2 research outputs found
Structural tendencies - Effects of adaptive evolution of complex (chaotic) systems
We describe systems using Kauffman and similar networks. They are directed
funct ioning networks consisting of finite number of nodes with finite number
of discr ete states evaluated in synchronous mode of discrete time. In this
paper we introduce the notion and phenomenon of `structural tendencies'.
Along the way we expand Kauffman networks, which were a synonym of Boolean
netw orks, to more than two signal variants and we find a phenomenon during
network g rowth which we interpret as `complexity threshold'. For simulation we
define a simplified algorithm which allows us to omit the problem of periodic
attractors. We estimate that living and human designed systems are chaotic (in
Kauffman sens e) which can be named - complex. Such systems grow in adaptive
evolution. These two simple assumptions lead to certain statistical effects
i.e. structural tendencies observed in classic biology but still not explained
and not investigated on theoretical way. E.g. terminal modifications or
terminal predominance of additions where terminal means: near system outputs.
We introduce more than two equally probable variants of signal, therefore our
networks generally are not Boolean networks. T hey grow randomly by additions
and removals of nodes imposed on Darwinian elimination. Fitness is defined on
external outputs of system. During growth of the system we observe a phase
transition to chaos (threshold of complexity) in damage spreading. Above this
threshold we identify mechanisms of structural tendencies which we investigate
in simulation for a few different networks types, including scale-free BA
networks.Comment: 20 pages with fugures, to be published in Int. J. Mod. Phys.
Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009
Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In
recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence