Self-steering AI: A Mathematical Framework for Dynamic Goal Adaptation in Autonomous Systems

Self-Steering AI: A Mathematical Framework for Dynamic Goal Adaptation in Autonomous Systems This working paper introduces self-steering AI, a unifying framework in which goals are treated as evolving state variables rather than fixed specifications. The paper develops the mathematics of goal dynamics — including Lyapunov stability, information-theoretic speed limits, and constraint projections — and connects these foundations to neural, symbolic, and hybrid architectures, learning algorithms, and monitoring techniques. Applications are illustrated in robotics, healthcare, finance, human–AI collaboration, and multi-agent societal systems. Beyond the technical results, the paper explores philosophical and governance implications such as autonomy, identity, ethics, and alignment. The guiding insight is that rigid, static objectives are brittle, while carefully constrained and transparent goal evolution provides resilience, corrective alignment, and long-term trustworthiness. This reframing synthesizes threads from adaptive control, reinforcement learning, and AI alignment into a single coherent framework. Version 2.3 is released on Zenodo as a working paper to support open review, feedback, and iterative refinement. For collaborations or applied projects on self-steering AI, please contact the author at jpblankert.com Keywords:self-steering AI, dynamic goals, adaptive alignment, stability analysis, Lyapunov methods, information theory, constraint projection, safe reinforcement learning, goal adaptation, human–AI interaction, multi-agent systems, AI governance, AI safety, autonomous systems