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Copacetic Smartening of Small Data for HLC

HLC 小数据的共智能

基本信息

批准号：
EP/R030987/1
负责人：
Natalie Clewley
金额：
$ 19.25万
依托单位：
CRANFIELD UNIVERSITY
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2018
资助国家：
英国
起止时间：
2018 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FR030987%2F1
关键词：
Copacetic Smartening Small Data HLC

项目摘要

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.

在过去的一年里，对更多类似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。在简易爆炸装置处置(IEDD)这样的环境中，深度学习技术和传统概率方法的高度复杂和上下文相关的高度复杂的黑匣子模型并不总是成功的，这可能会导致错误判断的严重后果。转向更透明、更可解释、更人性化的方法将改变人机关系，为人类和机器提供更高效和有效的协作环境，从而改善英国的增长和就业前景。这项可行性研究聚焦于人类认知优于任何机器的高风险情况，当人类被要求做出判断时，信息稀少，时间匮乏，他们之前的知识、经验和直觉往往在他们的决策中发挥关键作用。与机器不同，人类依靠小规模数据和小规模模型(如图式或框架)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判断力。一个关键的挑战是确定那些处于人类用来以令人满意的方式做出判断的这些图式的核心的少数几个关键的学习和推理核心(CLK)，即，事情似乎是在一致或完美的秩序中。以IEDD为背景，本研究摆脱了传统的贝叶斯和基于概率的方法，而是转向一种受认知科学启发的新方法，以开发类似人类的推理技术和学习图式。然后，该模式将被编码到可解释的人工智能(XAI)代理中，这样它们就可以与人类合作，在高认知负荷任务中提高性能，并用于未来专家的学习和培训。