A useful capability is that of classifying some agent's behavior using data from a sequence, or trace, of sensor measurements. The sensor selection problem involves choosing a subset of available sensors to ensure that, when generated, observation traces will contain enough information to determine whether the agent's activities match some pattern. In generalizing prior work, this paper studies a formulation in which multiple behavioral itineraries may be supplied, with sensors selected to distinguish between behaviors. This allows one to pose fine grained questions, e.g., to position the agent's activity on a spectrum. In addition, with multiple itineraries, one can also ask about choices of sensors where some behavior is always plausibly concealed by (or mistaken for, or conflated with) another. Using sensor ambiguity to limit the acquisition of knowledge is a strong privacy guarantee, and one which some earlier work has examined. By concretely formulating privacy requirements for sensor selection, this paper connects both lines of work: privacy -- where there is a bound from above, and behavior verification -- where sensors are bounded from below. We examine the worst case computational complexity that results from both types of bounds, proving that upper bounds are more challenging under standard computational complexity assumptions. The problem is intractable in general, but we give a novel approach to solving this problem that can exploit interrelationships between constraints, and we see opportunities for a few optimizations. Case studies are presented to demonstrate the usefulness and scalability of our proposed solution, and to assess the impact of the optimizations.

翻译：一项实用的能力是利用传感器测量序列（即跟踪数据）来对智能体的行为进行分类。传感器选择问题涉及从可用传感器中挑选一个子集，以确保生成的观测轨迹包含足够信息，从而判断智能体的活动是否与某些模式匹配。在已有研究的基础上，本文探讨了一种多行为路径输入的方案，通过选择传感器来区分不同行为。这使我们能够提出细粒度的问题，例如将智能体活动定位到某个谱系上。此外，在存在多条路径的情况下，还可以探究某些行为是否始终能被另一种行为合理掩盖（或误判、混淆）的传感器选择方案。利用传感器的歧义性来限制知识获取是一种强大的隐私保障机制，此前已有研究对此进行了探讨。通过具体化传感器选择中的隐私需求，本文连接了两条研究脉络：隐私（要求传感器能力存在上限）与行为验证（要求传感器能力存在下限）。我们分析了同时受两种约束影响时最坏情况下的计算复杂度，证明在标准计算复杂度假设下上限约束更具挑战性。该问题总体上难以求解，但我们提出了一种新颖的求解方法，能够利用约束间的相互关联关系，并发现若干优化机会。最后通过案例研究验证了所提方案的实用性与可扩展性，并评估了优化措施的效果。