Measuring Relations Between Concepts In Conceptual Spaces

The highly influential framework of conceptual spaces provides a geometric way of representing knowledge. Instances are represented by points in a high-dimensional space and concepts are represented by regions in this space. Our recent mathematical formalization of this framework is capable of representing correlations between different domains in a geometric way. In this paper, we extend our formalization by providing quantitative mathematical definitions for the notions of concept size, subsethood, implication, similarity, and betweenness. This considerably increases the representational power of our formalization by introducing measurable ways of describing relations between concepts.Comment: Accepted at SGAI 2017 (http://www.bcs-sgai.org/ai2017/). The final publication is available at Springer via https://doi.org/10.1007/978-3-319-71078-5_7. arXiv admin note: substantial text overlap with arXiv:1707.05165, arXiv:1706.0636

Formalized Conceptual Spaces with a Geometric Representation of Correlations

The highly influential framework of conceptual spaces provides a geometric way of representing knowledge. Instances are represented by points in a similarity space and concepts are represented by convex regions in this space. After pointing out a problem with the convexity requirement, we propose a formalization of conceptual spaces based on fuzzy star-shaped sets. Our formalization uses a parametric definition of concepts and extends the original framework by adding means to represent correlations between different domains in a geometric way. Moreover, we define various operations for our formalization, both for creating new concepts from old ones and for measuring relations between concepts. We present an illustrative toy-example and sketch a research project on concept formation that is based on both our formalization and its implementation.Comment: Published in the edited volume "Conceptual Spaces: Elaborations and Applications". arXiv admin note: text overlap with arXiv:1706.06366, arXiv:1707.02292, arXiv:1707.0516

Full Envelope Control of Nonlinear Plants with Parameter Uncertainty by Fuzzy Controller Scheduling

A full envelope controller synthesis technique is developed for multiple-input single-output (MISO) nonlinear systems with structured parameter uncertainty. The technique maximizes the controller\u27s valid region of operation, while guaranteeing pre-specified transient performance. The resulting controller does not require on-line adaptation, estimation, prediction or model identification. Fuzzy Logic (FL) is used to smoothly schedule independently designed point controllers over the operational envelope and parameter space of the system\u27s model. These point controllers are synthesized using techniques chosen by the designer, thus allowing an unprecedented amount of design freedom. By using established control theory for the point controllers, the resulting nonlinear dynamic controller is able to handle the dynamics of complex systems which can not otherwise be addressed by Fuzzy Logic Control. An analytical solution for parameters describing the membership functions allows the optimization to yield the location of point designs: both quantifying the controller\u27s coverage, and eliminating the need of extensive hand tuning of these parameters. The net result is a decrease in the number of point designs required. Geometric primitives used in the solution all have multi-dimensional interpretations (convex hull, ellipsoid, Voronoi-Delaunay diagrams) which allow for scheduling on n-dimensions, including uncertainty due to nonlinearities and parameter variation. Since many multiple-input multiple-output (MIMO) controller design techniques are accomplished by solving several MISO problems, this work bridges the gap to full envelope control of MIMO nonlinear systems with parameter variation

Emerging properties of signaling networks in cancer: a data-derived modeling approach

