An Architecture for Integrated Introspective Learning

This paper presents a computational model of integrated introspective learning, which is a deliberative learning process in which a reasoner introspects about its own performance on a reasoning task, identifies what it needs to learn to improve its performance, formulates learning goals to acquire the required knowledge, and pursues its learning goals using multiple learning strategies. We discuss two case studies of integrated introspective learning in two different task domains. The first case study deals with learning diagnostic knowledge during a troubleshooting task, and is based on observations of human operators engaged in a real-world troubleshooting task at an electronics assembly plant. The second case study deals with learning multiple kinds of causal and explanatory knowledge during a story understanding task. The model is computationally justified as a uniform and extensible framework for deliberative learning using multiple learning strategies, and cognitively justified a..