Abstract

Long‑horizon planning and decision algorithms enable autonomous intelligent agents to make sequential decisions over extended time frames in complex, uncertain environments. Unlike short‑term reactive strategies, long‑horizon planning involves anticipating future states, evaluating multi‑step outcomes, and optimizing cumulative performance with respect to strategic objectives. This capability is essential in domains such as autonomous vehicles, mobile robotics, space exploration, automated logistics, strategic gameplay, defense systems, and intelligent manufacturing. Long‑horizon decision making integrates core techniques from classical planning, reinforcement learning, probabilistic reasoning, hierarchical control, model predictive control, and heuristic search, often requiring trade‑offs between computational tractability and optimality. This research synthesizes foundational theories and recent advances in long‑horizon planning, compares algorithmic paradigms, and assesses their performance in autonomous agent applications. Through systematic literature review and analytical synthesis, we describe representative frameworks including Markov decision processes (MDPs), Partially Observable MDPs (POMDPs), hierarchical reinforcement learning, Monte Carlo Tree Search (MCTS), model‑based planning, and optimization‑based strategies. We highlight challenges in scalability, uncertainty handling, reward sparsity, and real‑time execution, and discuss solution approaches such as abstraction, temporal hierarchies, simulation‑based planning, and transfer learning. Empirical findings indicate that hybrid methods combining learning and planning outperform pure approaches in dynamic, long‑horizon scenarios. We conclude with directions for improving interpretability, safety, and generalization in long‑horizon planning for autonomous intelligent agents.