Second Life Abandoned Virtual Realities

There is something strange about online entities, be it blogs, virtual games, moocs, or vles. There’s something ethereal about all those entities. Because they will fade away quickly as they were praised. They have a lifetime so short that some users are still hanging to those entities while the servers are being shut down. An example is Second Life islands. How many abandoned places are still there in Second Life. Ghost of 2007 gold run. Who is paying for them? Some of these places were never really popular. The virtual worlds were sold by the companies that promoted them to suckers that spent much real money to buy virtual goods, islands and services.

Second Life is only an example, but you can clearly imagine the same thing happening to other worlds like WoW or Minecraft. Obviously right now they are highly sought and present users might say that they don’t feel that the online entity is going to disappear. Are really you sure about that? Second Life was a pain. A evolved IRC for finger and imagination impaired people. I never liked it, but just 8 years ago everybody was writing about it (me included saying how bad it was (in PT)).

The moral of finding all these abandoned places in the virtual worlds is that there always some Bullish movement sponsored by companies that are real Bears. Be careful where you place your online bets. We really need an Internet janitor.

  2015/08/17 4:50 PM Follow @sixhat

A Model for Foraging Ants, Controlled by Spiking Neural Networks and Double Pheromones

Foraging Ants Brain controlled by Spiking Neural Networks and Double Pheromones

A model of an Ant System where ants are controlled by a spiking neural circuit and a second order pheromone mechanism in a foraging task is presented. A neural circuit is trained for individual ants and subsequently the ants are exposed to a virtual environment where a swarm of ants performed a resource foraging task. The model comprises an associative and unsupervised learning strategy for the neural circuit of the ant. The neural circuit adapts to the environment by means of classical conditioning. The initially unknown environment includes different types of stimuli representing food (rewarding) and obstacles (harmful) which, when they come in direct contact with the ant, elicit a reflex response in the motor neural system of the ant: moving towards or away from the source of the stimulus. The spiking neural circuits of the ant is trained to identify food and obstacles and move towards the former and avoid the latter. The ants are released on a landscape with multiple food sources where one ant alone would have difficulty harvesting the landscape to maximum efficiency. In this case the introduction of a double pheromone mechanism (positive and negative reinforcement feedback) yields better results than traditional ant colony optimization strategies. Traditional ant systems include mainly a positive reinforcement pheromone. This approach uses a second pheromone that acts as a marker for forbidden paths (negative feedback). This blockade is not permanent and is controlled by the evaporation rate of the pheromones. The combined action of both pheromones acts as a collective stigmergic memory of the swarm, which reduces the search space of the problem. This paper explores how the adaptation and learning abilities observed in biologically inspired cognitive architectures is synergistically enhanced by swarm optimization strategies. The model portraits two forms of artificial intelligent behaviour: at the individual level the spiking neural network is the main controller and at the collective level the pheromone distribution is a map towards the solution emerged by the colony. The presented model is an important pedagogical tool as it is also an easy to use library that allows access to the spiking neural network paradigm from inside a Netlogo—a language used mostly in agent based modelling and experimentation with complex systems.

Intelligence and decision in foraging ants. Individual or Collective? Internal or External? What is the right balance between the two. Can one have internal intelligence without external intelligence? Can one take examples from nature to build in silico artificial lives that present us with interesting patterns? We explore a model of foraging ants in this paper that will be presented in early September in Exeter.

Co-authored with Cristian Jimenez-Romero, Jeffrey Johnson and Vitorino Ramos and available in arXiv 1507.08467 and Researchgate, this will be presented latter this year at UKCI 2015.


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[27] D. Sousa-Rodrigues, J. Louçã, and V. Ramos, “From standard ˜ to second-order swarm intelligence phase-space maps,” in 8th European Conference on Complex Systems, S. Thurner, Ed., Vienna, Austria, Sep 2011.

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  2015/07/31 2:05 PM Follow @sixhat

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