000			03152nam a22004335i 4500
001			u370634
003			SIRSI
005			20160812080044.0
007			cr nn 008mamaa
008			110822s2011 xxk| s |||| 0|eng d
020			_a9780857297907 _9978-0-85729-790-7
040			_cMX-MeUAM
050		4	_aQ334-342
050		4	_aTJ210.2-211.495
082	0	4	_a006.3 _223
100	1		_aMarwala, Tshilidzi. _eauthor.
245	1	0	_aMilitarized Conflict Modeling Using Computational Intelligence _h[recurso electrónico] / _cby Tshilidzi Marwala, Monica Lagazio.
264		1	_aLondon : _bSpringer London, _c2011.
300			_aXVIII, 254 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aAdvanced Information and Knowledge Processing, _x1610-3947
520			_aMilitarized Conflict Modeling Using Computational Intelligence examines the application of computational intelligence methods to model conflict. Traditionally, conflict has been modeled using game theory. The inherent limitation of game theory when dealing with more than three players in a game is the main motivation for the application of computational intelligence in modeling conflict. Militarized interstate disputes (MIDs) are defined as a set of interactions between, or among, states that can result in the display, threat or actual use of military force in an explicit way. These interactions can result in either peace or conflict. This book models the relationship between key variables and the risk of conflict between two countries. The variables include Allies which measures the presence or absence of military alliance, Contiguity which measures whether the countries share a common boundary or not and Major Power which measures whether either or both states are a major power. Militarized Conflict Modeling Using Computational Intelligence implements various multi-layer perception neural networks, Bayesian networks, support vector machines, neuro-fuzzy models, rough sets models, neuro-rough sets models and optimized rough sets models to create models that estimate the risk of conflict given the variables. Secondly, these models are used to study the sensitivity of each variable to conflict. Furthermore, a framework on how these models can be used to control the possibility of peace is proposed. Finally, new and emerging topics on modelling conflict are identified and further work is proposed.
650		0	_aComputer science.
650		0	_aArtificial intelligence.
650	1	4	_aComputer Science.
650	2	4	_aArtificial Intelligence (incl. Robotics).
700	1		_aLagazio, Monica. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780857297891
830		0	_aAdvanced Information and Knowledge Processing, _x1610-3947
856	4	0	_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-0-85729-790-7
596			_a19
942			_cLIBRO_ELEC
999			_c198514 _d198514