Urban warfare presents complex and dynamic challenges for modern military operations, demanding adaptive and responsive support systems. This study presents a Battle Management System (BMS) based on a Wireless Sensor Network (WSN) integrated with an Artificial Intelligence (AI)-powered planner to address key operational issues such as task coordination, resource allocation, mobility management, energy optimization, and communication resilience. Unlike prior works, our study introduces a tightly coupled integration where real-time sensor data dynamically influences AI-generated plans, allowing mission adaptation under battlefield constraints. Our study covers the practical implementation of this AI-enhanced BMS, a showcase of its capabilities through comprehensive urban warfare simulations, its real-world validation at a military facility demonstrating its effectiveness in an operational setting, and a performance benchmarking against state-of-the-art planners, showing superior results in mission success, risk reduction, and resource efficiency. Our findings confirm that AI-driven planning, in conjunction with WSNs, can generate operational plans that address the unique demands of urban combat scenarios with high precision and efficiency.

Background: In the field of Automated Planning, the existence of multiple agents in a certain temporal setting introduces the possibility of concurrency. This severely increases the complexity of planning in multi-agent temporal scenarios, as the possible states in the search space grow exponentially. Objectives: These types of domains are traditionally solved by making use of temporal reasoning techniques that do not directly address the “multi-agent nature” at their core. In contrast, we introduce MA-LAMA, a multi-agent temporal planner that makes use of multi-agent techniques to deal with the inherent complexity of multi-agent temporal scenarios. Methods: We propose a sequenced framework in which several multi-agent planning techniques are applied: automatic agent detection, task decomposition, cost-informed goal assignment, and agent interaction analysis; that are aimed to reduce the search complexity when solving temporal planning tasks. Results: Our results show that, in many cases, MA-LAMA outperforms other state-of-the-art temporal planners in plan cost optimization for well-known temporal domains. Conclusions: These results, along with the fact that MA-LAMA does not incorporate any temporal reasoning during search, suggest that several widely considered temporal domains are best suited to be solved with multi-agent planning techniques, rather than with temporal reasoning.

Several multi-agent techniques are utilized to reduce the complexity of classical planning tasks, however, their applicability to temporal planning domains is a currently open line of study in the field of Automated Planning. In this paper, we present MA-LAMA, a factored, centralized, unthreated, satisfying, multi-agent temporal planner, that exploits the 'multi-agent nature' of temporal domains to perform plan optimization. In MA-LAMA, temporal tasks are translated to the constrained snap-actions paradigm, and an automatic agent decomposition, goal assignment, and required cooperation analysis are carried out to build independent search steps, called Search Phases. These Search Phases are then solved by consecutive agent local searches, using classical heuristics and temporal constraints. Experiments show that MA-LAMA is able to solve a wide range of classical and temporal multi-agent domains, performing significantly better in plan quality than other state-of-the-art temporal planners.

Military tactical scenarios have been shifting to more often consider combat situations in urban environments. Threats in these environments are generally more dynamic in nature, imposing new requirements on sensors and communications systems that support military operations. Wireless sensor networks (WSNs) with a large number of small and mobile computing nodes became the typical solution. However, WSNs demand additional complexity to dynamically manage their tasks, resource allocation, mobility, power consumption, and communication. This paper illustrates the integration of AI techniques into a Battle Management System (BMS) to support military operations in urban environments. The BMS is enhanced with an AI-based planner able to plan tasks, allocate resources, and monitor the WSN operation. The planner takes into consideration energy harvesting capabilities, secure data transfer, and authorization procedures. It generates plans using the information received from the sensors. In case new situations emerge, based on data fusion information, it automatically replans to adapt to the uncertainty in the environment. Finally, it takes into account the coverage between the different components to optimize the communications and better support WSN’s operator(s) and their activities.