Unified behavior framework in discrete event simulation systems

Abstract

Intelligent agents provide simulations a means to add lifelike behavior in place of manned entities. When implemented, typically a single intelligent agent model (or approach to defining decision making), such as rule-based, behavior trees, neural networks, etc., is selected. This choice introduces restrictions into what behaviors agents can manifest, and can require significant testing in edge cases. This paper presents the incorporation and application of the Unified Behavior Framework (UBF) into the Advanced Framework for Simulation, Integration, and Modeling environment. The UBF provides the flexibility to implement any and all behavior-based systems, allowing the developer to rapidly assemble a decision making agent that leverages multiple paradigms or approaches. The UBF achieves this by leveraging several key software engineering principles: modular design, scalability through reduced code complexity, simplified development and testing through abstraction, and the promotion of code reuse. The use of UBF to define intelligent agents within a 2v2 Integrated Air Defense System is demonstrated.

Formation control method based on artificial potential fields for aircraft flight simulation

“…In the context of military simulation, usually the goal of autonomous entities is to represent lifelike intelligence, therefore requiring agents to be able to handle ever-increasing complex tasks and situations, resulting in both their design and overall time in development to also grow considerably. 44…”

“…Thus, there is no way to combine the action execution of different behaviors, what is much desired as it allows for new behaviors to emerge from existing ones, without the user having to implement a new predefined behavior, therefore reducing code complexity. 45…”

“…In the context of military simulation, usually the goal of autonomous entities is to represent lifelike intelligence, therefore requiring agents to be able to handle everincreasing complex tasks and situations, resulting in both their design and overall time in development to also grow considerably. 44 Scientists have proposed many artificial intelligence methods to cope with these increasing complexities that the simulated scenarios pose in the behavioral standpoint. However, most of them struggle with blending action outputs of behaviors that are acting upon the same entity.…”

“…Thus, there is no way to combine the action execution of different behaviors, what is much desired as it allows for new behaviors to emerge from existing ones, without the user having to implement a new predefined behavior, therefore reducing code complexity. 45 We have identified the Unified Behavior Framework (UBF) 46 as an effective means to do this, as the behaviors in UBF do not execute the action for the agent, instead they only produce an action object comprised of parameters, which user-defined arbiters are able to manipulate and fuse together.…”