FL246, Beckett Conference Room. Although much has been gained in economy of expression through this evolution, much has been lost in explicitness of this expression. Much of what is explicit in pictorial representations is, at best, implicit in textual ones, requiring inference to reveal it. One aspect of diagrammatic reasoning research hypothesizes that computing with textual representations suffers analogously from the same loss of explicitness. We define diagrammatic representations as those that analogically model the semantics of a problem domain and diagrammatic reasoning as the process by which inferences can be draw from such representations.
Humans possess a highly developed ability to reason directly with diagrammatic representations.
Endowing a machine with similar capabilities would be beneficial in the development of an artificially intelligent agent that interacts with a real world environment, rife with diagrammatic information, with a higher degree of autonomy than those without such capabilities. Further, these capabilities would provide a manner of human-machine interfacing via diagrams similar to a way humans communicate among themselves as a matter of course. Besides an interest in investigating general approaches to diagrammatic reasoning, this work has also been motivated by a desire to explore the boundaries of what can be computed via diagrammatic representations alone. Example applications include, among others, heuristic development in game playing, inductive learning from diagrams, inference from cartograms, and diagrammatic case-based reasoning.