4.4 Hybrid Cognitive Architectures 75

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” property to a significant extent. These architectures lack the rich, real-time interaction between the internal dynamics of various memory and learning processes that we believe is critical to achieving humanlike general intelligence using realistic computational resources. On the other hand, those architectures that feature richer integration — such as DUAL, Shruti, LIDA and MicroPsi— have the flaw of relying (at least in their current versions) on overly simplistic learning algorithms, which drastically limits their scalability. It does seem plausible to us that some of these hybrid architectures could be dramatically extended or modified so as to produce humanlike general intelligence. For instance, one could replace LIDA’s learning algorithms with others that interrelate with each other in a nuanced synergetic way; or one could replace MicroPsi’s simple learning and reasoning methods with much more powerful and scalable ones acting on the same data structures. However, making these changes would dramatically alter the cognitive architectures in question on multiple levels. 4.4.1 Neural versus Symbolic; Global versus Local The “symbolic versus emergentist” dichotomy that we have used to structure our review of cogni- tive architectures is not absolute nor fully precisely defined; it is more of a heuristic distinction. In this section, before plunging into the details of particular hybrid cognitive architectures, we review two other related dichotomies that are useful for understanding hybrid systems: neural versus symbolic systems, and globalist versus localist knowledge representation. HOUSE_OVERSIGHT_012991