基于线性时序逻辑的智能体不确定行为规划

With the increase of people's demand for agents, the activities of agents are no longer limited to simple environment and task format. Facing complex application scenarios, agents need to be able to make decisions and execute them autonomously. This paper studies the probabilistic action planning considering complex task constraints described by linear temporal logic. At the same time, the success rate and cost of task are both considered. The uncertain factors include agent behavior and environment attributes, and the task description is expressed by soft and hard constraints. The strategy of agent is generated here applying model checking in formal method. Single- and multi-agent model is established using Markov decision process, while task model is established using double-layer automata. Then, agent-task network model is designed to describe the constraints and the control strategy is solved through a coupled linear programming. The method above is verified through numerical simulation. The results show that the complex task constraints in the form of soft and hard constraints can be satisfied. The optimal strategy enable the agent to complete the task according to the constraint strength, and the control strategy can be adjusted by controlling the relaxation degree of control network model relevant to the penalty factor.