393 research outputs found
Automated Problem Decomposition for the Boolean Domain with Genetic Programming
Researchers have been interested in exploring the regularities and modularity of the problem space in genetic programming (GP) with the aim of decomposing the original problem into several smaller subproblems. The main motivation is to allow GP to deal with more complex problems. Most previous works on modularity in GP emphasise the structure of modules used to encapsulate code and/or promote code reuse, instead of in the decomposition of the original problem. In this paper we propose a problem decomposition strategy that allows the use of a GP search to find solutions for subproblems and combine the individual solutions into the complete solution to the problem
Directional adposition use in English, Swedish and Finnish
Directional adpositions such as to the left of describe where a Figure is in relation to a Ground. English and Swedish directional adpositions refer to the location of a Figure in relation to a Ground, whether both are static or in motion. In contrast, the Finnish directional adpositions edellÀ (in front of) and jÀljessÀ (behind) solely describe the location of a moving Figure in relation to a moving Ground (Nikanne, 2003).
When using directional adpositions, a frame of reference must be assumed for interpreting the meaning of directional adpositions. For example, the meaning of to the left of in English can be based on a relative (speaker or listener based) reference frame or an intrinsic (object based) reference frame (Levinson, 1996). When a Figure and a Ground are both in motion, it is possible for a Figure to be described as being behind or in front of the Ground, even if neither have intrinsic features. As shown by Walker (in preparation), there are good reasons to assume that in the latter case a motion based reference frame is involved. This means that if Finnish speakers would use edellÀ (in front of) and jÀljessÀ (behind) more frequently in situations where both the Figure and Ground are in motion, a difference in reference frame use between Finnish on one hand and English and Swedish on the other could be expected.
We asked native English, Swedish and Finnish speakersâ to select adpositions from a language specific list to describe the location of a Figure relative to a Ground when both were shown to be moving on a computer screen. We were interested in any differences between Finnish, English and Swedish speakers.
All languages showed a predominant use of directional spatial adpositions referring to the lexical concepts TO THE LEFT OF, TO THE RIGHT OF, ABOVE and BELOW. There were no differences between the languages in directional adpositions use or reference frame use, including reference frame use based on motion.
We conclude that despite differences in the grammars of the languages involved, and potential differences in reference frame system use, the three languages investigated encode Figure location in relation to Ground location in a similar way when both are in motion.
Levinson, S. C. (1996). Frames of reference and Molyneuxâs question: Crosslingiuistic evidence. In P. Bloom, M.A. Peterson, L. Nadel & M.F. Garrett (Eds.) Language and Space (pp.109-170). Massachusetts: MIT Press.
Nikanne, U. (2003). How Finnish postpositions see the axis system. In E. van der Zee & J. Slack (Eds.), Representing direction in language and space. Oxford, UK: Oxford University Press.
Walker, C. (in preparation). Motion encoding in language, the use of spatial locatives in a motion context. Unpublished doctoral dissertation, University of Lincoln, Lincoln. United Kingdo
Extensibility of Enterprise Modelling Languages
Die Arbeit adressiert insgesamt drei Forschungsschwerpunkte. Der erste Schwerpunkt setzt sich mit zu entwickelnden BPMN-Erweiterungen auseinander und stellt deren methodische Implikationen im Rahmen der bestehenden Sprachstandards dar. Dies umfasst zum einen ganz konkrete Spracherweiterungen wie z. B. BPMN4CP, eine BPMN-Erweiterung zur multi-perspektivischen Modellierung von klinischen Behandlungspfaden. Zum anderen betrifft dieser Teil auch modellierungsmethodische Konsequenzen, um parallel sowohl die zugrunde liegende Sprache (d. h. das BPMN-Metamodell) als auch die Methode zur Erweiterungsentwicklung zu verbessern und somit den festgestellten UnzulÀnglichkeiten zu begegnen.
Der zweite Schwerpunkt adressiert die Untersuchung von sprachunabhĂ€ngigen Fragen der Erweiterbarkeit, welche sich entweder wĂ€hrend der Bearbeitung des ersten Teils ergeben haben oder aus dessen Ergebnissen induktiv geschlossen wurden. Der Forschungsschwerpunkt fokussiert dabei insbesondere eine Konsolidierung bestehender Terminologien, die Beschreibung generisch anwendbarer Erweiterungsmechanismen sowie die nutzerorientierte Analyse eines potentiellen Erweiterungsbedarfs. Dieser Teil bereitet somit die Entwicklung einer generischen Erweiterungsmethode grundlegend vor. Hierzu zĂ€hlt auch die fundamentale Auseinandersetzung mit Unternehmensmodellierungssprachen generell, da nur eine ganzheitliche, widerspruchsfreie und integrierte Sprachdefinition Erweiterungen ĂŒberhaupt ermöglichen und gelingen lassen kann. Dies betrifft beispielsweise die Spezifikation der intendierten Semantik einer Sprache
Baldwinian accounts of language evolution
Since Hinton & Nowlan published their seminal paper (Hinton & Nowlan 1987), the
neglected evolutionary process of the Baldwin effect has been widely acknowledged.
