@article {Gershenson:2013,
	title = {Living in Living Cities},
	journal = {Artificial Life},
	volume = {In Press},
	year = {2013},
	abstract = {<p>This paper presents and overview of current and potential applications of living technology to urban problems. Living technology can be described as technology that exhibits the core features of living systems. These features can be useful to solve dynamic problems. In particular, urban problems concerning mobility, logistics, telecommunications, governance, safety, sustainability, and society and culture are presented, while solutions involving living technology are reviewed. A methodology for developing living technology is mentioned, while self-organizing traffic lights are used as a case study of the benefits of urban living technology. Finally, the usefulness of describing cities as living systems is discussed.</p>
},
	keywords = {cities, Self-organization, traffic, transport},
	url = {http://arxiv.org/abs/1111.3659},
	author = {Carlos Gershenson}
}
@inbook {Edmonds:2012,
	title = {Learning, Social Intelligence and the {Turing} Test - why an {\textquoteleft}{\textquoteleft}out-of-the-box" {Turing} Machine will not pass the {Turing} Test.},
	booktitle = {How the world computes : Turing Centenary Conference and 8th Conference on Computability in Europe, CiE 2012, Cambridge, UK, June 18-23, 2012. Proceedings},
	series = {Lecture Notes in Computer Science},
	volume = {7318/2012},
	year = {2012},
	pages = {182{\textendash}192},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	address = {Berlin Heidelberg},
	abstract = {The Turing Test (TT) checks for human intelligence, rather than any putative general intelligence. It involves repeated interaction requiring learning in the form of adaption to the human conversation partner. It is a macro-level post-hoc test in contrast to the definition of a Turing Machine (TM), which is a prior micro-level definition. This raises the question of whether learning is just another computational process, i.e. can be implemented as a TM. Here we argue that learning or adaption is fundamentally different from computation, though it does involve processes that can be seen as computations. To illustrate this difference we compare (a) designing a TM and (b) learning a TM, defining them for the purpose of the argument. We show that there is a well-defined sequence of problems which are not effectively designable but are learnable, in the form of the bounded halting problem. Some characteristics of human intelligence are reviewed including it{\textquoteright}s: interactive nature, learning abilities, imitative tendencies, linguistic ability and context-dependency. A story that explains some of these is the Social Intelligence Hypothesis. If this is broadly correct, this points to the necessity of a considerable period of acculturation (social learning in context) if an artificial intelligence is to pass the TT. Whilst it is always possible to {\textquoteright}compile{\textquoteright} the results of learning into a TM, this would not be a designed TM and would not be able to continually adapt (pass future TTs). We conclude three things, namely that: a purely "designed" TM will never pass the TT; that there is no such thing as a general intelligence since it necessary involves learning; and that learning/adaption and computation should be clearly distinguished.},
	doi = {10.1007/978-3-642-30870-3_18},
	url = {http://arxiv.org/abs/1203.3376},
	author = {Bruce Edmonds and Carlos Gershenson},
	editor = {S. Barry Cooper and Anuj Dawar and Benedikt L{\"o}we}
}
@conference {Gershenson1998a,
	title = {L{\'o}gica Multidimensional: Un Modelo de L{\'o}gica Paraconsistente},
	booktitle = {Memorias {XI} Congreso Nacional {ANIEI}},
	year = {1998},
	pages = {132{\textendash}141},
	address = {Xalapa, M{\'e}xico},
	abstract = {La l{\'o}gica multidimensional es un nuevo sistema de l{\'o}gica propuesto para modelar l{\'o}gica paraconsistente. Una breve definici{\'o}n de l{\'o}gica paraconsistente y ejemplos de cuando es usada son dados. Se definen los principios y propiedades de la l{\'o}gica multidimensional, tales como las variables l{\'o}gicas multidimensionales. Los operadores l{\'o}gicos Y, O, NO, SI... ENTONCES y SI Y S{\'O}LO SI son definidos y explicados para la l{\'o}gica multidimensional. Adem{\'a}s, se definen equivalencia, grado de contradicci{\'o}n, y la proyecci{\'o}n de la l{\'o}gica multidimensional en la difusa. Esto incluye un peque{\~n}o programa que},
	url = {http://tinyurl.com/y9hb4e},
	author = {Carlos Gershenson}
}
