Cases, context, and comfort: opportunities for case-based reasoning

Abstract

Artificial intelligence (AI) methods have the potential for broad impact in smart homes. Different AI methods offer different contributions for this domain, with different design goals, tasks, and circumstances dictating where each type of method best applies. In this chapter, we describe motivations and opportunities for applying case-based reasoning (CBR) to a human-centered approach to the capture, sharing, and revision of knowledge for smart homes. Starting from the CBR cognitive model of reasoning and learning, we illustrate how CBR could provide useful capabilities for problem detection and response, provide a basis for personalization and learning, and provide a paradigm for home-human communication to cooperatively guide performance improvement. After sketching how these capabilities could be served by case-based reasoning, we discuss some design issues for applying CBR within smart homes and case-based reasoning research challenges for realizing the vision.