Interactive Verification of Game Design and Playing Strategies

Authors:
Dimitris Kalles;Eirini Ntoutsi
Affiliations:
-;-
Venue:
ICTAI '02 Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence
Year:
2002

Citing 0
Cited 4

Player Co-Modelling in a Strategy Board Game: Discovering How to Play Fast

Cybernetics and Systems
Evolving Computer Game Playing via Human-Computer Interaction: Machine Learning Tools in the Knowledge Engineering Life-Cycle

Proceedings of the 2008 conference on Knowledge-Based Software Engineering: Proceedings of the Eighth Joint Conference on Knowledge-Based Software Engineering
Time does not always buy quality in co-evolutionary learning

SETN'10 Proceedings of the 6th Hellenic conference on Artificial Intelligence: theories, models and applications
Building a social multi-agent system simulation management toolbox

Proceedings of the 6th Balkan Conference in Informatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Reinforcement learning is considered as one of the most suitable and prominent methods for solving game problems due to its capability to discover good strategies by extended self-training and limited initial knowledge In this paper we elaborate on using reinforcementlearning for verifying game designs and playing strategies. Specifically, we examine a new strategy game that has been trained on self-playing games and analyze the game performance after human interaction. We demonstrate, through selected game instances, the impact of human interference to the learning process, and eventually the game design.