No abstract

No abstract

Abstract:In the military field, multi-agent simulation (MAS) plays an important role in studying wars statistically. For a military simulation system, which involves large-scale entities and generates a very large number of interactions during the runtime, the issue of how to improve the running efficiency is of great concern for researchers. Current solutions mainly use hybrid simulation to gain fewer updates and synchronizations, where some important continuous models are maintained implicitly to keep the system dynamics, and partial resynchronization (PR) is chosen as the preferable state update mechanism. However, problems, such as resynchronization interval selection and cyclic dependency, remain unsolved in PR, which easily lead to low update efficiency and infinite looping of the state update process. To address these problems, this paper proposes a lookahead behavior model (LBM) to implement a PR-based hybrid simulation. In LBM, a minimal safe time window is used to predict the interactions between implicit models, upon which the resynchronization interval can be efficiently determined. Moreover, the LBM gives an estimated state value in the lookahead process so as to break the state-dependent cycle. The simulation results show that, compared with traditional mechanisms, LBM requires fewer updates and synchronizations.

This paper investigates the mean square bipartite consensus problem of a multi-agent system with considering measurement noises and communication time-delays. And the interaction topology of the multi-agent system (MAS) is a signed digraph. The convergence of the proposed bipartite consensus protocol with both noisy measurements and uniform communication time-delays is analyzed. Sufficient conditions on the upper bound of the time-delays are first derived to guarantee the mean square bipartite consensus for the signed digraph which is structurally balanced and has a spanning tree. Then for the signed digraph which is structurally unbalanced and strongly connected, the states of all agents achieve zero in the mean square sense under the given conditions. Finally, numerical simulation examples are provided to illustrate the obtained theoretical results.

No abstract