Abstract :
I was among those who proposed problem solving methods (PSMs) in the late 1970s and early 1980s as a knowledge-level
description of strategies useful in building knowledge-based systems. This paper summarizes the evolution of my ideas in
the last two decades. I start with a review of the original ideas. From an artificial intelligence (AI) point of view, it is not
PSMs as such, which are essentially high-level design strategies for computation, that are interesting, but PSMs associated
with tasks that have a relation to AI and cognition. They are also interesting with respect to cognitive architecture proposals
such as Soar and ACT-R: PSMs are observed regularities in the use of knowledge that an exclusive focus on the architecture
level might miss, the latter providing no vocabulary to talk about these regularities. PSMs in the original conception are
closely connected to a specific view of knowledge: symbolic expressions represented in a repository and retrieved as
needed. I join critics of this view, and maintain with them that most often knowledge is not retrieved from a base as
much as constructed as needed. This criticism, however, raises the question of what is in memory that is not knowledge
as traditionally conceived in AI, but can support the construction of knowledge in predicate–symbolic form. My recent
proposal about cognition and multimodality offers a possible answer. In this view, much of memory consists of perceptual
and kinesthetic images, which can be recalled during deliberation and from which internal perception can generate linguistic–
symbolic knowledge. For example, from a mental image of a configuration of objects, numerous sentences can be
constructed describing spatial relations between the objects. My work on diagrammatic reasoning is an implemented
example of how this might work. These internal perceptions on imagistic representations are a new kind of PSM
