Ideally, the initial, preprogrammed knowledge of a machine would be restricted to the kinds of human knowledge thought to be innate (e.g., object persistence, motion continuity, basic models of animacy and mind, linguistic universals, means of classifying/taxonomizing the world, of organizing events in time, of abstracting from experience, and other such knowledge and know-how). The rest would be learned in human-like fashion. Unfortunately, we do not have embodied agents with human-like sensory and motor equipment or human-like innate mental capabilities; so apart from the simplest sort of verbal learning by robots such as verbal labeling of objects or actions, or using spatial prepositions or two-word sentences (e.g., Fleischman and Roy 2005; McClain and Levinson 2007; Cour et al. 2008), most current work on knowledge acquisition uses either (1) hand-coding, (2) knowledge extraction from text corpora, or (3) crowdsourcing coupled with some method of converting collected, verbally expressed “facts” to a usable format. We focus in this section on the acquisition of general background knowledge needed to support language understanding and production, leaving discussion of linguistic knowledge acquisition to section 9.