Humans have difficulty evaluating the effects of uncertainty on schedules. People often mitigate the effects of uncertainty by adding slack based on experience and non-stochastic analyses such as the critical path method (CPM). This is costly as it leads to longer than necessary schedules, and can be ineffective without a clear understanding of where slack is needed. COMPASS is an interactive real-time tool that analyzes schedule uncertainty for a stochastic task network. An important feature is that it concurrently calculates stochastic critical paths and critical tasks. COMPASS visualizes this information on top of a traditional Gantt view, giving users insight into how delays caused by uncertain durations propagate down the schedule. Evaluations with 10 users show that users can use COMPASS to answer a variety of questions about the possible evolutions of a schedule (e.g., what is the likelihood that all activities will complete before a given date?)

This paper presents a bio-inspired, distributed control algorithm calledTENTACLESfor a group of radio robots to move, self-configure and maintain communication between some critical entities (such as humans, command centers, or other systems) in an unknown environment. The basic idea is to direct robots' explorative movements to grow ¿tentacles¿ from entities and establish links when tentacles meet. This approach can self-heal failures of robots and improve communication coverage and quality over time. Experiments in simulations and real robots have shown positive results.

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

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.

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.

We address the problem of devising efficient decentralized allocation mechanisms for a divisible resource, which is critical to many technological domains such as traffic management on the Internet and bandwidth allocation to agents in ad hoc wireless networks. We introduce a class of efficient signal proportional allocation (ESPA) mechanisms that yields an allocation which maximizes social welfare with minimal signaling and computational requirements for the resource. Revenue limits for this class are obtained and a sequence of schemes that approach these limits arbitrarily closely are given. We also present a locally stable negotiation scheme applicable to the entire class and illustrate efficiency and revenue properties through simulation.