# A Lookahead Behavior Model for Multi-Agent Hybrid Simulation
## 1 Introduction  
Agent-based modeling and simulation has become the primary means of studying complex adaptive systems (CAS). It constructs the basic elements in the system as agents. Through the agents and interactions among them, it generates system changes and, therefore, forms a bottom-up perspective for people to simulate and research the emergent behavior of the system, where the nature of the system can be understood much better. The behavior model of a typical agent can be built using a generic \"sense-think-act\" paradigm [1; 2; 3; 4] in multi-agent simulation (MAS). The steps in this paradigm include the agent's process of perceiving the environment, making decisions, and acting, which correspond to the sense-think-act cycle, respectively. MAS can be applied not only to predict the behaviors of simple systems under specific conditions, but also to research the statistical trends in complex systems [5; 6; 7; 8; 9; 10; 11]. A number of typical applications in this work include disaster emergency management [12], traffic management [13; 14], military online decision-making [15; 16], and so on. These applications adjust the uncertain parameters by a set of stochastic numbers to obtain a large sample space for iterative calculations [17], so as to evaluate various plans and finally obtain several near-optimal solutions for decision-makers. However, in the simulation of complex systems, the typical time-stepped sense-think-act cycle takes a great deal more time when agents--and thus their interactions--are enormous. For example, the computing efficiency of MANA (map-aware non-uniform automata) system [18] for combatoperations, when the number of agents reaches 600, will be greatly reduced [19]. In military applications, the system usually consists of a large number of autonomous or semi-autonomous combat units, which require large-scale agents. This large-scale simulation, even in one iteration, would consume a great deal of time, rendering the time needed for optimal planning selection unable to be reasonably controlled. Therefore, the efficiency of the MAS becomes the vital aspect to be considered in a large-scale complex simulation system, especially in simulation-based optimization applications. A common approach proposed to improve the computational efficiency is hybrid agent-based simulation, which introduces discrete event simulation (DES) into agent-based models [20; 21; 22; 23]. As the discrete event-based approach does not need to scan the simulation model continuously when the system state stays unchanged, it is generally believed that the efficiency of the scheduling strategy based on discrete events is higher than that of the time-stepped method of periodic scanning [24]. It has been found that MAS and DES become a pair of contradictions for continuous models in the ways of state updating and the predictability of the agent behavior [25; 26; 27]. Complex systems in the real world are mixtures of continuous and discrete systems [28; 29]. There is often a need to retain the continuity of the object characteristics so as to capture the system dynamics. Thus, the time-stepped simulation is much more applicable in most typical MAS. Therefore, the matter of how to update the agent state is the main focus in building a hybrid simulation. According to its ways of implementation, the state update mechanism can be divided into fixed time synchronization (FS), variable time synchronization (VS), optimistic synchronization (OS), complete resynchronization (CR), and partial resynchronization (PR), which will be discussed in detail in Section 2. Among them, PR is an outstanding method in hybrid simulation with fewer updates and sync numbers. As shown in Reference [20], the PR method can greatly improve the computational efficiency in the National Airspace System when the agent requires frequent interaction and the state update time changes considerably.