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Towards Artificial Virtuous Agents: Games, Dilemmas and Machine Learning
Using modern day AI techniques, such as affinity-based reinforcement learning and explainable AI, we motivate the implementation of virtuous agents that play such role-playing games, and the examination of their decisions through a virtue ethical lens.
Symbolic Explanation of Affinity-Based Reinforcement Learning Agents with Markov Models
We have developed a policy regularization method that asserts the global intrinsic affinities of learned strategies.
Towards Responsible AI for Financial Transactions
The application of AI in finance is increasingly dependent on the principles of responsible AI.
Balancing Profit, Risk, and Sustainability for Portfolio Management
Stock portfolio optimization is the process of continuous reallocation of funds to a selection of stocks.
Reinforcement Learning with Intrinsic Affinity for Personalized Prosperity Management
The common purpose of applying reinforcement learning (RL) to asset management is the maximization of profit.
Can Interpretable Reinforcement Learning Manage Prosperity Your Way?
Personalisation of products and services is fast becoming the driver of success in banking and commerce.
Reinforcement Learning Your Way: Agent Characterization through Policy Regularization
These methods rely on empirical observations of the policy and thus aim to generalize a characterization of agents' behaviour.
Understanding Spending Behavior: Recurrent Neural Network Explanation and Interpretation
In this study, we address this issue by extracting a symbolic explanation for our model and providing an interpretation of our temporal features.
Clustering in Recurrent Neural Networks for Micro-Segmentation using Spending Personality
Customer segmentation has long been a productive field in banking.
