DogMate - Intent Recognition through Anticipation

Sidekicks generally lack the support to understand a fundamental character in their world -- the player's avatar. This has a negative impact on their behavioural believability. In this work, we present an approach to detect the intent underlying certain actions of the player. We begin by introducing synthetic characters and sidekicks in particular, emphasising on their believability. We then review related works which enhance the believability of characters by anticipating the actions of other characters. To understand the reason underlying their actions, we model the relation between intention and action and then proceed on elaborating a framework that can interpret the intent of an action based on an anticipatory mechanism. We then present a test-case in which our framework is used to create an architecture controlling the sidekick of the player. Finally, we discuss three aspects of our evaluation: the player'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.