Teambotica Architecture

The SRI UAV robot architecture is based on several years of research at SRI into intelligent reactive control, planning, negotiation, and robot motion control [1,2,3,4,5]. It is similar to systems like SAFER [6] and SRTA [7] in its ability to deal with multiple goals at once and evaluate when to discard goals. Figure 9 shows our Multi-Level Agent Adaptation (MLAA) architecture. Clearly, monitoring is pervasive and serves each layer in the architecture as well as the user (not shown).

Figure 9: Multi-level Agent Adaptation Architecture.
\begin{figure}\centerline{\includegraphics[bb=36 36 756 576,width=5.5in] {psfiles/UCAV-arch.eps}}\end{figure}

The coordination module receives goal requests from the human commander or other agents. The agent participates in a negotiation process to determine its role in achieving the goal. During negotiation, the agent consults the strategic planner to create a plan, or plan segment (referred to as a recipe), and assess the recipe's viability given current commitments. If the negotiation process results in the goal and its recipe being accepted, the EA Manager (see Figure 3) instantiates the recipe and initiates its execution. The Plan Initializer also creates monitoring sentinels for use by the EA to detect deviation from the recipe during execution. The execution of a recipe involves activation of tasks that must be blended with other active tasks to maximize the satisfaction of multiple goals. For example, if the robot needs to reach a waypoint by a set time, take a picture of a location nearby, and also remain concealed, the task blender modifies the path planner at runtime to achieve all three tasks. Finally, the lowest layer in the architecture is the interface between the tasking architecture and the physical, or simulated, robot controller.


D. E. Wilkins and K. L. Myers.
A common knowledge representation for plan generation and reactive execution.
Journal of Logic and Computation, 5(6):731-761, December 1995.

K. L. Myers.
A procedural knowledge approach to task-level control.
In Proc. of the 1996 International Conference on AI Planning Systems. AAAI Press, Menlo Park, CA, 1996.

D. E. Wilkins and K. L. Myers.
A multiagent planning architecture.
In Proc. of the 1998 International Conference on AI Planning Systems, pages 154-162, Pittsburgh, PA, 1998.

A. Cheyer and D. Martin.
The open agent architecture.
Journal of Autonomous Agents and Multi-Agent Systems, 4(1):143-148, 2001.

K. Konolige and K. Myers.
Artificial Intelligence Based Mobile Robots: Case studies of Successful Robot Systems, chapter The Saphira architecture: a design for autonomy.
MIT Press, 1998.

G. Holness, S. Karuppiah, D.and Uppala, and S. C. Ravela.
A service paradigm for reconfigurable agents.
In Proc. of the 2nd Workshop on Infrastructure for Agents, MAS, and Scalable MAS (Agents 2001), Montreal, Canada, 2001.

R. Vincent, B. Horling, V. Lesser, and T. Wagner.
Implementing soft real-time agent control.
In Proceedings of the 5th International Conference on Autonomous Agents. ACM Press, 2001.