95,558 research outputs found
Proceedings of the ECCS 2005 satellite workshop: embracing complexity in design - Paris 17 November 2005
Embracing complexity in design is one of the critical issues and challenges of the 21st century. As the realization grows that design activities and artefacts display properties associated with complex adaptive systems, so grows the need to use complexity concepts and methods to understand these properties and inform the design of better artifacts. It is a great challenge because complexity science represents an epistemological and methodological swift that promises a holistic approach in the understanding and operational support of design. But design is also a major contributor in complexity research. Design science is concerned with problems that are fundamental in the sciences in general and complexity sciences in particular. For instance, design has been perceived and studied as a ubiquitous activity inherent in every human activity, as the art of generating hypotheses, as a type of experiment, or as a creative co-evolutionary process. Design science and its established approaches and practices can be a great source for advancement and innovation in complexity science. These proceedings are the result of a workshop organized as part of the activities of a UK government AHRB/EPSRC funded research cluster called Embracing Complexity in Design (www.complexityanddesign.net) and the European Conference in Complex Systems (complexsystems.lri.fr). Embracing complexity in design is one of the critical issues and challenges of the 21st century. As the realization grows that design activities and artefacts display properties associated with complex adaptive systems, so grows the need to use complexity concepts and methods to understand these properties and inform the design of better artifacts. It is a great challenge because complexity science represents an epistemological and methodological swift that promises a holistic approach in the understanding and operational support of design. But design is also a major contributor in complexity research. Design science is concerned with problems that are fundamental in the sciences in general and complexity sciences in particular. For instance, design has been perceived and studied as a ubiquitous activity inherent in every human activity, as the art of generating hypotheses, as a type of experiment, or as a creative co-evolutionary process. Design science and its established approaches and practices can be a great source for advancement and innovation in complexity science. These proceedings are the result of a workshop organized as part of the activities of a UK government AHRB/EPSRC funded research cluster called Embracing Complexity in Design (www.complexityanddesign.net) and the European Conference in Complex Systems (complexsystems.lri.fr)
Partition strategies for incremental Mini-Bucket
Los modelos en grafo probabilĂsticos, tales como los campos aleatorios de
Markov y las redes bayesianas, ofrecen poderosos marcos de trabajo para la
representaciĂłn de conocimiento y el razonamiento en modelos con gran nĂşmero
de variables. Sin embargo, los problemas de inferencia exacta en modelos de
grafos son NP-hard en general, lo que ha causado que se produzca bastante
interĂŠs en mĂŠtodos de inferencia aproximados.
El mini-bucket incremental es un marco de trabajo para inferencia aproximada
que produce como resultado lĂmites aproximados inferior y superior de la
funciĂłn de particiĂłn exacta, a base de -empezando a partir de un modelo con
todos los constraints relajados, es decir, con las regiones mĂĄs pequeĂąas posibleincrementalmente
aĂąadir regiones mĂĄs grandes a la aproximaciĂłn. Los mĂŠtodos
de inferencia aproximada que existen actualmente producen lĂmites superiores
ajustados de la funciĂłn de particiĂłn, pero los lĂmites inferiores suelen ser demasiado
imprecisos o incluso triviales.
El objetivo de este proyecto es investigar estrategias de particiĂłn que mejoren
los lĂmites inferiores obtenidos con el algoritmo de mini-bucket, trabajando dentro
del marco de trabajo de mini-bucket incremental.
Empezamos a partir de la idea de que creemos que deberĂa ser beneficioso
razonar conjuntamente con las variables de un modelo que tienen una alta correlaciĂłn,
y desarrollamos una estrategia para la selecciĂłn de regiones basada en
esa idea. Posteriormente, implementamos nuestra estrategia y exploramos formas
de mejorarla, y finalmente medimos los resultados obtenidos usando nuestra
estrategia y los comparamos con varios mĂŠtodos de referencia.
Nuestros resultados indican que nuestra estrategia obtiene lĂmites inferiores
mĂĄs ajustados que nuestros dos mĂŠtodos de referencia. TambiĂŠn consideramos
y descartamos dos posibles hipĂłtesis que podrĂan explicar esta mejora.Els models en graf probabilĂstics, com bĂŠ els camps aleatoris de Markov i les
xarxes bayesianes, ofereixen poderosos marcs de treball per la representaciĂł
del coneixement i el raonament en models amb grans quantitats de variables.
Tanmateix, els problemes dâinferència exacta en models de grafs son NP-hard
en general, el qual ha provocat que es produeixi bastant dâinterès en mètodes
dâinferència aproximats.
