Theory Of Computation

Encoding Universal Computation in the Ground States of Ising Lattices

by Mile Gu

Mnemosyne, Metaphor and Theory of Mind An Imaginative Visual Essay of Computionalism

by Marcio Alves da Rocha

Transtechnology Research • Reader 2011 Plymouth University

This essay will explore historic principles of a Computational Theory of Mind and metaphor as a cognitive process. The... more This essay will explore historic principles of a Computational Theory of Mind and metaphor as a cognitive process. The conceptual metaphor developed by Lakoff and Johnson states that “our ordinary conceptual system, in terms of how we both think and act, is fundamentally metaphorical in nature” (1980, p. 3) to define our representational system and understand the natural world. Computational Theory of Mind is a historical view in philosophy in which the human mind ought to be conceived as an information processing system, considering that thought is a form of computation. Externalist theory versions are explored in this essay also, highlighting the tension between central dilemmas and different notions on the subject. Informed by the way that Warburg proposed to represent part of the history of art through juxtaposed images, this essay seeks to open up the possibility to reflect on the history of Computational Theory of Mind, using metaphors and juxtaposed images and will result in visual insights in to the detriment of exclusively textual as evidenced by Warburg in his Mnemosyne Atlas.
One of Warburg’s contributions to the history of art through the Mnemosyne Atlas, a contribution which later became more explicit in a science of images, was based on diametrically opposed criteria rather than a pure formalism, and broke with the continuum of art history’s traditionally established chronological and hermetic hierarchy. Warburg positioned images to uncover the polarity of the form within incidental ephemera, such as postage stamps and printed materials, constructing imaginative metaphors and uncovering the interpretative energy within them, making metaphor underlying for the work that he proposed. Through his unfinished Mnemosyne Atlas, Warburg practised a polarised iconography through images meticulously juxtaposed, reconfiguring the production of human knowledge and understanding, and questioning the meaning of images, as evidenced by the emotive potential each project gathered in his unfinished Atlas (Grau, 2004).
This essay will deal with the following key topics: Computationalism, Functionalism, Behaviourism, Connectionism, Embodiment and Enactivism.
Although the function of the essay is to explore aspects of Computational Theory of Mind it will not be completely detached from the personal/authorial view of the author.

More really is different

by Mile Gu

Quantum complexity, Emergence and Computation by Measurement: On what computers reveal about physical laws, and what physical laws reveal about computers

by Mile Gu

Declerck, G., Charlet, J. (2011). Intelligence Artificielle, ontologies et connaissances en médecine. Les limites de la mécanisation de la pensée

by Gunnar Declerck

Prepublication version
Complete reference :
Declerck, G., Charlet, J. (2011). Intelligence Artificielle, ontologies et connaissances en médecine. Les limites de la mécanisation de la pensée. Revue d’Intelligence Artificielle (RIA), vol. 25, n°4, pp. 445-472, n° spécial « Intelligence artificielle et santé »

This theoretical article aims to draw up an inventory of the latest advances in medical knowledge engineering in the... more This theoretical article aims to draw up an inventory of the latest advances in medical knowledge engineering in the specific area of ontologies and knowledge based systems design. Echoing the debates that animated the landscape of Artificial Intelligence (AI) from the 1970s under the impetus of Dreyfus HL, it aims to show that most of the difficulties currently faced by medical knowledge engineering are inherent in the nature of AI, whose project is the mechanization of cognitive activity. As such it promotes the idea that only a fair understanding of what machines can do, given their machinic character itself, and remains, despite its cognitive finitude, a property of human being, may offer to balancing the scales between tasks that can be allocated to machines and those that have to be left in charge of humans.

Explaining Experience in Nature: The Foundations of Logic and Apprehension

by Steven Ericsson-Zenith

Mente & computazione

by Ignazio Licata

Applications and Methodology of nuZ

by Martin Henson

Computation Over Arbitrary Models of Time (a Unified Model of Discrete, Analog, Quantum and Hybrid Computation)

by Mike Stannett

A Combinatory Account of Internal Structure

by Thomas Given-Wilson

Draft, final version to appear in the Journal of Symbolic logic

Traditional combinatory logic uses combinators S and K to represent all Turing-computable functions on natural... more Traditional combinatory logic uses combinators S and K to represent all Turing-computable functions on natural numbers, but there are Turing-computable functions on the combinators themselves that cannot be so represented, because they have
direct access to the internal structure of their arguments. Much of this expressive power is captured by adding a factorisation combinator F. The resulting SF-calculus is structure complete, in that it supports all pattern-matching functions whose patterns are in normal form, including a function that decides structural equality of arbitrary normal forms. A general characterisation of the structure complete, confluent combinatory calculi is given along with some examples. These are able to represent all their Turing-computable functions whose domain is limited to normal forms. The combinator F can be typed using an existential type to represent internal type information.

Computing networks: A general framework to contrast neural and swarm cognitions

by Carlos Gershenson

PALADYN Volume 1, Number 2, 147-153, DOI: 10.2478/s13230-010-0015-z

This paper presents the Computing Networks (CNs) framework. CNs are used to generalize neural and swarm architectures.... more This paper presents the Computing Networks (CNs) framework. CNs are used to generalize neural and swarm architectures. Artificial neural networks, ant colony optimization, particle swarm optimization, and realistic biological models are used as examples of instantiations of CNs. The description of these architectures as CNs allows their comparison. Their differences and similarities allow the identification of properties that enable neural and swarm architectures to perform complex computations and exhibit complex cognitive abilities. In this context, the most relevant characteristics of CNs are the existence multiple dynamical and functional scales. The relationship between multiple dynamical and functional scales with adaptation, cognition (of brains and swarms) and computation is discussed.

A formalization of geometric constraint systems and their decomposition

by Simon E.B. Thierry

Formal Aspects of Computing, 22(2):129-151, co-authored with Pascal Mathis

For more than a decade, the trend in geometric constraint systems solving has been to use a geometric... more For more than a decade, the trend in geometric constraint systems solving has been to use a geometric decomposition/recombination approach. These methods are generally grounded on the invariance of systems under rigid motions. To decompose further, other invariance groups (e.g. scalings) have recently been considered.

Geometric decomposition is grounded on the possibility to replace a solved subsystem with a smaller system called boundary.
This article shows the central property which justifies decomposition, without assuming specific types of constraints or invariance groups. The exact nature of the boundary system is given.

This formalization brings out the elements of a general and modular implementation.