%0 Journal Article %1 Ramos05 %A Ramos, Vitorino %A Fernandes, Carlos %A Rosa, Agostinho C. %D 2005 %K AI ant cognition colony dynamics emergent information lkl-kss networks simulation social sociocognitive swarms tags %T Social Cognitive Maps, Swarm Collective Perception and Distributed Search on Dynamic Landscapes %U http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_58.html

@article{Ramos05, added-at = {2006-10-23T17:25:28.000+0200}, author = {Ramos, Vitorino and Fernandes, Carlos and Rosa, Agostinho C.}, biburl = {https://www.bibsonomy.org/bibtex/28e7728a30a2adc90af034bead0ce0505/grahl}, citeulike-article-id = {407689}, comment = {They begin by explaining: Flocks of migrating birds and schools of fish are familiar examples of spatial selforganized patterns formed by living organisms through social foraging. Such aggregation patterns are observed not only in colonies of organisms as simple as single-cell bacteria, as interesting as social insects like ants and termites as well as in colonies of multi-cellular vertebrates as complex as birds and fish but also in human societies. Wasps, bees, ants and termites all make effective use of their environment and resources by displaying collective “swarm” intelligence. For example, termite colonies build nests with a complexity far beyond the comprehension of the individual termite, while ant colonies dynamically allocate labor to various vital tasks such as foraging or defense without any central decision-making ability. Slime mould is another perfect example. These are very simple cellular organisms with limited motile and sensory capabilities, but in times of food shortage they aggregate to form a mobile slug capable of transporting the assembled individuals to a new feeding area. Should food shortage persist, they then form into a fruiting body that disperses their spores using the wind, thus ensuring the survival of the colony. And conclude: Evolution of mass behaviours on time are difficult to predict, since the global behaviour is the result of many part relations operating in their own local neighbourhood. The emergence of network trails in ant colonies, for instance, are the product of several simple and local interactions that can evolve to complex patterns, which in some sense translate a meta-behaviour of that swarm. Moreover, the translation of one kind of low-level (present in a large number) to one meta-level is minimal. Although that behaviour is specified (and somehow constrained), there is minimal specification of the mechanism required to generate that behaviour; global behaviour evolves from the many relations of multiple simple behaviours, without global coordination (i.e. from local interactions to global complexity. One paradigmatic and abstract example is the notion, within a specified population, of common-sense, being the meta-result a type of collective-conscience. There is some evidence that our brain as well as many other complex systems, operates in the same way, and as a consequence collective perception capabilities could be derived from emergent properties, which cannot be neglected in any pattern search algorithm. These systems show in general, interesting and desirable features as flexibility (e.g. the brain is able to cope with incorrect, ambiguous or distorted information, or even to deal with unforeseen or new situations without showing abrupt performance breakdown) or versability, robustness (keep functioning even when some parts are locally damaged), and they operate in a massively parallel fashion. Present results point to that type of interesting features.}, interhash = {94852bf2e4e639cbbb0f29ababeb6161}, intrahash = {8e7728a30a2adc90af034bead0ce0505}, keywords = {AI ant cognition colony dynamics emergent information lkl-kss networks simulation social sociocognitive swarms tags}, priority = {4}, timestamp = {2007-07-25T11:43:55.000+0200}, title = {Social Cognitive Maps, Swarm Collective Perception and Distributed Search on Dynamic Landscapes}, url = {http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_58.html}, year = 2005 }