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Developing human-like decision making algorithms for high risk environments


Project description

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 Cyber Security and Computer Network Defence, 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 Cyber Security and Computer Network Defence 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.

The title of this research project is: "Copacetic Smartening of Small Data for HLC".