Modeling Top-Down Perception and Analogical Transfer with Single Anticipatory Mechanism

A new approach to anticipations is proposed – anticipation by analogy. Firstly, the role of selective attention was explored both with simulation data and psychological experiment. After that, the AMBR model for analogy-making has been extended with a simple anticipatory mechanism and is demonstrated how top-down perception and analogical transfer can both be based on one and the same anticipatory mechanism. Finally, attention and action mechanisms were added to the model and AMBR was implemented in a real robot that behaves in a natural environment. The Importance of Anticipations Humans are anticipatory agents. They always have expectations about the world they live in (sometimes correct, sometimes wrong). Our everyday behavior is based on the implicit employment of predictive models. If, for example, we are looking for a certain book in an unknown room, we try to imagine where it could possibly be and then we go to look at this place. This is an example of anticipatory behavior as opposed to simple reactive behavior when we first see the object and then move towards it. There are few attempts to implement anticipatory behavior in computational models or in real robots. Typically, the researchers from the neural network approach use learning as a main mechanism for generating implicit or explicit models of the environment. The learned network weights represent these models and the result could be considered an anticipatory system. Examples for this type of anticipations are the ALVIN model (Pomerleau, 1989), which learns not only to respond to the environment but also to predict the observations to be seen in the next step and the Anticipatory Learning Classifier Systems (Stolzmann, 1998, Butz at al, 2002), which combine online reinforcement learning and model learning methods and can learn several reward maps. The combination of online generalizing model learning and reinforcement learning allows the investigation of diverse anticipatory mechanisms including multi-objective goals integrating different motivations. Another approach towards building anticipatory capacities is based on the DYNA-PI systems (Sutton, 1990). These systems are based on reinforcement learning systems that plan on the basis of a model of the world. Recently these models have been used to implement a neural network planner (Baldassarre, 2002) that is capable of finding efficient start – goal paths, and deciding to re-plan if “unexpected” states are encountered. Planning iteratively generates “chains of predictions” starting from the current state and using the model of the environment. This model is a neural network trained to predict the next input when an