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Carnegie Mellon University Robotics Institute Research Guide

Carnegie Mellon University, Robotics Institute, Research Guide

Multi-Agent Systems

Research in the area of multi-agent and multi-robot systems is concerned with the effective coordination of autonomous agents to perform tasks so as to achieve high quality overall system performance. Multi-agent coordination challenges include the lack of single point of control, local views of each agent that provide only incomplete information, private goals and solution procedures of the agents, communication asynchrony, dynamic environments and uncertainty. Coordination regimes include teamwork, where the agents are cooperative and coordinate to achieve a global team goal, or regimes where the agents are self-interested and try to achieve individual goals. An extreme case is where agents are adversaries and try to fulfill their individual goals even while inflicting costs/harm on one another. Over the past 10 years, research at RI has encompassed all these types of coordination.

In teamwork settings, UAVs and UGVs collaborate to collect and fuse information for increased situational awareness (Sycara, Scerri), use market-based algorithms for task allocation for space application (Dias, Stentz), such as Mars rover exploration.

Other applications include using UAVs with different types of sensors to find ground vehicles that emit RF signals (Scerri, Sycara) in the least amount of time, and research in coordinated path planning so as to find safe paths with minimal cost that respect environmental constraints (Likhachev), or optimize the detection of victims in urban search and rescue (Sycara, Scerri). Additionally, applications such as robotic assembly (Singh, Simmons), and Oil and Gas extraction/refinement/transportation (Browning, Dias, Schempf) provide additional challenges for teamwork coordination:

When the agents are self interested and economically rational, trying to maximize their individual goals/utilities, automated negotiation techniques have been developed (Sycara) for resource allocation, and also for formation of optimal teams (coalitions) that consider the agents' task-related abilities and utilities (Sycara). In adversarial environments, effective algorithms and applications have been developed in domains such as robotic soccer (Veloso), pursuit evasion (Singh, Sycara), or in agent societies where different groups in the society may be in conflict with one another and where we are interested in understanding the spatio-temporal characteristics of such conflicts (Sycara).

Besides the work on applications, extensive research is being done on theoretical work on multi-agent coordination and control (Likhachev, Rubinstein, Scerri, Simmons, Smith, Sycara, Veloso) as well as multi-agent learning (Gordon, Simmons, Sycara, Veloso). Additional theoretical contributions have been made in multi-agent coordination in open environments where heterogeneous agents with different task performance capabilities can enter and exit dynamically. In such environments, algorithms have been developed for agent discovery, selection and coordination (Scerri, Sycara). Additionally, research has been concerned with coordination architectures, e.g. hierarchical vs. peer-to-peer interactions, as well as the level of individual agent sophistication and intelligence ranging from large numbers of simple agents that interact through evolutionary algorithms to sophisticated intelligent agents (Simmons, Smith, Sycara, Veloso).

Another dimension of challenges in multi-agent coordination is scaling up the number of coordinating agents and the associated algorithms for information sharing, task allocation and planning. To this end, research in RI has developed innovative algorithms for understanding the emergent dynamics of information sharing in very large agent networks with different topologies comprised of thousands of agents (Scerri, Sycara), efficient algorithms for scaling path planning (Likhachev, Scerri, Sycara) and algorithms for adversarial games in graphs (Sycara).

The research on multi-agent systems provides foundations for research in human and robot teams and Human Robot Interaction and Human Computer Interaction (Dolan, Scerri, Simmons, Steinfeld, Sycara). A significant share of multi-agent systems research within the RI is conducted in collaboration with other units of SCS and the rest of the university. For example, faculty members supervise students and collaborate with faculty in CSD (Sandholm, Harhold-Bachler) and others (Smith, Sycara) supervise students and collaborate with faculty in the Tepper School of Business.

Continue Reading: Planning & Scheduling


Faculty

  1. Brett
    Browning

  2. Bernardine
    Dias

  3. John
    Dolan

  4. Geoffrey
    Gordon

  5. Maxim
    Likhachev

  6. Zack
    Rubinstein

  7. Paul
    Scerri

  8. Reid
    Simmons

  9. Sanjiv
    Singh

  10. Stephen
    Smith

  11. Aaron
    Steinfeld

  12. Katia
    Sycara

  13. Manuela
    Veloso


Project Images

  • Real and Simulated Test Beds

  • Planning via Graph-Searches

  • Market-based Motion Planning

  • Multirobot Reliability

  • Search-and-Rescue Planning