  The ability to continuously learn and adapt to new situations is one where
humans are far superior compared to AI agents. We propose an approach to
knowledge transfer using behavioural strategies as a form of transferable
knowledge influenced by the human cognitive ability to develop strategies. A
strategy is defined as a partial sequence of events - where an event is both
the result of an agent's action and changes in state - to reach some predefined
event of interest. This information acts as guidance or a partial solution that
an agent can generalise and use to make predictions about how to handle unknown
observed phenomena. As a first step toward this goal, we develop a method for
extracting strategies from an agent's existing knowledge that can be applied in
multiple contexts. Our method combines observed event frequency information
with local sequence alignment techniques to find patterns of significance that
form a strategy. We show that our method can identify plausible strategies in
three environments: Pacman, Bank Heist and a dungeon-crawling video game. Our
evaluation serves as a promising first step toward extracting knowledge for
generalisation and, ultimately, transfer learning.
