A Fuzzy inference expert system to support the decision of deploying a military naval unit to a mission

Naval military units are complex systems required to operate in xed time frames in o shore tasks where maintenance operations are drastically limited. A failure during a mission is a critical event that can drastically in uence the mission success. The decision of switching a unit to a mission hence requires complex judgments involving information about the health status of machineries and the environmental conditions. The present procedure aims to support the decision about switching a unit to a mission considering the vagueness and uncertainty of information by means of fuzzy theory and emulates the decision process of a human expert by means of a rule-based inference engine. A numerical application is presented to prove the e ectiveness of the approach

A maritime decision support system to assess risk in the presence of environmental uncertainties: the REP10 experiment

The aim of this work is to report on an activity carried out during the 2010 Recognized Environmental Picture experiment, held in the Ligurian Sea during summer 2010. The activity was the first at-sea test of the recently developed decision support system (DSS) for operation planning, which had previously been tested in an artificial experiment. The DSS assesses the impact of both environmental conditions (meteorological and oceanographic) and non-environmental conditions (such as traffic density maps) on people and assets involved in the operation and helps in deciding a course of action that allows safer operation. More precisely, the environmental variables (such as wind speed, current speed and significant wave height) taken as input by the DSS are the ones forecasted by a super-ensemble model, which fuses the forecasts provided by multiple forecasting centres. The uncertainties associated with the DSS's inputs (generally due to disagreement between forecasts) are propagated through the DSS's output by using the unscented transform. In this way, the system is not only able to provide a traffic light map (run/not run the operation), but also to specify the confidence level associated with each action. This feature was tested on a particular type of operation with underwater gliders: the glider surfacing for data transmission. It is also shown how the availability of a glider path prediction tool provides surfacing options along the predicted path. The applicability to different operations is demonstrated by applying the same system to support diver operations

A technique for determining viable military logistics support alternatives

A look at today's US military will see them operating much beyond the scope of protecting and defending the United States. These operations now consist of, but are not limited to humanitarian aid, disaster relief, and conflict resolution. This broad spectrum of operational environments has necessitated a transformation of the individual military services into a hybrid force that can leverage the inherent and emerging capabilities from the strengths of those under the umbrella of the Department of Defense (DOD), this concept has been coined Joint Operations. Supporting Joint Operations requires a new approach to determining a viable military logistics support system. The logistics architecture for these operations has to accommodate scale, time, varied mission objectives, and imperfect information. Compounding the problem is the human in the loop (HITL) decision maker (DM) who is a necessary component for quickly assessing and planning logistics support activities. Past outcomes are not necessarily good indicators of future results, but they can provide a reasonable starting point for planning and prediction of specific needs for future requirements. Adequately forecasting the necessary logistical support structure and commodities needed for any resource intensive environment has progressed well beyond stable demand assumptions to one in which dynamic and nonlinear environments can be captured with some degree of fidelity and accuracy. While these advances are important, a holistic approach that allows exploration of the operational environment or design space does not exist to guide the military logistician in a methodical way to support military forecasting activities. To bridge this capability gap, a method called A Technique for Logistics Architecture Selection (ATLAS) has been developed. This thesis describes and applies the ATLAS method to a notional military scenario that involves the Navy concept of Seabasing and the Marine Corps concept of Distributed Operations applied to a platoon sized element. This work uses modeling and simulation to incorporate expert opinion and knowledge of military operations, dynamic reasoning methods, and certainty analysis to create a decisions support system (DSS) that can be used to provide the DM an enhanced view of the logistics environment and variables that impact specific measures of effectiveness.Ph.D.Committee Chair: Mavris, Dimitri; Committee Member: Fahringer, Philip; Committee Member: Nixon, Janel; Committee Member: Schrage, Daniel; Committee Member: Soban, Danielle; Committee Member: Vachtsevanos, Georg

Trusting AI: Integrating Artificial Intelligence into the Army’s Professional Expert Knowledge

Integrating artificially intelligent technologies for military purposes poses a special challenge. In previous arms races, such as the race to atomic bomb technology during World War II, expertise resided within the Department of Defense. But in the artificial intelligence (AI) arms race, expertise dwells mostly within industry and academia. Also, unlike the development of the bomb, effective employment of AI technology cannot be relegated to a few specialists; almost everyone will have to develop some level of AI and data literacy. Complicating matters is AI-driven systems can be a “black box” in that humans may not be able to explain some output, much less be held accountable for its consequences. This inability to explain coupled with the cession to a machine of some functions normally performed by humans risks the relinquishment of some jurisdiction and, consequently, autonomy to those outside the profession. Ceding jurisdiction could impact the American people’s trust in their military and, thus, its professional standing. To avoid these outcomes, creating and maintaining trust requires integrating knowledge of AI and data science into the military’s professional expertise. This knowledge covers both AI technology and how its use impacts command responsibility; talent management; governance; and the military’s relationship with the US government, the private sector, and society.https://press.armywarcollege.edu/monographs/1955/thumbnail.jp

Chinese Mine Warfare: A PLA Navy \u27Assassin\u27s Mace\u27 Capability

After a lengthy hiatus-lasting nearly six centuries—China is reemerging as a maritime power, this time with an emphasis on undersea warfare. Between 1996 and 2006, the Chinese navy took delivery of more than thirty submarines. These vessels include two new classes of nuclear submarines-the advanced Song-class diesel submarines and the Yuan class of diesel boats which, according to some reports, was a surprise for U.S. intelligence. Above and beyond this ambitious naval construction program, the People\u27s Republic of China (PRC) received during 2005-06 an additional eight formidable Kilo-class submarines (and associated weaponry), which were purchased in 2002, to add to the four it already operated. A new nuclear submarine base on Hainan Island may well herald a new era of more extended Chinese submarine operations.https://digital-commons.usnwc.edu/cmsi-red-books/1002/thumbnail.jp

Requirement analysis framework of naval military system for expeditionary warfare

Military systems are getting more complex due to the demands of various types of missions, rapidly evolving technologies, and budgetary constraints. In order to support complex military systems, there is a need to develop a new naval logistic asset that can respond to global missions effectively. This development is based on the requirement which must be satisfice-able within the budgetary constraints, address pressing real world needs, and allow designers to innovate. This research is conducted to produce feasible and viable requirements for naval logistic assets in complex military systems. The process to find these requirements has diverse uncertainties about logistics, environment and missions. To understand and address these uncertainties, this research includes instability analysis, operational analysis, sea state analysis and disembarkation analysis. By the adaptive Monte-Carlo simulation with maximum entropy, uncertainties are considered with corresponding probabilistic distribution. From Monte-Carlo simulation, the concept of Probabilistic Logistic Utility (PLU) was created as a measure of logistic ability. To demonstrate the usability of this research, this procedure is applied to a Medium Exploratory Connector (MEC) which is an Office of Naval Research (ONR) innovative naval prototype. Finally, the preliminary design and multi-criteria decision-making method become capable of including requirements considering uncertainties.Ph.D

Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning

The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques