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Autonomous virtual agents generally lack the support to understand a fundamental character in their world --- the user's avatar. This has a negative impact on their behavioural believability. In this paper, we present an approach to detect the intent underlying certain actions of the user. We begin by introducing the relation between intention and action, then proceed on elaborating a framework that can interpret the intent of an action based on the matching and mismatching of anticipated behaviour. We then present a test-case in which our framework is used to create an agent architecture controlling a virtual dog that interacts with the user within a virtual world. Finally, we discuss three aspects of our evaluation: the user's intent recognition, its interpretation and the solution's efficiency. Our results suggest that our solution can be used to detect certain intents and, in such cases, perform similarly to a human observer, with no impact on computational performance.

I mean it!: detecting user intentions to create believable behaviour for virtual agents in games