Summary: Knowledge Acquisition for Goal Prediction in a
Multi­User Adventure Game
D.W. Albrecht, A.E. Nicholson and I. Zukerman
Department of Computer Science, Monash University
Clayton, VICTORIA 3168, Australia
phone: +61 3 9905­5225 fax: +61 3 9905­5146
fdwa,annn,ingridg@cs.monash.edu.au
Abstract. We present an approach to goal recognition which uses a Dynamic
Belief Network to represent domain features needed to identify users' goals and
plans. Different network structures have been developed, and their conditional
probability distributions have been automatically acquired from training data.
These networks show a high degree of accuracy in predicting users' goals. Our
approach allows the use of incomplete, sparse and noisy data during both training
and testing. We then apply simple learning techniques to learn significant actions
in the domain. This speeds up the performance of the most promising dynamic
belief networks without loss in predictive accuracy.
1 Introduction
In this paper, we apply a Dynamic Belief Network (DBN) formalism for knowledge
representation and reasoning to predict users' goals, actions and locations in an adven­
ture game called the ``Shattered Worlds'' Multi­User Dungeon (MUD) (Section 2), and

Source: Albrecht, David - Caulfield School of Information Technology, Monash University
Nicholson, Ann - School of Computer Science and Software Engineering, Monash University

Collections: Computer Technologies and Information Sciences