4.4 Hybrid Cognitive Architectures 75 reasoning methods is powerful, but the overall cognitive architecture is simplistic compared to other systems and seems focused more on problem-solving than on the broader problem of intelligent agent control. e Shruti [SA93] is a fascinating biologically-inspired model of human reflexive inference, which represents in connectionist architecture relations, types, entities and causal rules using focal-clusters. However, much like Hofstadter’s earlier Copycat architecture [Hof95], Shruti seems more interesting as a prototype exploration of ideas than as a practical AGI system; at least, after a significant time of development it has not proved significantly effective in any applications e James Albus’s 4D/RCS robotics architecture shares a great deal with some of the emer- gentist architectures discussed above, e.g. it has the same hierarchical pattern recognition structure as DeSTIN and HTM, and the same three cross-connected hierarchies as DeSTIN, and shares with the developmental robotics architectures a focus on real-time adaptation to the structure of the world. However, 4D/RCS is not foundationally learning-based but relies on hard-wired architecture and algorithms, intended to mimic the qualitative structure of relevant parts of the brain (and intended to be augmented by learning, which differentiates it from emergentist approaches. As our own CogPrime approach is a hybrid architecture, it will come as no surprise that we believe several of the existing hybrid architectures are fundamentally going in the right direction. However, nearly all the existing hybrid architectures have severe shortcomings which we feel will prevent them from achieving robust humanlike AGI. Many of the hybrid architectures are in essence “multiple, disparate algorithms carrying out separate functions, encapsulated in black boxes and communicating results with each other.” For instance, PolyScheme, ACT-R and CLARION all display this “modularity”