Exploring Apprenticeship Learning for Player Modelling in Interactive Narratives
This work addresses the need for systems to aid in designing and personalizing interactive narratives, which is incremental as it applies an existing method to a new domain.
The paper tackled the problem of simulating player behavior in interactive narratives by using Receding Horizon Inverse Reinforcement Learning (RHIRL) to learn reward functions and derive policies, with preliminary results showing RHIRL can learn action sequences to complete a game and generate behavior similar to specific players.
In this paper we present an early Apprenticeship Learning approach to mimic the behaviour of different players in a short adaption of the interactive fiction Anchorhead. Our motivation is the need to understand and simulate player behaviour to create systems to aid the design and personalisation of Interactive Narratives (INs). INs are partially observable for the players and their goals are dynamic as a result. We used Receding Horizon IRL (RHIRL) to learn players' goals in the form of reward functions, and derive policies to imitate their behaviour. Our preliminary results suggest that RHIRL is able to learn action sequences to complete a game, and provided insights towards generating behaviour more similar to specific players.