Abstract
Robots share a conundrum central to all intelligence. Like humans, robots not only must address novel situations but also must start from what they already know: how, then, can any robot deal with novelty? In this chapter, we examine two cognitive strategies for addressing novelty: analogy and metareasoning. Analogy addresses new problems in a manner similar to a familiar problem; the familiar problem is part of the context of addressing the new problem. We show how a cognitive robot may use analogy to learn from a small number of initial demonstrations. However, analogical reasoning and reasoning more generally do not necessarily guarantee success in an open, dynamic world. This brings metareasoning into play. We show how a cognitive robot may use metareasoning to recover and learn from failure; here, failure forms part of the context for metareasoning. In the other direction, we discuss how these experiments in robot learning inform the development of cognitive theories of analogy and metareasoning.
Analogy and metareasoning: Cognitive strategies for robot learning
