Romeo Sanchez

VizScript: on the creation of efficient visualizations for understanding complex multi-agent systems

One of the most difficult tasks in software development is understanding the behavior of the final product. Making sure that a system behaves as users expect is a challenging endeavor. Understanding the behavior of a multi-agent system is even more challenging given the additional complexities of multi-agent problems. In this paper, we address the problem of users creating visualizations to debug and understand complex multi-agent systems. We introduce VizScript, a generic framework that expedites the process of creating such visualizations. VizScript combines a generic application instrumentation, a knowledge-base, and simple scene definitions primitives with a reasoning system, to produce an easy to use visualization platform. Using VizScript, we were able to recreate the visualizations for a complex multiagent system with an order-of-magnitude less effort than was required in a Java implementation

VizScript: visualizing complex interactions in multi-agent systems

We address the problem of users creating visualizations to debug and understand multi-agent systems. The key challenges are that (1) needs arise dynamically, i.e., it is difficult to know a priori what visualizations one wants, (2) extensive expertise on the system, the algorithms and visualization tools are often needed for implementation, and (3) agents can be running in a distributed environment. We have developed VizScript, a collection of tools to expedite the process of creating visualizations. VizScript combines a generic application instrumentation, a knowledge base, and simple scene definition primitives with a reasoning system, to produce an easy to use visualization system. Using VizScript we were able to recreate the visualizations for a complex multi-agent system with an order-of-magnitude less effort than was required in a Java implementation.

Distributed coordination in uncertain multiagent systems

We consider real-time multi-agent coordination in a dynamic and uncertain domain addressing both distributed state information and partial knowledge of the common reward function. The challenge is to find functional strategies when bounded rationality hinders the ability to encompass the values of possible sample paths of the system. This paper discusses a new approach based on assigning agents to monitor portions of the reward structure for which they aggregate and propagate appropriate profiles which compactly represent relevant information used for policy modification. This approach shows promise as an alternate and potentially superior technique with respect to current decision-theoretic and scheduling approaches.

CSC: Criticality-Sensitive Coordination

Our Criticality-Sensitive Coordination (CSC) agents are designed to enhance the performance of a human-team working together in uncertain and dynamic settings by monitoring and adapting their plans as dictated by the evolution of the environment. Such situations model military scenarios such as a coordinated joint operations or enterprise settings such as multiple-project management. Among the many challenges in these situations are the large space of possible states due to uncertainty, the distributed / partial knowledge of current state and plan among the agents and the need to react in a timely manner to events that may not be in the original model. In fact, reaction alone is often insufficient as in environments where success depends on completing sequences of coupled actions, one needs to anticipate future difficulties and enable contingencies to alleviate potential hazards.

Interfaces for team coordination

Coordinators are intelligent software agents that help humans to collaborate and coordinate execution of multiple activities in dynamic, distributed and uncertain domains. One of the main challenges is the generation of effective user interfaces for team coordination. Intelligent interfaces that avoid information overload, and facilitate user interactions to repair mission plans are needed, and they are the focus of this work.