Journal of Pioneering Artificial Intelligence Research

Abstract

This study presents a deterministic framework for autonomous decision-making in which perception, action selection, and coordination are achieved through progressive elimination of infeasible candidates rather than optimization or search. Instead of constructing solutions through cost evaluation or iterative improvement, the proposed approach operates on a finite candidate space and removes incompatible elements through explicit structural constraints until a single admissible outcome remains. A minimal real-time interception scenario is considered in which an autonomous agent must identify a moving target, execute an engagement sequence, and coordinate retrieval through a cooperative unit under adversarial conditions. In this setting, both percep¬tion and action selection are governed by the same elimination principle. The presence of multiple targets and decoys does not require ranking or probabilistic inference; instead, it introduces additional constraints that accelerate candidate reduction. The framework extends naturally to multi-agent systems, where coordination emerges from consistency of constraint-driven eliminations across agents. The same mechanism applies un¬der role inversion, governing both pursuit and evasion. The results suggest a complementary perspective on artificial intelligence in which decision is not constructed through search, but revealed through deterministic reduction over a constrained structure. In contrast to planning approaches such as Goal-Oriented Action Planning (GOAP), which rely on heuristic search and cost evaluation over action sequences, the proposed framework eliminates infeasible candidates directly, without constructing or ranking alternative plans. Al¬though illustrated through an interception scenario, the proposed framework is not limited to pursuit tasks. The same elimination-based mechanism applies to a wide range of domains, including maritime, aerial, and ground operations, as well as search and detection problems.