Mammalian signal transduction pathways are highly integrated within extended networks, with crosstalk emerging in space and time. This dynamic circuitry is dependent on changing activity states for proteins and organelles. Network structures govern specificity of cellular responses to external stimuli, including proliferation and cell death. Loss of regulation virtually underlies all disease. However, while the contributions of individual components to phenotype are mostly well understood, systematic elucidation for the emergence or loss of crosstalk and impact on phenotype remains a fundamental challenge in classical biology that can be investigated by systems biology. To that end, we established a mathematical modeling platform, at the interface between experimental and theoretical approaches, to integrate prior literature knowledge with high-content, heterogeneous datasets for the non-intuitive prediction of adaptive signaling events. In the first part of this work, we investigated high-content microscopy datasets of morphological, bio-energetic and functional features of mitochondria in response to pro- apoptotic treatment in MCF-7 breast cancer cells. Data pretreatment techniques were used to unify the heterogeneous datasets. Using fuzzy logic, we established a generalized data-driven modeling formalism to model signaling events solely based on measurements, capable of high simulation accuracy via non-discrete rule sets. Employing neural networks, a generalized fuzzy logic system, i.e. its rules and membership functions, could be parameterized for each potential signaling interaction. An exhaustive search approach identified models with least error, i.e. the most related signaling events, and predicted a hierarchy of apoptotic events, in which upon activation of pro-apoptotic Bax, mitochondrial fragmentation propagates apoptosis, which is consistent with reported literature. Hence, we established a predictive approach for investigation of protein and organelle interactions utilizing cell-to-cell heterogeneity, a critical source of biologically relevant information. In the second part of this work, we sought to identify network evolution in the topology of MAPK signaling in the A-375 melanoma cell line. To that end, the modeling method was extended to incorporate temporal and topological structure from phosphorylation profiles of key MAPK intermediates treated with different pharmacological inhibitors and acquired over 96 hours. To increase prediction power, a parameter reduction strategy was developed to identify and fix parameters with lowest contribution to model performance. Therefore, training datasets were bootstrapped and signatures of deviation in flexibility and accuracy were calculated. This novel strategy achieved an optimal set of free parameters. Finally, a reduced multi-treatment model encoding the behavior of the full MAPK dataset was systematically trained to a sequentially increasing subset of time points, enabling time-defined identification of discrepancies in reported vs. acquired network topology. To that end, an objective function for fuzzy logic model optimization was implemented, which accounted for time-defined model training. Analysis led to the identification of emerging discrepancies between model and data at specific time points, thus characterizing a potential network rearrangement upstream of MAPK kinase MEK1, consistent with studies reporting increased resistance to apoptosis exhibited by A-375 melanoma cell line. The approach presented here was successfully benchmarked against a recently published fuzzy-logic-based analysis of signal transduction

A computational intelligence approach to modelling interstate conflict : Forecasting and causal interpretations

The quantitative study of conflict management is concerned with finding models which are accurate and also capable of providing a causal interpretation of results. This dissertation applies computational intelligence methods to study interstate disputes. Both multilayer perceptron neural networks and Takagi-Sugeno neuro-fuzzy models are used to model interstate interactions. The multilayer perceptron neural network is trained in the Bayesian framework, using the Hybrid Monte Carlo method to sample from the posterior probabilities. It is found that the network is able to forecast conflict with an accuracy of 77.3%. A hybrid machine learning method using the neural network and the genetic algorithm is then presented as a method of suggesting how conflict can be brought under control. The automatic relevance determination approach and the sensitivity analysis are used as methods of extracting causal information from the neural network. The Takagi-Sugeno neuro-fuzzy model is optimised, using the Gustafson-Kessel clustering algorithm to partion the input space. It is found that the neuro-fuzzy model predicts conflict with an accuracy of 80.1%. The neuro-fuzzy model is also incorporated into the hybrid machine learning method to suggest how the identified conflict cases can be avoided. The casual interpretation is then formulated by a linguistic approximation of the fuzzy rules extracted from the neuro-fuzzy model. The major finding in this work is that the interpretations drawn from both the neural network and the neuro-fuzzy model are consistent

