Computational Approaches to Representation Change during Learning and Development: Papers from the AAAI Fall Symposium
Clayton T. Morrison and Tim Oates, Cochairs
In nearly every subfield of artificial intelligence, it is both true and well known that using the right representation is crucial. For example, moving from state-space planning to plan-space planning can make previously intractable problems solvable. Representation change is likewise implicated in psychological accounts of learning and problem solving, ranging from the relatively mundane to more significant “aha! moments” where an insight leads to problem reorganization and a breakthrough. Representation change also features prominently in many accounts of perceptual and cognitive development, and in some cases, such as that of Jean Piaget's constructivism, it is the driver of cognitive development.
The goal of this symposium was to bring together researchers from a diverse set of fields (artificial intelligence, machine learning, cognitive and developmental psychology, cognitive science, and philosophy) to survey the state of the art and establish a set of open problems and a research agenda in the area of automated development and, more specifically, change of representation. Submissions that emphasize computational mechanisms, present animal and human evidence of representation change, or propose learning and problem solving scenarios that require representation change are particularly encouraged.