Ethical Constraint Encoding in High-Autonomy AI Simulation Scenarios

Abstract

High-autonomy AI systems trained in simulation environments must be guided by ethical constraints that
influence not only the outcomes they produce but also the internal reasoning processes through which
decisions are formed. Traditional constraint strategies based on rule enforcement or reward shaping often fail
under complex or adversarial conditions, leading to behavior that superficially meets ethical requirements
while violating deeper normative expectations. This study introduces a framework for encoding ethical
constraints directly into the representational and policy layers of autonomous agents, combined with dynamic
context-based modulation that adjusts ethical priorities according to situational demands. Simulation results
across cooperative, competitive, and mixed-motivation environments show that agents with embedded ethical
priors exhibit consistent value-aligned behavior, maintain strategic adaptability, and resist exploitation
attempts that circumvent rule-based controls. The findings highlight the importance of treating ethical
alignment as a structural learning principle rather than a post-hoc regulatory mechanism.