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Stigmergy and Gaming

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This post from PlayStation “Lifestyle”

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Neuroswarm

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This article is really creating a buzz (sorry!!) The idea has some resonance to an aspect of Hayek’s social epistemology (see the article that I just today uploaded).

In much the same way that synapses are strengthened while unused linkages weaken and wither away, so too are paths to salient social knowledge strengthened or weakened – “social connectionism,” if you will.

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Smells Like Stigmergy

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Love the witty title of Dave Reeves’ blog post.

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Stigmergy and City Planning

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Here’s an interview conducted by Howard Rheingold with Mark Elliott who like myself has been promoting the virtues of stigmergy. The application of the concept to city planning and consultation is especially interesting since it’s a world I have recently come into contact with.

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Self-organized cooperation between robotic swarms

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Check out this new paper from Swarm Intelligence.

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Web Stigmergy

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Stigmergy yet again – well, there is no escaping it. While I wouldn’t call stigmergy a new paradigm (that’s too pompous – in any event, paradigms only have shape in a historical sense), stigmergy is one of the most fruitful mechanisms around that speaks to distributed cognition.

Check out this paper nicely titled “Standing on the shoulders of ants: stigmergy in the Web” by a student who is working on the stigmergic properties of the Web. A nice overture to further more technical work highlighting some important issues that need to be addressed.  Two points are in order.

(1) I disagree that ”we don‟t yet have a clear definition of stigmergy” – I don’t think that one can prescribe necessary and sufficient conditions but we sure can specify typical features.

(2) And “combining bio-inspired designs and algorithms based on stigmergy with social network analysis might facilitate the creation of a more sophisticated web application.” This is already here, the preeminent example being Amazon’s recommendation algorithm.

Recommendation algorithms generally come in two varieties – collaborative filtering (CF) and cluster models (CM). CF attempts to mimic the process of ‘‘word-of-mouth’’ by which people recommend products or services to one another. CF runs on the notion that people who agreed in the past will agree in the future. CF aggregates ratings of items to recognize similarities between users, and generates a new recommendation of an item by weighting the ratings of similar users for the same item. But this technique is computationally expensive because ‘‘the average customer vector is extremely sparse’’ (Linden, Smith, & York, 2003, p. 77). By contrast CM divides the agent base into segments, treating the task as a classificatory problem. An agent is assigned a category comprised of similar agent profiles. Only then are recommendations generated. CM is computationally efficient since it only searches segments, rather than the complete database. Amazon.com’s recommendation algorithm is a derivative form of CF and CM. Consider an example. A search on Amazon for ‘‘stigmergy’’ returns 176 items, the default sort being by relevance (as opposed to price, reviews, publication date). Also given some prominence is a category ‘‘Customers who bought items in your Recent History also bought x, y, z . . ..’’ supplemented by Listmania, lists of salient material compiled by agents (all-comers as in Wikipedia) who ostensibly have some intimacy with the topic. There are also so-called ‘‘reviews’’ of a given title. All this over and above a record of my recent purchases which included stigmergy related material, assuming one hasn’t expunged Amazon’s cookies from one’s browser. Even on offer is the opportunity, for many titles, to peruse the contents page, read an excerpt and even be enticed by the dustjacket hyperbole. Furthermore, one can be alerted by email when a new title or new edition of a book matching one’s previous trails of interest, will become available: a preorder entitling the buyer to a discount. This all adds up to a highly bespoke experience that is better tailored than being in a bookstore, because it is unlikely the bookstore even stocks a title you have yet to discover as one scans the shelves – there is no ‘‘pheromone’’ trail. The Amazon algorithm rather than matching user-to-user finds items that customers tend to purchase together. It is computationally efficient (and easily scalable) because much of the computation has already been done off-line. The stigmergic interest of Amazon’s algorithm is patently clear: an item-to-item search generates a trail that gives rise to novel patterns of behavior. CF’s great virtue is that suppliers can be finely attuned to consumer behavior. The downside is that there runs the risk of ‘‘a kind of dysfunctional communal narrowing of attention’’ that can be self-fulfilling (Clark, 2003, p. 158; Gureckis & Goldstone, 2006, p. 296). Excerpt from Stigmergic epistemology, stigmergic cognition.

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Stigmergy Installation

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Check out this installation supported by the Welcome Trust.

People walking along Euston Road will encounter an unusually arresting reflection of themselves in a new light installation, ‘Reflex’, created by rAndom International.

The work inhabits the windows of the Wellcome Trust as though it were a living organism. Reacting to viewers, passers-by and traffic on Euston Road, ‘Reflex’ produces mesmerising flows of light, inviting a physical response to the building.

The installation’s swarming behaviour is based on an algorithm developed to emulate the collective decision making that we see in large groups of creatures such as birds or ants.

The work is constructed from hundreds of brass rods and thousands of LEDs arranged on small custom chips. Their movement is based on programmes that aim to simulate complex natural phenomena. ‘Reflex’ recreates “stigmergy”, whereby traces left by random actions stimulate further actions that build on one another, leading to the spontaneous emergence of apparently patterned activity. rAndom’s work allows for error, experimentation and unpredictability, and ‘Reflex’ encourages its viewers to see how they can influence the work.

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Stigmergic Simulations

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Here are some terrific stigmergic simulations by architectural student Yang Chenghan that I chanced across:

The first is a 3D simulation deploying 45-70 agents (source code)

The second a 2D simulation deploying 20-30 agents (source code)

Here are some great synthetic stigmergic stills Yang has created.

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Swarm Cognition

Check out the latest themed issue of Swarm Intelligence. Here is the freely available introduction. Nice to see the term “cognition” used since I have been using the term in this regard for a few years.

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Determining the Role of Geospatial Technologies For Stigmergic Coordination in Situation Management: Implications of the Wireless Grid

Here’s a nice poster from Janet Marsden at Syracuse:

Abstract — Geospatial technologies in conjunction with wireless grids will offer a context for locating and coordinating team activities in such a way that the nature of each team member’s effort may be known and understood by other members. This constructed group knowledge enables teams to respond to unforeseen and emergent contingencies and act in concert through the active interpretation of shared artifacts alone without prior planning and coordination. Stigmergic or sematectonic coordination refers to how an individual behaves as part of a collaborative team engaged in a complex task, such as emergency response (i.e. where the task is of such complexity that a coordinated team effort is required to accomplish it). Human stigmergic coordination emerges on the basis of how tasks and goals are structured and understood between the members of the team. Geographically coded information, generated and shared dynamically, gives teams maps of each others’ activities, plus remotely sensed data. The major function of the geospatial technology repository and interface is to provide dynamic knowledge of group activities in real time. Environmental changes reveal new dependencies for adaptive collaboration as conditions on the ground evolve, enabling participants to track the evolution of each other’s work and mutually adjust to it in a timely manner.

Index Terms — geospatial technology, virtual collaboration, wireless grids, complex systems, situation management.

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