ONASI: Online Agent Modeling Using a Scalable Markov Model
Priyath T. Sandanayake, Diane J. Cook · International Journal of Pattern Recognition and Artificial Intelligence · 2003
Human and software agents exhibit regularities in their activities. We describe our ONASI algorithm, which derives a Markov model from such observed regularities, and dynamically scales the model through merging and splitting of states. ONASI uses this model to predict the agent's next action. We evaluate ONASI's predictive accuracy on a dataset of Wumpus World games, and demonstrate from this approach that the model can correctly predict the agent's next action with adjustable computation and memory resources. These predictions can be used to imitate, assist or obstruct an agent.