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Minimizing the Information Leakage Regarding High-Level Task Specifications

机译：最小化关于高级任务规范的信息泄漏

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We consider a scenario in which an autonomous agent carries out a mission in a stochastic environment while passively observed by an adversary. For the agent, minimizing the information leaked to the adversary regarding its high-level specification is critical in creating an informational advantage. We express the specification of the agent as a parametric linear temporal logic formula, measure the information leakage by the adversary’s confidence in the agent’s mission specification, and propose algorithms to synthesize a policy for the agent which minimizes the information leakage to the adversary. In the scenario considered, the adversary aims to infer the specification of the agent from a set of candidate specifications, each of which has an associated likelihood probability. The agent’s objective is to synthesize a policy that maximizes the entropy of the adversary’s likelihood distribution while satisfying its specification. We propose two approaches to solve the resulting synthesis problem. The first approach computes the exact satisfaction probabilities for each candidate specification, whereas the second approach utilizes the Fréchet inequalities to approximate them. For each approach, we formulate a mixed-integer program with a quasiconcave objective function. We solve the problem using a bisection algorithm. Finally, we compare the performance of both approaches on numerical simulations.
机译：我们考虑一种情景，其中自主代理在被敌人被动地观察的同时在随机环境中执行任务。对于代理商来说，最大限度地减少对其高级规范的对手泄露的信息对于创造信息优势至关重要。我们将代理商的规范称为参数线性时间逻辑公式，衡量对手对代理的任务规范的信心的信息泄漏，并提出算法综合代理的政策，这使得对对手的信息泄漏最小化。在考虑的场景中，对手旨在从一组候选规范推断代理的规范，其中每个候选规范具有相关的似然概率。代理人的目标是综合一项政策，以满足其规范的同时最大化对抗的兴奋性分配的熵。我们提出了两种方法来解决所产生的合成问题。第一方法计算每个候选规范的确切满意度概率，而第二种方法利用FRéchet不等式近似。对于每种方法，我们使用Quasiconcave目标函数制定混合整数程序。我们使用一分算法解决问题。最后，我们比较两种方法对数值模拟的性能。

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