Abstract
Consider an agent acting to achieve its temporal goal, but with a "trembling hand". In this case, the agent may mistakenly instruct, with a certain (typically small) probability, actions that are not intended due to faults or imprecision in its action selection mechanism, thereby leading to possible goal failure. We study the trembling-hand problem in the context of reasoning about actions and planning for temporally extended goals expressed in Linear Temporal Logic on finite traces (LTLf), where we want to synthesize a strategy (aka plan) that maximizes the probability of satisfying the LTLf goal in spite of the trembling hand. We consider both deterministic and nondeterministic (adversarial) domains. We propose solution techniques for both cases by relying respectively on Markov Decision Processes and on Markov Decision Processes with Set-valued Transitions with LTLf objectives, where the set-valued probabilistic transitions capture both the nondeterminism from the environment and the possible action instruction errors from the agent. We formally show the correctness of our solution techniques and demonstrate their effectiveness experimentally through a proof-of-concept implementation.
Abstract (translated)
考虑一个动作来实现其时间目标，但带着“颤抖的手”。在这种情况下，代理商可能会错误地指定一定概率的非意图行动，由于其动作选择机制的故障或粗略而导致的，从而导致可能的目标失败。我们在有限痕迹（LTLf）下对动作进行推理和规划的背景下研究颤抖手问题，我们试图合成一个策略（即计划），使其最大概率地满足LTLf目标，即使代理商犯错误。我们研究了确定性和非确定性（对抗）域。我们分别依赖马尔可夫决策过程和具有LTLf目标的可设值转移的马尔可夫决策过程来提出解决方案，其中集合值概率转移捕捉了环境中的不确定性和代理商的可能的指令错误。我们通过形式化证明展示了我们解决方案的正确性，并通过实验验证了它们的有效性。
URL
https://arxiv.org/abs/2404.16163
