Copacetic Smartening of Small Data for HLC

去年，对更像人类的计算的需求变得越来越明显，这包括赋予机器类似人类的感知推理和学习能力。令人费解的“黑盒子”，高度复杂的深度学习技术和传统概率方法的上下文依赖模型，在简易爆炸装置处置（IEDD）等环境中并不总是成功的，这可能会导致错误判断的严重后果。朝着更透明、更可解释、更人性化的方向发展，将改变人机关系，为人类和机器的合作提供更高效、更有效的环境，从而改善英国的增长和就业前景。这项可行性研究的重点是那些高风险的情况下，人类的认知能力优于任何机器，当人类被要求在信息稀少、时间有限的情况下做出判断时，他们以前的知识、经验和“直觉”往往在他们的决策中起着关键作用。与机器不同，人类依靠小规模数据和小规模模型（例如模式或框架）来做出判断，反映意外事件的可能性或可能性，以提高他们在给定情况下的意义。一个关键的挑战是识别那些少数关键的学习和推理核（click），它们是这些模式的核心，人类使用这些模式以一种令人满意的方式做出判断，感觉正确，也就是说，事情似乎处于正常或完美的顺序。利用IEDD背景作为背景，本研究远离了传统的贝叶斯和基于概率的方法，而是朝着一种受认知科学启发的新方法发展，以开发类似人类的推理技术和学习模式。然后，该模式将被编码为可解释的人工智能（XAI）代理，以便它们可以与人类一起工作，以提高在高认知负荷任务中的表现，并用于学习和培训未来的专家。

The need for more human-like computing, which involves endowing machines with human-like perceptual reasoning and learning abilities, has becoming increasingly evident in the last year. The inexplicable 'black box', highly complex and context dependent models of deep learning techniques and conventional probability approaches, are not always successful in environments like Improvised Explosive Device Disposal (IEDD), which can have severe consequences for incorrect judgements. Moving towards a more transparent, explainable and human-like approach will transform the human-machine relationship and provide a more efficient and effective environment for humans and machines to collaborate in, leading to improved prospects for UK growth and employment.This feasibility study focuses on those high risk situations where human cognition is superior to any machine, when humans are called to make judgements where information is sparse, time is poor and their previous knowledge, experience and 'gut feel' often play a critical part in their decision making. Unlike machines, humans rely on small scale data and small scale models (e.g. schema or frames) to make their judgements, reflecting on the possibilities or likelihoods of surprise events to improve their sense making in a given situation. A key challenge is to identify those few critical learning and inference kernels (CLIKs) that are at the heart of these schema humans use to make their judgements in a satisficing manner that feels right, i.e. things appear to be in copacetic or perfect order. Using the IEDD context as its setting, this research moves away from the conventional Bayesian and probability-based approaches, instead moving towards a novel approach inspired by the cognitive sciences to develop human-like inference techniques and learning schema. The schema will then be encoded into explainable artificial intelligence (XAI) agents so they can work alongside humans to enhance performance during high cognitive load tasks and for the learning and training of future experts.