We formalise and study multi-agent timed models MAPTs (Multi-Agent with Periodic timed Tasks), where each agent is associated with a regular timed schema upon which all possible actions of the agent rely. MAPTs allow for an accelerated semantics and a layered structure of the state space, so that it is possible to explore the latter dynamically and use heuristics to greatly reduce the computation time needed to address reachability problems. We use an available tool for the Petri net implementation of MAPTs, to explore the state space of autonomous vehicle systems. Then, we compare this exploration with timed automata-based approaches in terms of expressiveness of available queries and computation time.

中文翻译：

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具有周期性定时任务的多智能体系统的动态探索

我们形式化并研究了多代理定时模型 MAPT（具有周期性定时任务的多代理），其中每个代理都与一个规则的定时模式相关联，代理的所有可能的动作都依赖于该模式。MAPT 允许加速语义和状态空间的分层结构，因此可以动态地探索后者并使用启发式算法来大大减少解决可达性问题所需的计算时间。我们使用一个可用的工具来实现 MAPT 的 Petri 网，以探索自动驾驶汽车系统的状态空间。然后，我们在可用查询的表达能力和计算时间方面将这种探索与基于定时自动机的方法进行比较。