Toward a Computational Model of "Context"

Virtual and robotic agents must be able to understand "communicative acts" (utterances, gestures, controlled facial expressions etc.) if they are to interact and collaborate with humans. For researchers in AI, HCI, HRI and related fields, automatic comprehension of communicative acts has turned out to be a very tough nut to crack. Drawing on recent research from cognitive science and evolutionary psychology, the paper argues that an insufficient conceptualization of "context" is at the heart of this problem, and that we should focus on very simple, non-linguistic communicative acts (pointing gestures etc.) in order to investigate how agents can comprehend communicative acts in realistic contexts. I propose a tripartite model of context which is informed by experimental research on how humans recognize objects (via "affordances"), causal relations among objects, and the collaborative activities of fellow-humans. The model is not a formal one, but detailed enough to help in the development of comprehension algorithms in future research.