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In its basic form, a prototype is an expression of design intent. Prototyping allows designers to present their designs and see them in action. In the context of digital products, a prototype is a simulation of the final interaction between the user and the interface. Depending on what a product team needs a prototype to do, it can simulate an entire app or just a single interaction.
Low-fidelity (lo-fi) prototyping is a quick and easy way to translate high-level design concepts into tangible and testable artifacts. The first and most important role of lo-fi prototypes is to check and test functionality rather than the visual appearance of the product.
Paper prototyping allows you to prototype a digital product interface without using digital software. The technique is based on creating hand drawings of different screens that represent user interfaces of a product. While this is a relatively simple technique, it can be useful when a product team needs to explore different ideas and refine designs quickly. This is especially true in the early stages of design when the team is trying different approaches.
High-fidelity (hi-fi) prototypes appear and function as similar as possible to the actual product that will ship. Teams usually create high-fidelity prototypes when they have a solid understanding of what they are going to build and they need to either test it with real users or get final-design approval from stakeholders.
Samsung's Platform Development Kit software for key-value SSDs originally supported their own software API, but now additionally supports the vendor-neutral SNIA standard API. The prototype drives are currently available for companies that are interested in developing software to use KV SSDs. Samsung's KV SSDs probably will not move from prototype status to being mass production products until after the corresponding key-value command set extension to NVMe is finalized, so that KV SSDs can be supported without needing a custom NVMe driver. The SNIA standard API for key-value drives is a high-level transport-agnostic API that can support drives using NVMe, SAS or SATA interfaces, but each of those protocols needs to be extended with key-value support.
TECHNOLOGY AREA(S): Human Systems OBJECTIVE: Develop technologies to support the construction of Artificial Intelligence agents for use in simulations in infantry small unit decision making training. Agents must have realistic thinking, require minimal manual coding and editing of behaviors, and the tools must be capable of developing behaviors across a range of military infantry activities. DESCRIPTION: Simulation is a key enabler for training that allows Warfighters to develop their skills without incurring costs associated with training (e.g. fuel, munitions, etc.) or putting their safety at risk [1]. The use of first-person simulations (e.g., Virtual Battle Space 3) for infantry training is manpower-intensive, requiring additional operators (i.e., pucksters) to control friendly and enemy military units, limiting the ability to training only individual unit leaders (e.g. squad) without additional manpower support. Historically, the military has long supported the use of constructive agents and virtual humans [2, 3] although their application within the Marine Corps has been limited [1]. Infantry small unit leaders need simulation-based training without the manpower costs associated with pucksters such as training that leverages intelligent and behaviorally realistic agents that are easy to teach. Agents are autonomous entities capable of goal-directed activities based on their perception of the simulated environment, such that military personnel interacting with them would not require extensive training nor amount to a frustrating undertaking. Advances in Artificial Intelligence (AI) have increased machines' capability to learn and allow machines to outperform humans on video games and board games [4, 5]. However, these techniques require a very large dataset for training and are mostly constrained to discrete domains. As such, these approaches are not easily extensible to the infantry simulation where rules and environments are ill-defined and action spaces are continuous. Aside from deep reinforcement learning, other approaches that complement traditional human learning have been investigated. For example, Interactive Task Learning (ITL) is an approach to support artificial agents learning new tasks through natural interactions and observations with humans [6]. The Air Force Research Laboratory (AFRL) has been developing a Synthetic Teammate capability [7], but there has been limited application to the infantry domain.The goal of this effort is to develop technologies to support the construction of intelligent artificial agents in order to reduce the manpower required to develop and oversee the execution of those agents within infantry training first-person simulations. The focus of the training is on developing AI-based behavior for friendly and enemy agents to support Marine Corps Forward Observers training. In addition to the technologies for agent learning, there is also a need to develop metrics and evaluations that can span across multiple Infantry tasks and domains. All development and demonstrations should be done with simulation engines that have no or minimal licensing fees for development or run-time execution (e.g. Unity). PHASE I: Required Phase I deliverables will include a feasibility study. Included in this study will be an initial concept design for Artificial Intelligence for Infantry Simulation in Small Unit Decision Making Training that models key elements as well as a detailed outline of success criteria. Additionally, at least one behavior using the technologies proposed should be developed. Since access to Marine Corps personnel will not be supported during Phase I, surrogate tasks are acceptable for proof of concept. A final report will be generated, including system performance metrics and plans for Phase II. Ensuring an open architecture to allow integration with other military relevant systems (e.g. Augmented Immersive Team Trainer, Virtual Battle Space 3) will be considered a critical performance metric. Phase II plans should include key component technological milestones and plans for at least two demonstrations. PHASE II: Phase II will include further behavioral development and evaluations with at least two evaluations from SME representatives that will be identified from the government. Required Phase II deliverables will include the construction, demonstration, and validation of a prototype system based on results from Phase I. All appropriate engineering testing will be performed and a critical design review will be performed to finalize the design and technologies before the evaluations. Additional deliverables will include: 1) a working prototype, 2) any associated drawings and specification for its construction, and 3) test data on its performance, in accordance with the demonstration success criteria developed in Phase I. PHASE III: The performer will be expected to support the Marine Corps in transitioning the software products that enable the construction of intelligent and behavioral realistic agents for Infantry small unit training. The software products are expected to support and/or be integrated with existing Marine Corps training simulations (e.g. Augmented Immersive Team Trainer, Virtual Battle Space 3). Phase III tasks will include certifying and qualifying the system for Marine Corps use, delivering a Marine Corps user manual for the product, and providing Marine Corps system specification materials. Private Sector Commercial Potential: It is anticipated this technology will have broad applications in military as well as commercial settings. The use of artificial intelligence (AI) is continuing to grow, but it is currently limited to certain sets of tasks. For example, virtual reality is currently being used by some professional sports teams (e.g. NFL), but has limited or no AI application. It would be very beneficial to allow position specific (e.g. Quarterback) training that supports realistic agent behavior so that players can practice for upcoming games and specific opponents either with their team or independently. REFERENCES: 1. Naval Research Advisory Committee. (2009). Immersive Simulation for Marine Corps Small Unit Training. Retrieved 6 June 2016 from _rpt_Immersive_Sim.pdf 153554b96e
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