Journal of Pioneering Artificial Intelligence Research

Abstract

This study introduces a deterministic framework for autonomous decision-making based on structural elim ination over finite candidate sets. In contrast to conventional approaches in robotics and machine learning, where decisions are obtained through iterative search, optimization, or learning, the proposed framework formulates autonomy as a non-iterative process in which infeasible candidates are progressively removed under deterministic objectives. The system operates within an open objective architecture, where each objec tive acts as a constraint and is admitted only if feasibility is preserved, ensuring structural consistency while allowing extensibility of goals. The framework is further situated within a three-layer view of autonomous sys tems, comprising perception, decision, and execution. At the perception level, recognition is achieved through Structural Elimination Recognition (SER), while at the decision level, admissible actions are obtained via constraint-based pruning of candidate sets. Execution is treated as a separate layer governed by system dynamics and control, where iterative methods may still be required. A minimal illustrative example demon strates that decisions can be revealed without search or parameter updates, even under changing objective conditions in real practical scenarios. The results highlight a complementary regime of autonomy in which solutions emerge from the elimination of infeasible alternatives rather than exploration of a solution space. The framework reveals that the limiting factor in autonomous systems is the compatibility structure of the objective set.