The Rocky Road from Hume to Kant: Correlations and Theories in Robots and Animals

Abstract

This essay will address the problem of prediction. Prediction is at its root concerned with the idea of causation. The notion of how causal relationships can be represented in minds has been an important thread in Sloman’s work, and ongoing conversations with him have influenced my thinking. This paper will examine several aspects of prediction and causation. First it examines reasons why animals and machines benefit from being able to predict, and the consequent requirements on prediction mechanisms. Next it will examine some actual machines that we have synthesised for predicting the effects a robot manipulator has on an object it pushes. These mechanisms contain varying amounts of prior knowledge. This leads to the issue of whether and how predicting machines benefit from prior knowledge, and whether a prediction mechanism is equivalent in any sense to the notion of a theory. The paper will reach a point where I claim that given the constraints faced by animals and robots it is often better to construct many micro-theories rather one macro-theory. The idea of theories will also lead to the examination of the notion of levels of description in theory building. This will lead in turn to consider whether hierarchies of increasingly abstract prediction machines can lead to better robots, and to better understanding of animals.