SBIR Phase I: Adaptive Hybrid Intelligent Systems Managing Uncertainty in Operational Environments

这一小型企业创新研究(SBIR)第一阶段项目建议研究规则引擎技术的改进，以支持自主“智能系统”的开发，这些系统可以识别它们必须何时以及如何适应意外的环境条件。推理技术在提供有价值的控制、监控和数据分析功能的各种商业系统中非常重要。在医疗保健领域，它们为患者监测、疾病监测和决策支持等提供不可或缺的服务，使此类系统能够可靠和可预测地运行。然而，现实世界环境的特点是意外事件，自主系统发现如果没有人类的帮助，这些事件很难识别和补偿。这些挑战表明，需要一个高级管理组件，该组件能够识别何时操作条件发生变化以及何时系统必须重新配置或重新训练其控制算法。我们将研究自适应混合智能系统的开发，将两种或两种以上的推理技术结合到一个通用的管理框架中，承诺提高自主系统的响应性和适应性。这个项目的更广泛的影响/商业潜力可以很容易地在医疗保健市场上得到展示，在这个市场上，现在生成的患者数据量无法由任何一家提供商可靠地分析。迫切需要新的临床决策支持技术和产品来处理和推理复杂的患者数据，并帮助临床医生做出适当的决策。如果成功，所提出的混合智能系统架构可能适用于许多医疗设备。例如，目前手动调整以保持足够的患者血氧饱和度的气体混合器可以通过算法进行伺服控制，当简单地增加气体输送不合适时，这些算法可以安全地检测和报警。此外，混合智能系统架构的用途并不是医疗领域所特有的。控制算法或模型的最佳性能需要上下文感知的任何用例都可能是一个潜在的候选者。混合控制架构承诺减少这类系统对人为监督的依赖，如果应用得当，可以减少人为错误，提高成本效益，同时仍保持质量标准。

This Small Business Innovation Research (SBIR) Phase I project proposes to research improvements in rule engine technologies that enable the development of autonomous "smart systems" that can identify when and how they must adapt to unexpected environmental conditions. Reasoning technologies are important in a variety of commercial systems providing valuable control, monitoring, and data analysis capabilities. Within healthcare, they provide indispensable services for patient monitoring, disease surveillance, and decision support, etc., allowing such systems to operate reliably and predictably. Nevertheless, real-world environments are characterized by unexpected events that autonomous systems find difficult to recognize and compensate for without human assistance. These challenges suggest the need for a high-level Management Component capable of identifying when operational conditions change and when the system must re-configure or re-train its control algorithms. We will research the development of adaptive hybrid intelligent systems combining two or more reasoning technologies into a general-purpose management framework that promises to improve an autonomous system's responsiveness and adaptability.The broader impact/commercial potential of this project can be readily illustrated within the healthcare market where the volume of patient data now being generated cannot reliably be analyzed by any one provider. New clinical decision support technologies and products are desperately needed to process and reason over complex patient data and to assist clinicians in making appropriate decisions. If successful, the proposed hybrid intelligent system architecture is potentially applicable to many medical devices. For example, gas blenders that are currently adjusted manually to maintain adequate patient oxygen saturation could be servo controlled with algorithms that safely detect and alarm when simply increasing gas delivery is not appropriate. Furthermore, the utility of hybrid intelligent system architectures is not specific to the medical domain. Any use case where contextual awareness is required for optimal performance of a control algorithm or model could be a potential candidate. Hybrid control architectures promise to reduce the dependency that such systems have on human oversight, and if appropriately applied, could reduce human error, improve cost-effectiveness and yet still maintain quality standards.