MALLET, a multi-agent logic language for encoding teamwork, is intended to enable expression of teamwork emulating human teamwork, allowing experimentation with different levels and forms of inferred team intelligence. A consequence of this goal is that the actual teamwork behavior is determined by the level of intelligence built into the underlying system as well as the semantics of the language. In this paper, we give the design objectives, the syntax, and an operational semantics for MALLET in terms of a transition system. We show how the semantics can be used to reason about the behaviors of team-based agents. The semantics can also be used to guide the implementation of various MALLET interpreters emulating different forms of team intelligence, as well as formally study the properties of team-based agents specified in MALLET. We have explored various forms of proactive information exchange behavior embodied in human teamwork using the CAST system, which implements a built-in MALLET interpreter.

This paper presents an approach to human-agent mixed teams for distributed team training, demonstrated through the integration of the domain independent multiagent architecture CAST with the military command-and-control simulation software DDD (distributed dynamic decision-making). We extend the CAST architecture in a way such that agents can replace any number of teammates on a DDD team. To capture the overall picture of teamwork, we explore the MALLET language in CAST for the specification of team structures and processes that involve both humans and agents.

Although there have been many prototypes of visualization in support of information retrieval, there has been little systematic evaluation that distinguishes the benefits of the visualization per se from that of various accompanying features.The current study focuses on such an evaluation of NIRVE, a tool that supports visualization of search results.Insofar as possible, functionally equivalent 3D, 2D, and text versions of NIRVE were implemented.Nine novices and six professional users completed a series of information-seeking tasks on a set of retrieved documents.There were high interface costs for the 3D visualization, although those costs decreased substantially with experience.Performance was best when the tool's properties matched task demands; only under the right combination of task, user, and interface did 3D visualization result in performance comparable to functionally matched 2D and textual tools.