Inferring Agent's Goals from Observing Successful Traces

Abstract

Intention recognition is the task of inferring the intentions and goals of an agent. Intention recognition has many applications. Especially, it can be very useful in the context of intelligent personal assistants like robots or mobile applications, smart environments, and monitoring user needs. We present a method to infer the possible goals of an agent by observing him in a series of successful attempts to reach them. We model this problem as a case of concept learning and propose an algorithm to produce concise hypotheses. However, this first proposal does not take into account the sequential nature of our observations and we discuss how we can infer better hypotheses when we can make some assumption about the behavior of the agents and use background knowledge about the dynamics of the environment. Then we talk about future work to improve our method.