While it is commonly agreed that analogy is useful in human problem solving, exactly how analogy can and should be used remains an intriguing problem. VanLehn (1998) for instance argues that there are differences in how novices and experts use analogy, but the VanLehn and Jones (1993) Cascade model does not implement these differences. This paper analyzes several variations in strategies for using analogy to explore possible sources of novice/expert differences. We describe a series of ablation experiments on an expert model to examine the effects of strategy variations in using analogy in problem solving. We provide evidence that failing to use qualitative reasoning when encoding problems, being careless in validating analogical inferences, and not using multiple retrievals can degrade the efficiency of problem-solving.
In this paper we discuss an elaboration account of insight that provides answers to two of the main questions regarding insight problem solving: why insight problems are so difficult for humans and why insight is so rapid in nature. We claim that the difficulty in insight problems is due to misguided heuristic search and that this difficulty is overcome using a reformulation mechanism. Furthermore, we claim that search is carried out quickly when the heuristics are good--explaining the rapid nature of insight. We clarify our account by providing examples and initial empirical results. In conclusion, we review related work and discuss possible future work.
We reminisce and discuss applications of algorithmic probability to a wide range of problems in artificial intelligence, philosophy and technological society. We propose that Solomonoff has effectively axiomatized the field of artificial intelligence, therefore establishing it as a rigorous scientific discipline. We also relate to our own work in incremental machine learning and philosophy of complexity.