Especially in the field of language evolution, the Baldwin effect (Baldwin 1896d,
Simpson 1953) has been expected to salvage the long-lasting deadlocked situation of
modern linguistics: i.e., it may shed light on the relationship between environment
and innateness in the formation of language.However, as intense research of this evolutionary theory goes on, certain robust
difficulties have become apparent. One example is genotype-phenotype correlation.
By computer simulations, both Yamauchi (1999, 2001) and Mayley (19966) show
that for the Baldwin effect to work legitimately, correlation between genotypes and
phenotypes is the most essential underpinning. This is due to the fact that this type
of the Baldwin effect adopts as its core mechanism Waddington's (1975) "genetic
assimilation". In this mechanism, phenocopies have to be genetically closer to the
innately predisposed genotype. Unfortunately this is an overly naiive assumption
for the theory of language evolution. As a highly complex cognitive ability, the
possibility that this type of genotype-phenotype correlation exists in the domain of
linguistic ability is vanishingly small.In this thesis, we develop a new type of mechanism, called "Baldwinian Niche
Construction (BNC), that has a rich explanatory power and can potentially overÂŹ
come this bewildering problem of the Baldwin effect. BNC is based on the theory
of niche construction that has been developed by Odling-Smee et al. (2003). The
incorporation of the theory into the Baldwin effect was first suggested by Deacon
(1997) and briefly introduced by Godfrey-Smith (2003). However, its formulation
is yet incomplete.In the thesis, first, we review the studies of the Baldwin effect in both biology
and the study of language evolution. Then the theory of BNC is more rigorously
developed. Linguistic communication has an intrinsic property that is fundamentally described in the theory of niche construction. This naturally leads us to the
theoretical necessity of BNC in language evolution. By creating a new linguistic
niche, learning discloses a previously hidden genetic variance on which the Baldwin
'canalizing' effect can take place. It requires no genetic modification in a given
genepool. There is even no need that genes responsible for learning occupy the
same loci as genes for the innate linguistic knowledge. These and other aspects of
BNC are presented with some results from computer simulations
Chomskyan (R)evolutions
It is not unusual for contemporary linguists to claim that âModern Linguistics began in 1957â (with the publication of Noam Chomskyâs Syntactic Structures). Some of the essays in Chomskyan (R)evolutions examine the sources, the nature and the extent of the theoretical changes Chomsky introduced in the 1950s. Other contributions explore the key concepts and disciplinary alliances that have evolved considerably over the past sixty years, such as the meanings given for âUniversal Grammarâ, the relationship of Chomskyan linguistics to other disciplines (Cognitive Science, Psychology, Evolutionary Biology), and the interactions between mainstream Chomskyan linguistics and other linguistic theories active in the late 20th century: Functionalism, Generative Semantics and Relational Grammar. The broad understanding of the recent history of linguistics points the way towards new directions and methods that linguistics can pursue in the future
Approximate text generation from non-hierarchical representations in a declarative framework
This thesis is on Natural Language Generation. It describes a linguistic realisation
system that translates the semantic information encoded in a conceptual graph into an
English language sentence. The use of a non-hierarchically structured semantic representation (conceptual graphs) and an approximate matching between semantic structures allows us to investigate a more general version of the sentence generation problem
where one is not pre-committed to a choice of the syntactically prominent elements in
the initial semantics. We show clearly how the semantic structure is declaratively related to linguistically motivated syntactic representation â we use D-Tree Grammars
which stem from work on Tree-Adjoining Grammars. The declarative specification of
the mapping between semantics and syntax allows for different processing strategies
to be exploited. A number of generation strategies have been considered: a pure topdown strategy and a chart-based generation technique which allows partially successful
computations to be reused in other branches of the search space. Having a generator
with increased paraphrasing power as a consequence of using non-hierarchical input
and approximate matching raises the issue whether certain 'better' paraphrases can be
generated before others. We investigate preference-based processing in the context of
generation
Evolutionary design of deep neural networks
MenciĂłn Internacional en el tĂtulo de doctorFor three decades, neuroevolution has applied evolutionary computation to the optimization of
the topology of artificial neural networks, with most works focusing on very simple architectures.
However, times have changed, and nowadays convolutional neural networks are the industry and
academia standard for solving a variety of problems, many of which remained unsolved before the
discovery of this kind of networks.