Evolutionary Robot Swarms Under Real-World Constraints

Tese de doutoramento em Engenharia Electrotécnica e de Computadores, na especialidade de Automação e Robótica, apresentada ao Departamento de Engenharia Electrotécnica e de Computadores da Faculdade de Ciências e Tecnologia da Universidade de CoimbraNas últimas décadas, vários cientistas e engenheiros têm vindo a estudar as estratégias provenientes da natureza. Dentro das arquiteturas biológicas, as sociedades que vivem em enxames revelam que agentes simplistas, tais como formigas ou pássaros, são capazes de realizar tarefas complexas usufruindo de mecanismos de cooperação. Estes sistemas abrangem todas as condições necessárias para a sobrevivência, incorporando comportamentos de cooperação, competição e adaptação. Na “batalha” sem fim em prol do progresso dos mecanismos artificiais desenvolvidos pelo homem, a ciência conseguiu simular o primeiro comportamento em enxame no final dos anos oitenta. Desde então, muitas outras áreas, entre as quais a robótica, beneficiaram de mecanismos de tolerância a falhas inerentes da inteligência coletiva de enxames. A área de investigação deste estudo incide na robótica de enxame, consistindo num domínio particular dos sistemas robóticos cooperativos que incorpora os mecanismos de inteligência coletiva de enxames na robótica. Mais especificamente, propõe-se uma solução completa de robótica de enxames a ser aplicada em contexto real. Nesta ótica, as operações de busca e salvamento foram consideradas como o caso de estudo principal devido ao nível de complexidade associado às mesmas. Tais operações ocorrem tipicamente em cenários dinâmicos de elevadas dimensões, com condições adversas que colocam em causa a aplicabilidade dos sistemas robóticos cooperativos. Este estudo centra-se nestes problemas, procurando novos desafios que não podem ser ultrapassados através da simples adaptação da literatura da especialidade em algoritmos de enxame, planeamento, controlo e técnicas de tomada de decisão. As contribuições deste trabalho sustentam-se em torno da extensão do método Particle Swarm Optimization (PSO) aplicado a sistemas robóticos cooperativos, denominado de Robotic Darwinian Particle Swarm Optimization (RDPSO). O RDPSO consiste numa arquitetura robótica de enxame distribuída que beneficia do particionamento dinâmico da população de robôs utilizando mecanismos evolucionários de exclusão social baseados na sobrevivência do mais forte de Darwin. No entanto, apesar de estar assente no caso de estudo do RDPSO, a aplicabilidade dos conceitos aqui propostos não se encontra restrita ao mesmo, visto que todos os algoritmos parametrizáveis de enxame de robôs podem beneficiar de uma abordagem idêntica. Os fundamentos em torno do RDPSO são introduzidos, focando-se na dinâmica dos robôs, nos constrangimentos introduzidos pelos obstáculos e pela comunicação, e nas suas propriedades evolucionárias. Considerando a colocação inicial dos robôs no ambiente como algo fundamental para aplicar sistemas de enxames em aplicações reais, é assim introduzida uma estratégia de colocação de robôs realista. Para tal, a população de robôs é dividida de forma hierárquica, em que são utilizadas plataformas mais robustas para colocar as plataformas de enxame no cenário de forma autónoma. Após a colocação dos robôs no cenário, é apresentada uma estratégia para permitir a criação e manutenção de uma rede de comunicação móvel ad hoc com tolerância a falhas. Esta estratégia não considera somente a distância entre robôs, mas também a qualidade do nível de sinal rádio frequência, redefinindo assim a sua aplicabilidade em cenários reais. Os aspetos anteriormente mencionados estão sujeitos a uma análise detalhada do sistema de comunicação inerente ao algoritmo, para atingir uma implementação mais escalável do RDPSO a cenários de elevada complexidade. Esta elevada complexidade inerente à dinâmica dos cenários motivaram a ultimar o desenvolvimento do RDPSO, integrando para o efeito um mecanismo adaptativo baseado em informação contextual (e.g., nível de atividade do grupo). Face a estas considerações, o presente estudo pode contribuir para expandir o estado-da-arte em robótica de enxame com algoritmos inovadores aplicados em contexto real. Neste sentido, todos os métodos propostos foram extensivamente validados e comparados com alternativas, tanto em simulação como com robôs reais. Para além disso, e dadas as limitações destes (e.g., número limitado de robôs, cenários de dimensões limitadas, constrangimentos reais limitados), este trabalho contribui ainda para um maior aprofundamento do estado-da-arte, onde se propõe um modelo macroscópico capaz de capturar a dinâmica inerente ao RDPSO e, até certo ponto, estimar analiticamente o desempenho coletivo dos robôs perante determinada tarefa. Em suma, esta investigação pode ter aplicabilidade prática ao