents, the agents have the luxury of using reliable, multi-step negotiation protocols. Conversely, we will develop a model of communication for multiagent environments with unreliable, high-cost communication.

A flexible collaboration model towards an effective overall team behavior. Collaboration between agents will be achieved through a flexible role-based approach by which the task space is decomposed and the agents are assigned subtasks. Agents will be capable of real-time evaluation and deliberation in order to select between alternative pre-compiled contingency plans.

Development of individual and team adaptive capabilities through layered learning. We research layered learning as an approach to complex multiagent domains that involves incorporating low-level learned behaviors into higher-level behaviors.

Our proposed work builds strongly upon our research work over the last few years. We have had research results of significant impact demonstrating the effectiveness of planning, execution, and learning for continuous asynchronous objectives, and for building teams of multiple intelligent agents in a simulated dynamic adversarial environment.

We expect that by leveraging and extending our current work, our research will have a considerable impact on the performance of military command and control.