Convolutional neural networks involve complex topologies, and the manual design of these
topologies for solving a problem at hand is expensive and inefficient. In this thesis, our aim is to
use neuroevolution in order to evolve the architecture of convolutional neural networks.
To do so, we have decided to try two different techniques: genetic algorithms and grammatical
evolution. We have implemented a niching scheme for preserving the genetic diversity, in order
to ease the construction of ensembles of neural networks. These techniques have been validated
against the MNIST database for handwritten digit recognition, achieving a test error rate of 0.28%,
and the OPPORTUNITY data set for human activity recognition, attaining an F1 score of 0.9275.
Both results have proven very competitive when compared with the state of the art. Also, in all
cases, ensembles have proven to perform better than individual models.
Later, the topologies learned for MNIST were tested on EMNIST, a database recently introduced
in 2017, which includes more samples and a set of letters for character recognition. Results have
shown that the topologies optimized for MNIST perform well on EMNIST, proving that architectures
can be reused across domains with similar characteristics.
In summary, neuroevolution is an effective approach for automatically designing topologies for
convolutional neural networks. However, it still remains as an unexplored field due to hardware
limitations. Current advances, however, should constitute the fuel that empowers the emergence of
this field, and further research should start as of today.This Ph.D. dissertation has been partially supported by the Spanish Ministry of Education, Culture and Sports under FPU fellowship with identifier FPU13/03917.
This research stay has been partially co-funded by the Spanish Ministry of Education, Culture and Sports under FPU short stay grant with identifier EST15/00260.Programa Oficial de Doctorado en Ciencia y TecnologĂa InformĂĄticaPresidente: MarĂa Araceli SanchĂs de Miguel.- Secretario: Francisco Javier Segovia PĂ©rez.- Vocal: Simon Luca
Constructivist Artificial Intelligence With Genetic Programming
Learning is an essential attribute of an intelligent system. A proper understanding of the process of learning in terms of knowledge-acquisition, processing and its effective use has been one of the main goals of artificial intelligence (AI). AI, in order to achieve the desired flexibility, performance levels and wide applicability should explore and exploit a variety of learning techniques and representations. Evolutionary algorithms, in recent years, have emerged as powerful learning methods employing task-independent approaches to problem solving and are potential candidates for implementing adaptive computational models. These algorithms, due to their attractive features such as implicit and explicit parallelism, can also be powerful meta-leaming tools for other learning systems such as connectionist networks. These networks, also known as artificial neural networks, offer a paradigm for learning at an individual level that provide an extremely rich landscape of learning mechanisms which AI should exploit. The research proposed in this thesis investigates the role of genetic programming (GP) in connectionism, a learning paradigm that, despite being extremely powerful has a number of limitations. The thesis, by systematically identifying the reasons for these limitations has argued as to why connectionism should be approached with a new perspective in order to realize its true potentialities. With genetic-based designs the key issue has been the encoding strategy. That is, how to encode a neural network within a genotype so as to achieve an optimum network structure and/ or an efficient learning that can best solve a given problem. This in turn raises a number of key questions such as: 1. Is the representation (that is the genotype) that the algorithms employ sufficient to express and explore the vast space of network architectures and learning mechanisms? 2. Is the representation capable of capturing the concepts of hierarchy and modularity that are vital and so naturally employed by humans in problem-solving? 3. Are some representations better in expressing these? If so, how to exploit the strengths that are inherent to these representations? 4. If the aim is really to automate the design process what strategies should be employed so as to minimize the involvement of a designer in the design loop? 5. Is the methodology or the approach able to overcome at least some of the limitations that are commonly seen in connectionist networks? 6. Most importantly, how effective is the approach in problem-solving? These issues are investigated through a novel approach that combines genetic programming and a self-organizing neural network which provides a framework for the simulations. Through the powerful notions of constructivism and micro-macro dynamics the approach provides a way of exploiting the potential features (such as the hierarchy and modularity) that are inherent to the representation that GP employs. By providing a general definition for learning and by imposing a single potential constraint within the representation the approach demonstrates that genetic programming, if used for construction and optimization, could be extremely creative. The method also combines the bottom-up and top-down strategies that are key to evolve ALife-like systems. A comparison with earlier methods is drawn to identify the merits of the proposed approach. A pattern recognition task is considered for illustration. Simulations suggest that genetic- programming can be a powerful meta-leaming tool for implementing useful network architectures and flexible learning mechanisms for self-organizing neural networks while interacting with a given task environment. It appears that it is possible to extend the novel approach further to other types of networks. Finally the role of flexible learning in implementing adaptive AI systems is discussed. A number of potential applications domain is identified
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