It knows what you're going to do: adding anticipation to a Quakebot
Proceedings of the fifth international conference on Autonomous agents
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
An Experimental Study of Anticipation in Simple Robot Navigation
Anticipatory Behavior in Adaptive Learning Systems
Prediction Time in Anticipatory Systems
Anticipatory Behavior in Adaptive Learning Systems
Ikaros: Building cognitive models for robots
Advanced Engineering Informatics
An interacting multiple model algorithm with a switching Markov chain
Mathematical and Computer Modelling: An International Journal
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We investigating the role of anticipation and attention in a dynamic environment in a number of large scale simulations of an agent that tries to negotiate a number of gates that continuously open and close. In particular we have looked at learning mechanisms that can predict the future positions of the gates and control strategies that will allow the agent to pass through the gates unharmed. The simulations reported below use the AARC architecture [1]. This architecture combines a large number of different cognitive mechanisms. In Experiment 1, the task for the agent is to pass through a single gate and in Experiment 2, to pass through three successive gates. The results shows that the AARC architecture is flexible enough to handle very diverse situations. It is also somewhat surprising that linear predictors are sufficient in most cases.