Abstract

Machine reasoning systems integrate knowledge representation, logical inference, and decision models to support autonomous and strategic decision‑making across domains such as robotics, defense, healthcare, finance, and autonomous vehicles. These systems extend beyond data‑driven prediction by embedding structured reasoning capabilities that enable interpretation of complex scenarios, anticipation of future states, and justification of decisions. This research synthesizes foundational theories in symbolic reasoning, probabilistic reasoning, and hybrid neuro‑symbolic approaches; examines architectural patterns that enable real‑time reasoning in dynamic environments; and evaluates practical deployments of reasoning systems in autonomous and strategic contexts. A mixed methodology combining systematic literature analysis, architectural evaluation, and comparative case synthesis reveals the strengths and limitations of current machine reasoning paradigms. Key findings show that while symbolic reasoning provides explainability and formal guarantees, its brittleness in uncertain environments necessitates integration with probabilistic and learning‑based methods. Neuro‑symbolic reasoning emerges as a promising avenue for scalable, adaptive reasoning capable of handling both structured knowledge and perceptual data. Challenges remain in knowledge acquisition, scalability, real‑time performance, and human‑machine interaction. The paper concludes with recommendations for hybrid reasoning architectures, standardized benchmarks, and human‑centered design practices to advance machine reasoning for autonomous and strategic decision‑making.