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Machine learning : techniques and foundations
The field of machine learning studies computational methods for acquiring new knowledge, new skills, and new ways to organize existing knowledge. In this paper we present some of the basic techniques and principles that underlie AI research on learning, including methods for learning from examples, learning in problem solving, learning by analogy, grammar acquisition, and machine discovery. In each case, we illustrate the techniques with paradigmatic examples
Learning cognitive maps: Finding useful structure in an uncertain world
In this chapter we will describe the central mechanisms that influence how people learn about large-scale space. We will focus particularly on how these mechanisms enable people to effectively cope with both the uncertainty inherent in a constantly changing world and also with the high information content of natural environments. The major lessons are that humans get by with a less is more approach to building structure, and that they are able to quickly adapt to environmental changes thanks to a range of general purpose mechanisms. By looking at abstract principles, instead of concrete implementation details, it is shown that the study of human learning can provide valuable lessons for robotics. Finally, these issues are discussed in the context of an implementation on a mobile robot. © 2007 Springer-Verlag Berlin Heidelberg
Text mining processing pipeline for semi structured data D3.3
Unstructured and semi-structured cohort data contain relevant information about the health condition of a patient, e.g., free text describing disease diagnoses, drugs, medication reasons, which are often not available in structured formats. One of the challenges posed by medical free texts is that there can be several ways of mentioning a concept. Therefore, encoding free text into unambiguous descriptors allows us to leverage the value of the cohort data, in particular, by facilitating its findability and interoperability across cohorts in the project.Named entity recognition and normalization enable the automatic conversion of free text into standard medical concepts. Given the volume of available data shared in the CINECA project, the WP3 text mining working group has developed named entity normalization techniques to obtain standard concepts from unstructured and semi-structured fields available in the cohorts. In this deliverable, we present the methodology used to develop the different text mining tools created by the dedicated SFU, UMCG, EBI, and HES-SO/SIB groups for specific CINECA cohorts
Multi-Agent Fitness Functions For Evolutionary Architecture
The dynamics of crowd movements are self-organising and often involve complex pattern formations.
Although computational models have recently been developed, it is unclear how
well their underlying methods capture local dynamics and longer-range aspects, such as evacuation.
A major part of this thesis is devoted to an investigation of current methods, and
where required, the development of alternatives. The main purpose is to utilise realistic models
of pedestrian crowds in the design of fitness functions for an evolutionary approach to
architectural design.
We critically review the state-of-the-art in pedestrian and evacuation dynamics. The concept
of 'Multi-Agent System' embraces a number of approaches, which together encompass
important local and longer-range aspects. Early investigations focus on methods-cellular
automata and attractor fields-designed to capture these respective levels.
The assumption that pattern formations in crowds result from local processes is reflected in
two dimensional cellular automata models, where mathematical rules operate in local neighbourhoods.
We investigate an established cellular automata and show that lane-formation
patterns are stable only in a low-valued density range. Above this range, such patterns suddenly
randomise. By identifying and then constraining the source of this randomness, we
are only able to achieve a small degree of improvement. Moreover, when we try to integrate
the model with attractor fields, no useful behaviour is achieved, and much of the randomness
persists. Investigations indicate that the unwanted randomness is associated with 2-lattice
phase transitions, where local dynamics get invaded by giant-component clusters during the
onset of lattice percolation. Through this in-depth investigation, the general limits to cellular
automata are ascertained-these methods are not designed with lattice percolation properties
in mind and resulting models depend, often critically, on arbitrarily chosen neighbourhoods.
We embark on the development of new and more flexible methodologies. Rather than
treating local and global dynamics as separate entities, we combine them. Our methods
are responsive to percolation, and are designed around the following principles: 1) Inclusive
search provides an optimal path between a pedestrian origin and destination. 2) Dynamic
boundaries protect search and are based on percolation probabilities, calculated from local
density regimes. In this way, more robust dynamics are achieved. Simultaneously, longer-range
behaviours are also specified. 3) Network-level dynamics further relax the constraints
of lattice percolation and allow a wider range of pedestrian interactions.
Having defined our methods, we demonstrate their usefulness by applying them to lane-formation
and evacuation scenarios. Results reproduce the general patterns found in real
crowds.
We then turn to evolution. This preliminary work is intended to motivate future research in
the field of Evolutionary Architecture. We develop a genotype-phenotype mapping, which produces
complex architectures, and demonstrate the use of a crowd-flow model in a phenotype-fitness
mapping. We discuss results from evolutionary simulations, which suggest that obstacles
may have some beneficial effect on crowd evacuation. We conclude with a summary,
discussion of methodological limitations, and suggestions for future research
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
This paper surveys the current state of the art in Natural Language
Generation (NLG), defined as the task of generating text or speech from
non-linguistic input. A survey of NLG is timely in view of the changes that the
field has undergone over the past decade or so, especially in relation to new
(usually data-driven) methods, as well as new applications of NLG technology.
This survey therefore aims to (a) give an up-to-date synthesis of research on
the core tasks in NLG and the architectures adopted in which such tasks are
organised; (b) highlight a number of relatively recent research topics that
have arisen partly as a result of growing synergies between NLG and other areas
of artificial intelligence; (c) draw attention to the challenges in NLG
evaluation, relating them to similar challenges faced in other areas of Natural
Language Processing, with an emphasis on different evaluation methods and the
relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118
pages, 8 figures, 1 tabl
Building Machines That Learn and Think Like People
Recent progress in artificial intelligence (AI) has renewed interest in
building systems that learn and think like people. Many advances have come from
using deep neural networks trained end-to-end in tasks such as object
recognition, video games, and board games, achieving performance that equals or
even beats humans in some respects. Despite their biological inspiration and
performance achievements, these systems differ from human intelligence in
crucial ways. We review progress in cognitive science suggesting that truly
human-like learning and thinking machines will have to reach beyond current
engineering trends in both what they learn, and how they learn it.
Specifically, we argue that these machines should (a) build causal models of
the world that support explanation and understanding, rather than merely
solving pattern recognition problems; (b) ground learning in intuitive theories
of physics and psychology, to support and enrich the knowledge that is learned;
and (c) harness compositionality and learning-to-learn to rapidly acquire and
generalize knowledge to new tasks and situations. We suggest concrete
challenges and promising routes towards these goals that can combine the
strengths of recent neural network advances with more structured cognitive
models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary
proposals (until Nov. 22, 2016).
https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
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