An Optimum Well Control Using Reinforcement Learning and Policy Transfer; Application to Production Optimization and Slugging Minimization

Production optimization of oil, gas and geothermal wells suffering from unstable multiphase flow phenomena such as slugging is a challenging task due to their complexity and unpredictable dynamics. In this work, reinforcement learning which is a novel machine learning based control method was applied to find optimum well control strategies to...

Moral Decision Making in Human-Agent Teams: Human Control and the Role of Explanations

With the progress of Artificial Intelligence, intelligent agents are increasingly being deployed in tasks for which ethical guidelines and moral values apply. As artificial agents do not have a legal position, humans should be held accountable if actions do not comply, implying humans need to exercise control. This is often labeled as Meaningful...

Evaluating XAI: A comparison of rule-based and example-based explanations

Current developments in Artificial Intelligence (AI) led to a resurgence of Explainable AI (XAI). New methods are being researched to obtain information from AI systems in order to generate explanations for their output. However, there is an overall lack of valid and reliable evaluations of the effects on users' experience of, and behavior in...

Allocation of moral decision-making in human-agent teams: A pattern approach

Artificially intelligent agents will deal with more morally sensitive situations as the field of AI progresses. Research efforts are made to regulate, design and build Artificial Moral Agents (AMAs) capable of making moral decisions. This research is highly multidisciplinary with each their own jargon and vision, and so far it is unclear whether...

SAIL: A Social Artificial Intelligence Layer for Human-Machine Teaming

Human-machine teaming (HMT) is a promising paradigm to approach future situations in which humans and autonomous systems closely collaborate. This paper introduces SAIL, a design method and framework for the development of HMT-concepts. Starting point of SAIL is that an HMT can be developed in an iterative process in which an existing autonomous...

The sail framework for implementing human-machine teaming concepts

Human-machine teaming (HMT) is a promising paradigm to approach situations in which humans and autonomous systems must closely collaborate. This paper describes SAIL, a software framework for implementing HMT-concepts. The approach of SAIL is to integrate existing autonomous systems in a framework, that serves as a social layer between...

Contrastive Explanations with Local Foil Trees

Recent advances in interpretable Machine Learning (iML) and eXplainable AI (XAI) construct explanations based on the importance of features in classification tasks. However, in a high-dimensional feature space this approach may become unfeasible without restraining the set of important features. We propose to utilize the human tendency to ask...

Contrastive Explanations for Reinforcement Learning in terms of Expected Consequences

Machine Learning models become increasingly proficient in complex tasks. However, even for experts in the field, it can be difficult to understand what the model learned. This hampers trust and acceptance, and it obstructs the possibility to correct the model. There is therefore a need for transparency of machine learning models. The development...

Using perceptual and cognitive explanations for enhanced human-agent team performance

Adaptive Maritime Automation: Final Report

BCI to potentially enhance streaming images to a VR Headset by Predicting Head Rotation

While numerous studies show that brain signals contain information about an individual’s current state that are potentially valuable for smoothing man–machine interfaces, this has not yet lead to the use of brain computer interfaces (BCI) in daily life. One of the main challenges is the common requirement of personal data that is correctly...

Pluggable Social Artificial Intelligence for Enabling Human-Agent Teaming

As intelligent systems are increasingly capable of performing their tasks without the need for continuous human input, direction, or supervision, new human-machine interaction concepts are needed. A promising approach to this end is human-agent teaming, which envisions a novel interaction form where humans and machines behave as equal team...

ICM: An Intuitive Model Independent and Accurate Certainty Measure for Machine Learning

End-users of machine learning-based systems benefit from measures that quantify the trustworthiness of the underlying models. Measures like accuracy provide for a general sense of model performance, but offer no detailed information on specific model outputs. Probabilistic outputs, on the other hand, express such details, but they are not...