Jason Intentional Learning: An Operational Semantics

This paper introduces an operational semantics for defining Intentional Learning on Jason, the well known Java-based implementation of AgentSpeak(L). This semantics enables Jason to define agents capable of learning the reasons for adopting intentions based on their own experience. In this work, the use of the term Intentional Learning is strictly circumscribed to the practical rationality theory where plans are predefined and the target of the learning processes is to learn the reasons to adopt them as intentions. Top-Down Induction of Logical Decision Trees (TILDE) has proved to be a suitable mechanism for supporting learning on Jason: the first-order representation of TILDE is adequate to form training examples as sets of beliefs, while the obtained hypothesis is useful for updating the plans of the agents.