The innovative reapplication of a multiagent system for human-in-the-loop (HIL) simulation was a consequence of appropriate agent-oriented design. The use of intelligent agents for simulating human decision making offers the potential for analysis and design methodologies that do not distinguish between agent and human until implementation. With this as a driver in the design process, the construction of systems in which humans and agents can be interchanged is simplified. Two systems have been constructed and deployed to provide defense analysts with the tools required to advise and assist the Australian Defense Force in the conduct of maritime surveillance and patrol. The experiences gained from this process indicate that it is simpler, both in design and implementation, to add humans to a system designed for intelligent agents than it is to add intelligent agents to a system designed for humans.

Toward that end, this article presents PROTECT, a game-theoretic system deployed by the United States Coast Guard (USCG) in the Port of Boston for scheduling its patrols. USCG has termed the deployment of PROTECT in Boston a success; PROTECT is currently being tested in the Port of New York, with the potential for nationwide deployment. PROTECT is premised on an attackerdefender Stackelberg game model and offers five key innovations. First, this system is a departure from the assumption of perfect adversary rationality noted in previous work, relying instead on a quantal response (QR) model of the adversary's behavior -- to the best of our knowledge, this is the first real-world deployment of the QR model. Second, to improve PROTECT's efficiency, we generate a compact representation of the defender's strategy space, exploiting equivalence and dominance.

Instead, patrol units move about the transit system, inspecting the tickets of passengers, who face fines if caught fare evading. The deterrence of fare evasion depends on the unpredictability and effectiveness of the patrols. TRUSTS models the problem of computing patrol strategies as a leader-follower Stackelberg game where the objective is to deter fare evasion and hence maximize revenue. This problem differs from previously studied Stackelberg settings in that the leader strategies must satisfy massive temporal and spatial constraints; moreover, unlike in these counterterrorism-motivated Stackelberg applications, a large fraction of the ridership might realistically consider fare evasion, and so the number of followers is potentially huge. A third key novelty in our work is deliberate simplification of leader strategies to make patrols easier to execute. We present an efficient algorithm for computing such patrol strategies and present experimental results using real-world ridership data from the Los Angeles Metro Rail system. The Los Angeles County Sheriff's Department is currently carrying out trials of TRUSTS. There are, quite literally, no barriers to entry, as illustrated in figure 1. Instead, security personnel are dynamically deployed throughout the transit system, randomly inspecting passenger tickets. This proof-of-payment fare collection method is typically chosen as a more cost-effective alternative to direct fare collection, that is, when the revenue lost to fare evasion is believed to be less than what it would cost to make fare evasion impossible. For the LA Metro, with approximately 300,000 riders daily, this revenue loss can be significant; the annual cost has been estimated at $5.6 million. The Los Angeles Sheriff's Department (LASD) deploys uniformed patrols onboard trains and at stations for fare checking (and for other purposes such as crime prevention), in order to discourage fare evasion.

This article describes the ExpertCop tutorial system, a simulator of crime in an urban region. In ExpertCop, the students (police officers) configure and allocate an available police force according to a selected geographic region and then interact with the simulation. The student interprets the results with the help of an intelligent tutor, the pedagogical agent, observing how crime behaves in the presence of the allocated preventive policing. The interaction between domain agents representing social entities as criminals and police teams drives the simulation. ExpertCop induces students to reflect on resource allocation.

Unmanned surface vessels (USVs), which can patrol Singapore waters autonomously, have been on trial since late last year said the Singapore Police Coast Guard (PCG). Two of the USVs - one measuring 9m long and the other 16m - were showcased to the media yesterday, in the open water off Marina Bay. Leveraging on technology, USVs are part of Singapore's "multi-layered" defence, which also includes sophisticated land-based CCTV cameras and sensors, to protect its maritime borders. Said Superintendent Lin Zhenqiang, head of operations and security at the Police Coast Guard: "These USVs are able to conduct autonomous patrols. And this will help us project police presence."

As questions continue to mount about the Niger firefight that killed four U.S. soldiers in early October, here's a timeline on what happened based on new details from the Department of Defense. U.S. military officials sought permission to send an armed drone near a patrol of Green Berets before a deadly ambush Oct. 4 in Niger, but the request was blocked, raising questions about whether those forces had adequate protection against the dangers of their mission. New information shows the Green Beret team was part of a larger mission, one potentially more dangerous than initially described, and one believed to merit an armed drone. But the request was blocked in a chain of approval that snakes through the Pentagon, State Department and the Nigerien government, according to officials briefed on the events. One focus of military investigations into what happened in Niger will be what a military official now says were two changes in the mission of the Green Beret team--from initially training Nigerien forces, to advising on a mission to capture or kill a wanted terrorist, to investigating the terrorist's abandoned camp.