It is well understood that mental modeling forms the foundation of many everyday interactions between humans. This includes both collaborative and deceptive interactions. One could argue that the modeling and manipulation of mental states lies at the heart of effective deception. In this paper, we examine the security problem of insider threat attacks. In this case, an adversary has already infiltrated an organization. The primary challenge for this attacker is to avoid suspicion until their true goal can be achieved. We see how existing model-based explanatory methods can be leveraged to generate lies that explain away potentially suspicious activities. We also propose a novel planning formulation which generates plans that appear to achieve an assigned goal while getting close enough to reach an alternative, covert goal. We evaluate our method through computational experiments and a user study.

We present a five-year retrospective on the development of the VoxWorld platform, first introduced as a multimodal platform for modeling motion language, that has evolved into a platform for rapidly building and deploying embodied agents with contextual and situational awareness, capable of interacting with humans in multiple modalities, and exploring their environments. In particular, we discuss the evolution from the theoretical underpinnings of the VoxML modeling language to a platform that accommodates both neural and symbolic inputs to build agents capable of multimodal interaction and hybrid reasoning. We focus on three distinct agent implementations and the functionality needed to accommodate all of them: Diana, a virtual collaborative agent; Kirby, a mobile robot; and BabyBAW, an agent who self-guides its own exploration of the world.