Extended Goal Recognition: A Planning-Based Model for Strategic Deception.

Goal recognition is the problem of determining an agent’s intent by observing its actions. In the context of AI research, the problem is tackled for two quite different purposes: to determine an agent’s most probable goal or, for human-aware planning including planned—or strategic—deception, to determine an observer’s most likely belief about that goal. Making no distinction, contemporary models tend to assume an infallible observer, deceived only while it has limited access to information or if the environment itself is only partially observable. Focusing on the second purpose, we propose an extended framework that incorporates formal definitions of confirmation bias, selective attention and memory decay. In contrast to pre-existing models, our approach combines explicit consideration of prior probabilities with a principled representation of observer confidence and distinguishes between potential observations—i.e., every observable event within the observer’s frame of reference—and recalled observations which we model as a function of attention and memory. We show that when these factors are taken into consideration, false beliefs may arise and can be made to persist, even in a fully observable environment—thus providing a perceptual model readily incorporated into the “thinking” of an adversarial agent for the purpose of strategic deception.