El mini-bucket incremental es un marc de treball per a lâinferència aproximada
que produeix com a resultat lĂmits aproximats inferior i superior de la
funciĂł de particiĂł exacta que funciona començant a partir dâun model al qual
se li han relaxat tots els constraints -ĂŠs a dir, un model amb les regions mĂŠs
petites possibles- i anar afegint a lâaproximaciĂł regions incrementalment mĂŠs
grans. Els mètodes dâinferència aproximada que existeixen actualment produeixen
lĂmits superiors ajustats de la funciĂł de particiĂł. Tanmateix, els lĂmits
inferiors acostumen a ser massa imprecisos o fins aviat trivials.
El objectiu dâaquest projecte es recercar estratègies de particiĂł que millorin
els lĂmits inferiors obtinguts amb lâalgorisme de mini-bucket, treballant dins del
marc de treball del mini-bucket incremental.
La nostra idea de partida pel projecte es que creiem que hauria de ser beneficiĂłs
per la qualitat de lâaproximaciĂł raonar conjuntament amb les variables del
model que tenen una alta correlació entre elles, i desenvolupem una estratègia
per a la selecciĂł de regions basada en aquesta idea. Posteriorment, implementem
la nostra estratègia i explorem formes de millorar-la, i finalment mesurem els
resultats obtinguts amb la nostra estratègia i els comparem a diversos mètodes
de referència.
Els nostres resultats indiquen que la nostra estratègia obtĂŠ lĂmits inferiors
mÊs ajustats que els nostres dos mètodes de referència. TambÊ considerem i
descartem dues possibles hipòtesis que podrien explicar aquesta millora.Probabilistic graphical models such as Markov random fields and Bayesian networks
provide powerful frameworks for knowledge representation and reasoning
over models with large numbers of variables. Unfortunately, exact inference
problems on graphical models are generally NP-hard, which has led to signifi-
cant interest in approximate inference algorithms.
Incremental mini-bucket is a framework for approximate inference that provides
upper and lower bounds on the exact partition function by, starting from
a model with completely relaxed constraints, i.e. with the smallest possible
regions, incrementally adding larger regions to the approximation. Current
approximate inference algorithms provide tight upper bounds on the exact partition
function but loose or trivial lower bounds.
This project focuses on researching partitioning strategies that improve the
lower bounds obtained with mini-bucket elimination, working within the framework
of incremental mini-bucket.
We start from the idea that variables that are highly correlated should be
reasoned about together, and we develop a strategy for region selection based
on that idea. We implement the strategy and explore ways to improve it, and
finally we measure the results obtained using the strategy and compare them to
several baselines.
We find that our strategy performs better than both of our baselines. We
also rule out several possible explanations for the improvement
The Environmental Contribution to Wayfinding in Museums: Enhancement and Usage by Controlling Flows and Paths
The field of research in which wayfinding is situated refers to the way people move in reaction to environmental stimulation. It therefore fully concerns not just signage but also space designing, its geometric configuration, technical solutions and their material characterization. The focus is consequently on environmental factors that facilitate wayfinding in a museum (accessibility, visibility, etc.) and on other elements such as spatial configuration, architectural features and functional aspects. These factors influence relational phenomena and therefore visitorsâ satisfaction. Methods and tools for designing and managing spaces have been studied in the research. The configurational analysis method of space has been used to objectify syntactic features of space. In particular, the outcomes of an experimental project, which have been analyzed in a masterâs thesis on the re-functionalization of the museum of Palazzo dei Diamanti in Ferrara, are presented. Permeability, proximity, connections of spaces, namely meaningful features to ensure wayfinding have been examined. Space parameters resulting from the geometry of the layout, from the visual connections and from the changes of direction were then evaluated. The outcomes have been used as inputs for designing a unitary tour route circuit, that also reconnects the museumâs second floor, and for planning three independent alternative routes for a differentiated use of the museum
An Empirical Study of a Software Maintenance Process
This paper describes how a process support tool is used to collect metrics about a major upgrade to our own electronic retail system. An incremental prototyping lifecycle is adopted in which each increment is categorised by an effort type and a project component. Effort types are Acquire, Build, Comprehend and Design and span all phases of development. Project components include data models and process models expressed in an OO modelling language and process algebra respectively as well as C++ classes and function templates and build components including source files and data files. This categorisation is independent of incremental prototyping and equally applicable to other software lifecycles. The process support tool (PWI) is responsible for ensuring the consistency between the models and the C++ source. It also supports the interaction between multiple developers and multiple metric-collectors. The first two releases of the retailing software are available for ftp from oracle.ecs.soton.ac.uk in directory pub/peter. Readers are invited to use the software and apply their own metrics as appropriate. We would be interested to correspond with anyone who does so
A Hybrid Three Layer Architecture for Fire Agent Management in Rescue Simulation Environment
This paper presents a new architecture called FAIS for imple- menting
intelligent agents cooperating in a special Multi Agent environ- ment, namely
the RoboCup Rescue Simulation System. This is a layered architecture which is
customized for solving fire extinguishing problem. Structural decision making
algorithms are combined with heuristic ones in this model, so it's a hybrid
architecture
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