colmatar a lacuna que se faz sentir no âmbito das estratégias de enxames de robôs em contexto real e, em particular, em cenários de busca e salvamento.Over the past decades, many scientists and engineers have been studying nature’s best and time-tested patterns and strategies. Within the existing biological architectures, swarm societies revealed that relatively unsophisticated agents with limited capabilities, such as ants or birds, were able to cooperatively accomplish complex tasks necessary for their survival. Those simplistic systems embrace all the conditions necessary to survive, thus embodying cooperative, competitive and adaptive behaviours. In the never-ending battle to advance artificial manmade mechanisms, computer scientists simulated the first swarm behaviour designed to mimic the flocking behaviour of birds in the late eighties. Ever since, many other fields, such as robotics, have benefited from the fault-tolerant mechanism inherent to swarm intelligence. The area of research presented in this Ph.D. Thesis focuses on swarm robotics, which is a particular domain of multi-robot systems (MRS) that embodies the mechanisms of swarm intelligence into robotics. More specifically, this Thesis proposes a complete swarm robotic solution that can be applied to real-world missions. Although the proposed methods do not depend on any particular application, search and rescue (SaR) operations were considered as the main case study due to their inherent level of complexity. Such operations often occur in highly dynamic and large scenarios, with harsh and faulty conditions, that pose several problems to MRS applicability. This Thesis focuses on these problems raising new challenges that cannot be handled appropriately by simple adaptation of state-of-the-art swarm algorithms, planning, control and decision-making techniques. The contributions of this Thesis revolve around an extension of the Particle Swarm Optimization (PSO) to MRS, denoted as Robotic Darwinian Particle Swarm Optimization (RDPSO). The RDPSO is a distributed swarm robotic architecture that benefits from the dynamical partitioning of the whole swarm of robots by means of an evolutionary social exclusion mechanism based on Darwin’s survival-of-the-fittest. Nevertheless, although currently applied solely to the RDPSO case study, the applicability of all concepts herein proposed is not restricted to it, since all parameterized swarm robotic algorithms may benefit from a similar approach The RDPSO is then proposed and used to devise the applicability of novel approaches. The fundamentals around the RDPSO are introduced by focusing on robots’ dynamics, obstacle avoidance, communication constraints and its evolutionary properties. Afterwards, taking the initial deployment of robots within the environment as a basis for applying swarm robotics systems into real-world applications, the development of a realistic deployment strategy is proposed. For that end, the population of robots is hierarchically divided, wherein larger support platforms autonomously deploy smaller exploring platforms in the scenario, while considering communication constraints and obstacles. After the deployment, a way of ensuring a fault-tolerant multi-hop mobile ad hoc communication network (MANET) is introduced to explicitly exchange information needed in a collaborative realworld task execution. Such strategy not only considers the maximum communication range between robots, but also the minimum signal quality, thus refining the applicability to real-world context. This is naturally followed by a deep analysis of the RDPSO communication system, describing the dynamics of the communication data packet structure shared between teammates. Such procedure is a first step to achieving a more scalable implementation by optimizing the communication procedure between robots. The highly dynamic characteristics of real-world applications motivated us to ultimate the RDPSO development with an adaptive strategy based on a set of context-based evaluation metrics. This thesis contributes to the state-of-the-art in swarm robotics with novel algorithms for realworld applications. All of the proposed approaches have been extensively validated in benchmarking tasks, in simulation, and with real robots. On top of that, and due to the limitations inherent to those (e.g., number of robots, scenario dimensions, real-world constraints), this Thesis further contributes to the state-of-the-art by proposing a macroscopic model able to capture the RDPSO dynamics and, to some extent, analytically estimate the collective performance of robots under a certain task. It is the author’s expectation that this Ph.D. Thesis may shed some light into bridging the reality gap inherent to the applicability of swarm strategies to real-world scenarios, and in particular to SaR operations.FCT - SFRH/BD /73382/201