Bridging Efficiency and Morality: Ethical LTL Construction for Faster Convergence in Q-Learning‘s Ethical Dilemmas

AI agents make decisions before executing actions, and we expect AI systems to make these decisions independently in large-scale autonomous deployments. Decisions made by AI agents are vital for preventing harm to humans and prioritizing human lives, both of which are considered moral objectives. Thus, the objective of building a general-purpose robot is to establish a mechanism for monitoring ethical decision-making by studying the decision-making process in partially observable Markov decision process (POMDP) environments. The challenge is how to generate an ethical linear temporal logic specification for model checking to ensure that the decisions made by AI are moral. Existing approaches use rule-based metrics or utility functions to constrain AI decision-making, but they are easily compromised because they lack a reasoning system. Our proposed approach is designed from a psychological perspective and trains the agent to learn a model-checking logic system without prior knowledge. We evaluate the approach on a modified burning-room problem and demonstrate its robustness and flexibility.