The autonomous mission: A theory of value-aligned optimization in the age of intelligent agents

The misalignment of Artificial Intelligence (AI) with established objectives will lead to a perplexing array of existential risks, ranging from autonomous nuclear defense systems to detrimental United Nations (UN) disaster-response robots, as inadequate value alignment generates semantic and territorial disarray. This groundbreaking study presents ‘Mission-Aligned Optimization (MAO)’, a fundamental theory of hierarchical, real-time adaptation that ensures accountability between guardians and machines. Significant innovations encompass the Mission-Adaptive Resource and Quality (MARQ) Framework, which is crucial for dynamically reconciling the trade-offs among quality-of-result (QoR), temporal, and resource limitations amid uncertainty; distributed hierarchical optimization methodologies, which facilitate the conversion of missions into verifiable optimization models (such as multi-unmanned vehicles conducting priority convoy operations, exemplified in Uber’s Alternating Direction Method of Multipliers (ADMM); and auditable human models, such as those developed by Princeton or utilized in UN distributed humanitarian efforts. MAO delineates the foundational elements for ‘purpose-constitutive’ AI, applicable to robust commercial, humanitarian, and military contexts.