Adaptive Controllable Emergence in Multi-Task Air and Space Defense Systems: A Framework for Mission Reconfiguration, Resilient Coordination, Intelligent Decision-Making, and Dynamic Resource Allocation
Emergent collective intelligence provides an important theoretical and computational perspective for understanding how locally interacting agents can generate coordinated global behaviors that cannot be explained by the behavior of individual agents alone. In large-scale air and space defense systems, this property is particularly relevant because heterogeneous sensing, decision-making, communication, and execution resources must operate under dynamic environments, incomplete information, changing mission requirements, limited resources, and potentially degraded communication conditions. However, conventional controllable-emergence models generally assume relatively stable task structures and predefined interaction rules, which limits their adaptability when multiple tasks arrive concurrently or when the network topology and available resources change over time.This study proposes an Adaptive Controllable Emergence (ACE) framework for multi-task air and space defense systems. The proposed framework extends graph-based multi-agent modeling and multi-agent reinforcement learning by introducing four coupled mechanisms: dynamic mission reconfiguration, resilient coordination, intelligent distributed decision-making, and dynamic resource allocation. The system is represented as a time-varying interaction graph in which sensing, decision, and execution agents dynamically modify their relationships according to mission requirements and resource availability. A decentralized partially observable Markov decision process is employed to formulate local decision-making under incomplete information. A multi-objective reward function jointly considers mission completion, coordination quality, resource utilization, network resilience, adaptation cost, and decision latency. Furthermore, a mission-reconfiguration mechanism is introduced to enable the system to modify task-agent assignments when the task set, network topology, or resource state changes. The resulting framework transforms controllable emergence from a static rule-design problem into an adaptive optimization process in which microscopic policies continuously modify macroscopic system behavior. The proposed mathematical formulation provides a basis for analyzing emergence quality, adaptation speed, coordination robustness, resource efficiency, and convergence. A simulation framework is also developed for evaluating the proposed architecture under static, dynamic, multi-task, and communication-degradation scenarios. The framework is intended as a general computational model for studying adaptive coordination in large-scale multi-agent systems rather than as a platform-specific operational defense procedure.