000			03891cam a2200505Mi 4500
001			9780429028618
003			FlBoTFG
005			20220724194142.0
006			m o d
007			cr |||||||||||
008			200320s2020 flua fo 000 0 eng d
040			_aOCoLC-P _beng _erda _epn _cOCoLC-P
020			_a0429650248
020			_a9780429028618 _q(electronic bk.)
020			_a042902861X _q(electronic bk.)
020			_a9780429644962 _q(electronic bk. : Mobipocket)
020			_a0429644965 _q(electronic bk. : Mobipocket)
020			_a9780429647604 _q(electronic bk. : EPUB)
020			_a0429647603 _q(electronic bk. : EPUB)
020			_a9780429650246 _q(electronic bk.)
035			_a(OCoLC)1206241958
035			_a(OCoLC-P)1206241958
050		4	_aQ337.3
072		7	_aCOM _x021030 _2bisacsh
072		7	_aCOM _x037000 _2bisacsh
072		7	_aMAT _x004000 _2bisacsh
072		7	_aUMB _2bicssc
082	0	4	_a006.3/824 _223
245	0	0	_aSwarm intelligence _bfrom social bacteria to humans / _cedited by Andrew Schumann.
264		1	_aBoca Raton : _bCRC Press, _c2020.
300			_a1 online resource _billustrations (black and white, and colour)
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
520			_aThe notion of swarm intelligence was introduced for describing decentralized and self-organized behaviors of groups of animals. Then this idea was extrapolated to design groups of robots which interact locally to cumulate a collective reaction. Some natural examples of swarms are as follows: ant colonies, bee colonies, fish schooling, bird flocking, horse herding, bacterial colonies, multinucleated giant amoebae Physarum polycephalum, etc. In all these examples, individual agents behave locally with an emergence of their common effect. An intelligent behavior of swarm individuals is explained by the following biological reactions to attractants and repellents. Attractants are biologically active things, such as food pieces or sex pheromones, which attract individuals of swarm. Repellents are biologically active things, such as predators, which repel individuals of swarm. As a consequence, attractants and repellents stimulate the directed movement of swarms towards and away from the stimulus, respectively. It is worth noting that a group of people, such as pedestrians, follow some swarm patterns of flocking or schooling. For instance, humans prefer to avoid a person considered by them as a possible predator and if a substantial part of the group in the situation of escape panic (not less than 5%) changes the direction, then the rest follows the new direction, too. Some swarm patterns are observed among human beings under the conditions of their addictive behavior such as the behavior of alcoholics or gamers. The methodological framework of studying swarm intelligence is represented by unconventional computing, robotics, and cognitive science. In this book we aim to analyze new methodologies involved in studying swarm intelligence. We are going to bring together computer scientists and cognitive scientists dealing with swarm patterns from social bacteria to human beings. This book considers different models of simulating, controlling, and predicting the swarm behavior of different species from social bacteria to humans.
588			_aOCLC-licensed vendor bibliographic record.
650		0	_aSwarm intelligence.
650		7	_aCOMPUTERS / Database Management / Data Mining _2bisacsh
650		7	_aCOMPUTERS / Machine Theory _2bisacsh
650		7	_aMATHEMATICS / Arithmetic _2bisacsh
700	1		_aSchumann, Andrew, _eeditor.
856	4	0	_3Read Online _uhttps://www.taylorfrancis.com/books/9780429028618
856	4	2	_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
942			_2lcc _cEBK
999			_c14324 _d14324