@ARTICLE (, AUTHOR = "B. A. Huberman and T. Hogg", TITLE = "Phase Transitions in Artificial Intelligence Systems", JOURNAL = "Artificial Intelligence", VOLUME = "33", PAGES = "155-171", YEAR = 1987)
We predict that large-scale artificial intelligence systems and cognitive models will undergo sudden phase transitions from disjointed parts into coherent structures as their topological connectivity increases beyond a critical value. These situations, ranging from production systems to semantic net computations, are characterized by event horizons in space-time that determine the range of causal connections between processes. At transition, these event horizons undergo explosive changes in size. This phenomenon, analogous to phase transitions in nature, provides a new paradigm with which to analyze the behavior of large-scale computation and determine its generic features.