A Self-Organizing Multiple-View Representation of 3D Objects

We explore representation of 3D objects in which several distinct 2D views are stored for each object. We demonstrate the ability of a two-layer network of thresholded summation units to support such representations. Using unsupervised Hebbian relaxation, we trained the network to recognise ten objects from different viewpoints. The training process led to the emergence of compact representations of the specific input views. When tested on novel views of the same objects, the network exhibited a substantial generalisation capability. In simulated psychophysical experiments, the network's behavior was qualitatively similar to that of human subjects.

ARLO: Another Representation Language Offer

This paper describes ARLO, a representation language loosely modelled after Greiner and Lenant's RLL-1. ARLO is a structure-based representation language for describing structure-based representation languages, including itself. A given representation language is specified in ARLO by a collection of structures describing how its descriptions are interpreted, defaulted, and verified. This high level description is compiles into lisp code and ARLO structures whose interpretation fulfills the specified semantics of the representation. In addition, ARLO itself- as a representation language for expressing and compiling partial and complete language specifications- is described and interpreted in the same manner as the language it describes and implements. This self-description can be extended of modified to expand or alter the expressive power of ARLO's initial configuration. Languages which describe themselves like ARLO- provide powerful mediums for systems which perform automatic self-modification, optimization, debugging, or documentation. AI systems implemented in such a self-descriptive language can reflect on their own capabilities and limitations, applying general learning and problem solving strategies to enlarge or alleviate them.