Hierarchical Multi-Agent Reinforcement Learning for Air Combat Maneuvering

The application of artificial intelligence to simulate air-to-air combat scenarios is attracting increasing attention. To date the high-dimensional state and action spaces, the high complexity of situation information (such as imperfect and filtered information, stochasticity, incomplete knowledge about mission targets) and the nonlinear flight dynamics pose significant challenges for accurate air combat decision-making. These challenges are exacerbated when multiple heterogeneous agents are involved. We propose a hierarchical multi-agent reinforcement learning framework for air-to-air combat with multiple heterogeneous agents. In our framework, the decision-making process is divided into two stages of abstraction, where heterogeneous low-level policies control the action of individual units, and a high-level commander policy issues macro commands given the overall mission targets. Low-level policies are trained for accurate unit combat control. Their training is organized in a learning curriculum with increasingly complex training scenarios and league-based self-play. The commander policy is trained on mission targets given pre-trained low-level policies. The empirical validation advocates the advantages of our design choices.Comment: 22nd International Conference on Machine Learning and Applications (ICMLA 23

Command Agent Belief Architecture to Support Commander Decision Making in Military Simulation

In the war, military conflicts have many aspects that are consistent with complexity theory e.g., the higher commander’s decision is directed at animate entity that react under hierarchical and self-organised structure in decentralised command and control for the collectivist dynamism of decomposed elements due to nonlinear complexity of warfare on the battlefield. Agent technology have been found to be suitable for modelling tactical behaviour of entities at multiple level of resolution under hierarchical command and control (C2) structure and provide a powerful abstraction mechanism required for designing simulations of complex and dynamic battlefield situations. Intelligent agents can potentially reduce the overhead on such experiments and studies. Command agents, plan how to carry out the operation and assign tasks to subordinate agents. They receive information from battlefield environment and use such information to build situation awareness and also to respond to unforeseen situations. In the paper, we have proposed a mechanism for modelling tactical behaviour of an intelligent agent by which higher command level entities should be able to synthesize their beliefs derived from the lower level sub ordinates entities. This paper presents a role-based belief, desire and intention mechanism to facilitate in the representation of military hierarchy, modelling of tactical behaviour based on agent current belief, teammate’s belief propagation, and coordination issues. Higher commander can view the battlefield information at different levels of abstraction based on concept of aggregation and disaggregation and take appropriate reactive response to any unforeseen circumstances happening in battlefield

Asimovian Adaptive Agents

The goal of this research is to develop agents that are adaptive and predictable and timely. At first blush, these three requirements seem contradictory. For example, adaptation risks introducing undesirable side effects, thereby making agents' behavior less predictable. Furthermore, although formal verification can assist in ensuring behavioral predictability, it is known to be time-consuming. Our solution to the challenge of satisfying all three requirements is the following. Agents have finite-state automaton plans, which are adapted online via evolutionary learning (perturbation) operators. To ensure that critical behavioral constraints are always satisfied, agents' plans are first formally verified. They are then reverified after every adaptation. If reverification concludes that constraints are violated, the plans are repaired. The main objective of this paper is to improve the efficiency of reverification after learning, so that agents have a sufficiently rapid response time. We present two solutions: positive results that certain learning operators are a priori guaranteed to preserve useful classes of behavioral assurance constraints (which implies that no reverification is needed for these operators), and efficient incremental reverification algorithms for those learning operators that have negative a priori results

SCALING REINFORCEMENT LEARNING THROUGH FEUDAL MULTI-AGENT HIERARCHY

Militaries conduct wargames for training, planning, and research purposes. Artificial intelligence (AI) can improve military wargaming by reducing costs, speeding up the decision-making process, and offering new insights. Previous researchers explored using reinforcement learning (RL) for wargaming based on the successful use of RL for other human competitive games. While previous research has demonstrated that an RL agent can generate combat behavior, those experiments have been limited to small-scale wargames. This thesis investigates the feasibility and acceptability of -scaling hierarchical reinforcement learning (HRL) to support integrating AI into large military wargames. Additionally, this thesis also investigates potential complications that arise when replacing the opposing force with an intelligent agent by exploring the ways in which an intelligent agent can cause a wargame to fail. The resources required to train a feudal multi-agent hierarchy (FMH) and a standard RL agent and their effectiveness are compared in increasingly complicated wargames. While FMH fails to demonstrate the performance required for large wargames, it offers insight for future HRL research. Finally, the Department of Defense verification, validation, and accreditation process is proposed as a method to ensure that any future AI application applied to wargames are suitable.Lieutenant Colonel, United States ArmyApproved for public release. Distribution is unlimited

A Review of Platforms for the Development of Agent Systems

Agent-based computing is an active field of research with the goal of building autonomous software of hardware entities. This task is often facilitated by the use of dedicated, specialized frameworks. For almost thirty years, many such agent platforms have been developed. Meanwhile, some of them have been abandoned, others continue their development and new platforms are released. This paper presents a up-to-date review of the existing agent platforms and also a historical perspective of this domain. It aims to serve as a reference point for people interested in developing agent systems. This work details the main characteristics of the included agent platforms, together with links to specific projects where they have been used. It distinguishes between the active platforms and those no longer under development or with unclear status. It also classifies the agent platforms as general purpose ones, free or commercial, and specialized ones, which can be used for particular types of applications.Comment: 40 pages, 2 figures, 9 tables, 83 reference