A great many sundry events have transpired since I’ve last committed anything to this place. My Descartes paper still isn’t done, and I feel guilty and insufficient. Hume rocks my socks though and we had a discussion in my class about "could Hume love Kiad? Is Kiad’s crush on hume real even though it only exists in her mind?" Yeah. We even discussed whether or not porn was real enough for hume to get him off. We got a little off track.

My complexity class is difficult. The Taylor book, The Moment of Complexity (probably much to whatifoundthere’s chagrin) is completely bunk and I hate it. Unfortunately, my professor is extensively quoted throughout, so I feel rather guilty making fun of choice quotes like this:

What Darwin needed to complete his theory was Smith’s account of the division of labor. Darwin, Depew and Weber point out, 'rids the organic world of Aristotelian essences altogether by generalizing the individualist ontology of political economy.'

Bleh. The entire book, The Moment of Complexity is just a wankfest of everything that is potentially construed as complex and then iterated. He also quotes extensively from Hofstadter but doesn’t seem to understand what he is explaining, otherwise, wouldn’t his summeries serve to illuminate the reader instead of requiring the reader to read the source in order to make sense of the summary of purpose? Gah.

I’ve been obsessed with swarm intelligence, and I think I’ve found some fantastic references. I thought I’d share them here in case people like pbrane, evan or daemonv had comments or suggestions on other resources.

So far my favourite book is Swarm Intelligence by James Kennedy and Russell C. Eberhart. The author’s concept of ‘emergence’ is sophisticated and I feel quite philosophically sound. The presentation is very clear and easy to understand- some of my favourite parts of the book so far are their section on flocks, herds, schools and swarms, social behaviour as optimisation. The discussion of the mathematical models developed by the Santa Fe Institute and the Los Alamos National Laboratory that describe the dynamics of swarms and collective intelligences is quite lively and exciting.

Like Hofstadter, Millonas (The author of the above mathematical models) compares the communications network within a swarm of ants to the highly interconnected architecture of neurons in a brain. Research on live ants has shown that when food is placed at some distance from the nest, with two paths of unequal length leading to it, they will end up with the swarm following the shorter path. If a shorter path is introduced, though, for instance, if an obstacle is removed, they are unable to switch to it. If both paths are of equal length, the ants will choose one or the other. If two food sources are offered, with one being a richer source than the other, a swarm of ants will choose the richer source; if a richer source is offered after the choice has been made, most species are unable to switch, but some species are able to change their pattern to the better source. If two equal sources are offered, an ant will choose one or the other arbitrarily. Both cases can be described in terms of three characteristics:

• Their structure comprises a set of nodes and their interconnections
  
• The states of node variables change dynamically over time.
  
• There is learning-changes in the strengths of the connections among the nodes.

(This argument is based on a famous paper by Doyne Farmer(1991)- another Sanfe Fe Institute habitué-depicting "The Rosetta Stone of Connectionism." Millonas argues that the intelligence of an ant swarm arises during phase transitions-the same transitions that Langton described as defining "the edge of chaos." The movements of ants are essentially random as long as there is no systematic pheromone pattern; activity is a function of two parameters, which are the strength of pheromones and the attractiveness of the pheromone to the ants.

Another book that is mainly useful for me because it is a complilation of technical essays with a CD of example code is Evolutionary Design by Computers edited by Peter J. Bentley. The collection includes several theoretical essays that discuss the relationship of computer-driven design to human innovation. A section on evolutionary designs furnishes several case studies on real applications of these techniques--specifically, engineering problems for designing satellite booms, flywheels, and a reliability measurement for networks.
From Chapter 12: Evolving Three-Dimensional Morphology and Behaviour by Karl Sims

A classic trade-off in the fields of artificial life and computer animation is that of complexity vs. control. It is often difficult to build interesting or realistic virtual entities and still maintain control over them.

I rather like that. When creating virtual representations of cellular automata, complexity occurs even when we apply understood rules. Just as we cannot control or always predict the evolution of nature, when modelling artificial life as closely to nature, it seems that we again lose control of it. The metaphor seems to be self-reflexive.

Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines (Intelligent Robotics and Autonomous Agents)by Stefano Nolfi and Dario Floreano

Some of my notes relevant to swarms:

A key feature of intelligent systems is generality, i.e., the ability to carry out a certain task in different enviromental conditions or the ability to carry out different tasks. In the context of predators and prey, for example, predators should be able to catch different types of prey.

• Most established systems are interesting because they can solve non trivial tasks in simple wayss. However, they are strongly dependent on the current state of the enviroment.
  
• If the environment changes (e.g., the strategy of the competitor changes) they may become unable to solve their task.
  
• These two askpects (i.e., simplicity and lack of generality) are two sides of the same coin. The former systems are able to solve non trivial tasks with simple strategies because they exploit the reularities available in the environment (including the physical characteristics of their own sensory-motor sytems).
  
• In order to be general, systems should be as autonomous as possible from their environment; intelligent systems should rely less on the regulairies available in the enviroment and more on their internal 'neverous mechanisms.
  
  Artificial Life VII: Proceedings of the Seventh International Conference on Artificial Life (Complex Adaptive Systems) by Mark Bedau, John S. McCaskill (Editor), Norman H. Packard (Editor), Steen Rasmussen (Editor), John McCaskill, Norman Packard
  
  There are four distinct collective behaviors of prey, that are only modified by the equation of motion, e:
  
  • Marching
    The lements form a regular triangular crystal, moving at a constant velocity. The formation is stable against disturbance.
    
  • Oscillation
    Several group motions exhibit regular oscillations:
    (i)Wavy motion of the cluster along a linear trajectory. (ii)A cluster circling a center outside the cluster. (iii) A cluster circling a center inside the cluster.
    
  • Wandering
    Although the lattice-like order inside the cluster persists, the center of thecluster can wander quite irregularly. Chaotic intermittency of motion is found.
    
  • Swarming
    The most irregular motions are found in swarming. although the cluster persists, lattice-like order is broken completely. The veolocity of the elements has a large distribution, and the mobility of the cluster is small.
  
  
  
  
  more later