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The wireless intelligent monitoring and analysis systems is a proof-of-concept directed at discovering solution(s) for providing decentralized intelligent data analysis and control for distributed containers equipped with wireless sensing units. The objective was to embed smart behavior directly within each wireless sensor container, through the incorporation of agent technology into each sensor suite. This approach provides intelligent directed fusion of data based on a social model of teaming behavior. This system demonstrates intelligent sensor behavior that converts raw sensor data into group knowledge to better understand the integrity of the complete container environment. The emergent team behavior is achieved with lightweight software agents that analyze sensor data based on their current behavior mode. When the system starts-up or is reconfigured the agents self-organize into virtual random teams based on the leader/member/lonely paradigm. The team leader collects sensor data from their members and investigates all abnormal situations to determine the legitimacy of high sensor readings. The team leaders flag critical situation and report this knowledge back to the user via a collection of base stations. This research provides insight into the integration issues and concerns associated with integrating multi-disciplinary fields of software agents, artificial life and autonomous sensor behavior into a complete system.

This article discusses how mobile agents and scripting technology can play an important role in first-responder training simulations. From a training perspective, the power of mobile agents can greatly improve the training experience if they are easy to use and integrate and have the necessary power. Certain features should be available to mobile agents in applications such as this. As opposed to traditional mobile agent applications, supplementing a real-time training system with a mobile agent system requires many features so that agents integrate appropriately with the running application. Mobile agents with the ability to interface with the running binary application and with the features discussed in this paper can be very effective and valuable. This paper looks at the features that are necessary. We implement these features into a mobile agent system, Mobile-FIRST, and then examine its use in a first responder training video game currently in production. Using a system with these principles, integration of mobile agents becomes simple and intuitive, and can greatly improve the application in many different ways.

This LDRD sought to develop technology that enhances scenario construction speed, entity behavior robustness, and scalability in Live-Virtual-Constructive (LVC) simulation. We investigated issues in both simulation architecture and behavior modeling. We developed pathplanning technology that improves the ability to express intent in the planning task while still permitting an efficient search algorithm. An LVC simulation demonstrated how this enables "one-click" layout of squad tactical paths, as well as dynamic re-planning for simulated squads and for real and simulated mobile robots. We identified human response latencies that can be exploited in parallel/distributed architectures. We did an experimental study to determine where parallelization would be productive in Umbra-based FOF simulations. We developed and implemented a data-driven simulation composition approach that solves entity class hierarchy issues and supports assurance of simulation fairness. Finally, we proposed a flexible framework to enable integration of multiple behavior modeling components that model working memory phenomena with different degrees of sophistication. 4 ACKNOWLEDGMENTSThe authors would like to thank the following individuals for their contributions to the project: