4.3 Emergentist Cognitive Architectures 65
4.3 Emergentist Cognitive Architectures
Another species of cognitive architecture expects abstract symbolic processing to emerge from lower-level “subsymbolic” dynamics, which sometimes (but not always) are designed to simu- late neural networks or other aspects of human brain function. These architectures are typically strong at recognizing patterns in high-dimensional data, reinforcement learning and associative memory; but no one has yet shown how to achieve high-level functions such as abstract reason- ing or complex language processing using a purely subsymbolic approach. A few of the more important subsymbolic, emergentist cognitive architectures are:
e DeSTIN [ARK09a, ARCO9], which is part of CogPrime, may also be considered as an autonomous AGI architecture, in which case it is emergentist and contains mechanisms to encourage language, high-level reasoning and other abstract aspects of intelligent to emerge from hierarchical pattern recognition and related self-organizing network dynamics. In CogPrime DeSTIN is used as part of a hybrid architecture, which greatly reduces the reliance on DeSTIN’s emergent properties.
e Hierarchical Temporal Memory (HTM) [I06] is a hierarchical temporal pattern recognition architecture, presented as both an AI approach and a model of the cortex. So far it has been used exclusively for vision processing and we will discuss its shortcomings later in the context of our treatment of DeSTIN.
e SAL [JL08], based on the earlier and related IBCA (Integrated Biologically-based Cog- nitive Architecture) is a large-scale emergent architecture that seeks to model distributed information processing in the brain, especially the posterior and frontal cortex and the hippocampus. So far the architectures in this lineage have been used to simulate various human psychological and psycholinguistic behaviors, but haven’t been shown to give rise to higher-level behaviors like reasoning or subgoaling.
e NOMAD (Neurally Organized Mobile Adaptive Device) automata and its successors [KE06] are based on Edelman’s “Neural Darwinism” model of the brain, and feature large numbers of simulated neurons evolving by natural selection into configurations that carry out sensorimotor and categorization tasks. The emergence of higher-level cognition from this approach seems rather unlikely.
e Ben Kuipers and his colleagues [MK07, MIX08, MIXO09]have pursued an extremely innovative research program which combines qualitative reasoning and reinforcement learning to enable an intelligent agent to learn how to act, perceive and model the world. Kuipers’ notion of “bootstrap learning” involves allowing the robot to learn almost everything about its world, including for instance the structure of 3D space and other things that humans and other animals obtain via their genetic endowments. Compared to Kuipers’ approach, CogPrime falls in line with most other approaches which provide more “hard-wired” structure, following the analogy to biological organisms that are born with more innate biases.
There is also a set of emergentist architectures focused specifically on developmental robotics, which we will review below in a separate subsection, as all of these share certain common characteristics.
Our general perspective on the emergentist approach is that it is philosophically correct but currently pragmatically inadequate. Eventually, some emergentist approach could surely succeed at giving rise to humanlike general intelligence — the human brain, after all, is plainly an emergentist system. However, we currently lack understanding of how the brain gives rise to abstract reasoning and complex language, and none of the existing emergentist systems
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