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Unformatted text preview: Understanding the Brains Emergent Properties Don Miner and Marc Pickett and Marie desJardins Department of Computer Science and Electrical Engineering University of Maryland, Baltimore County 1000 Hilltop Circle, Baltimore, Maryland 21250 Abstract In this paper, we discuss the possibility of applying rule abstraction, a method designed to understand emergent systems, to the physiology of the brain. Rule abstraction reduces complex systems into simpler subsystems, each of which are then understood in terms of their respective subsystems. This process aids in the understanding of complex systems and how behavior emerges from the low-level interactions. We believe that this technique can be applied to the brain in order to understand the mind and its essential cognitive phenomena. Once a sufficient model of the brain and mind is created, our framework could then be used to build artificial general intelligence that is based on human intelligence. Introduction In this paper, we propose a method of understanding human intelligence by understanding how the mind emerges from the physiology of the brain. The brain may be viewed as a complex system that produces features of human-level intel- ligence from the low-level physical mechanisms in the neu- ral system. We hypothesize that we can improve our under- standing of how the brain works by reducing its emergent behavior into layers of successively more complex behav- iors on top of the neurological subsystem. To achieve this goal, we propose the use of rule abstraction , our mechanism for creating hierarchies of emergent behaviors (discussed in more detail in the next section). The purpose of this paper is to stimulate discussion about the value of such an approach for understanding the human brain and, as a result, under- stand intelligence. Understanding the mind by directly studying low-level structures, such as neurons and glial cells has not proven fruitful to date. For example, biologically inspired sys- tems such as Jeff Hawkins Memory Prediction (Hawkins & Blakeslee 2004) and Blue Brain (Markram 2006) have not led to general models of intelligence. The leap from neurons to high-level processes, such as reasoning and language, is too great for humans or machines to decipher in a single step as of 2009. However, in smaller-scale complex sys- tems, such as boid flocking (Reynolds 1987), we can math- ematically model how simple agent-level rules produce the Copyright c 2008, The Second Conference on Artificial General Intelligence (AGI-09.org). All rights reserved....
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- Spring '08