Home

About

Advanced Search

Browse by Discipline

Scientific Societies

E-print Alerts

Add E-prints

E-print Network
FAQHELPSITE MAPCONTACT US


  Advanced Search  

 
Bayesian Models for Keyhole Plan Recognition in an Adventure Game
 

Summary: Bayesian Models for Keyhole Plan Recognition in
an Adventure Game
DAVID W. ALBRECHT, INGRID ZUKERMAN and ANN E. NICHOLSON
Department of Computer Science, Monash University
Clayton, VICTORIA 3168, AUSTRALIA
fdwa,ingrid,annng@cs.monash.edu.au
Abstract. We present an approach to keyhole plan recognition which uses a dynamic belief (Bayesian)
network to represent features of the domain that are needed to identify users' plans and goals. The
application domain is a Multi­User Dungeon adventure game with thousands of possible actions
and locations. We propose several network structures which represent the relations in the domain to
varying extents, and compare their predictive power for predicting a user's current goal, next action
and next location. The conditional probability distributions for each network are learned during a
training phase, which dynamically builds these probabilities from observations of user behaviour.
This approach allows the use of incomplete, sparse and noisy data during both training and testing.
We then apply simple abstraction and learning techniques in order to speed up the performance of
the most promising dynamic belief networks without a significant change in the accuracy of goal
predictions. Our experimental results in the application domain show a high degree of predictive
accuracy. This indicates that dynamic belief networks in general show promise for predicting a
variety of behaviours in domains which have similar features to those of our domain, while reduced
models, obtained by means of learning and abstraction, show promise for efficient goal prediction in

  

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