# A Lookahead Behavior Model for Multi-Agent Hybrid Simulation
## 6 Discussions
### Applicable Scope  
As mentioned above, the sense, think, and act steps in the LBM model can simulate the sensor detection, commander decision-making, and platform mobility plus weapon lethality, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Aspects** & **Original TBM** & **Delayed TBM** & **LBM** \\ \hline Steps contained in & & Sense, think, and act & Sense, think, lookahead, and act \\ \hline The relationship of & Serial in a cycle, & Serial in a cycle with arbitrary & Think/sense can be skipped, \\ steps & indispensible for & delays between two steps, & indispensible for lookahead \\  & each step & indispensible for think step & before each action \\ \hline The relationship of & & & \\ cycles & Fixed time interval & Arbitrary delay & Arbitrary \\ \hline Ability to conduct & & & \\ simultaneously & No & Yes (except for think step) & Yes \\ \hline Number of updates & More & More & Less \\ \hline Number of & & & \\ resynchronizations & More & More & Less \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparisons of LBM with original TBM and delayed TBM (LBM: lookahead behavior model, TBM: traditional agent behavior model).  
At the same time, the implicit modeling-based discrete method is used in modeling the dynamics of the system to realize the state transition of the continuous model (such as the movement process). In the lookahead step, the LBM predicts possible interaction events within the time window, according to the actions the agents would take and the corresponding implicit models.  
Moreover, LBM can simulate semi-autonomous entities in the military field, or skip the sense step to simulate entities whose self-perceiving capabilities can be ignored in the battlefields. This shows that LBM has the ability to model complex military systems, which include continuous and discrete models.  
The implementation of LBM-PR makes LBM take full advantage of the efficient scheduling of DES. This advantage is even more pronounced in systems where the scale is large and the agent behaviors do not change frequently.  
However, there are also some disadvantages in efficiency. The event scheduler itself has some overhead in the event scheduling and cancellation. If the frequent changes in behavior lead to the cancellation of a large number of events, it will not only bring additional operating costs for the event scheduler, but also increase the number of invalid lookaheads.  
The lookahead process in LBM is closely related to the model. In practice, it is often necessary to simplify the model or optimize the lookahead process according to the research content. In the example discussed in this article, the movement of the MovingAgent is sufficiently simplified, otherwise the overhead of the lookahead itself will increase.  
Compared to other methods, LBM increases the difficulty in modeling. Specifically, the time window-based lookahead needs to pre-sort all possible types of events, as well as all possible interactions between agents. The estimated value-based unlock requires the pre-recognition of all possible CDs among all agents, and an estimated state value set or a state prediction function is suggested to be prepared in advance of the simulation. In systems that require a large number of run iterations for decision-making, the estimated value needed to resolve the cyclic dependency should be derived from some statistical values in practice.