Modular Belief Updates and Confusion about Measures of Certainty in Artificial Intelligence Research
This addresses a foundational issue in AI for researchers and practitioners dealing with uncertainty reasoning, but it is incremental as it clarifies existing concepts rather than introducing new methods.
The paper tackles the problem of confusion in AI research regarding measures of certainty, specifically focusing on modular belief updates, and identifies their inappropriate use in two influential expert systems.
Over the last decade, there has been growing interest in the use or measures or change in belief for reasoning with uncertainty in artificial intelligence research. An important characteristic of several methodologies that reason with changes in belief or belief updates, is a property that we term modularity. We call updates that satisfy this property modular updates. Whereas probabilistic measures of belief update - which satisfy the modularity property were first discovered in the nineteenth century, knowledge and discussion of these quantities remains obscure in artificial intelligence research. We define modular updates and discuss their inappropriate use in two influential expert systems.