127,847 research outputs found
Representing Case Variations for Learning General and Specific Adaptation Rules
International audienceAdaptation is a task of case-based reasoning systems that is largely domain-dependant. This motivates the study of adaptation knowledge acquisition (AKA) that can be carried out thanks to learning processes on the variations between cases of the case base. This paper studies the representation of these variations and the impact of this representation on the AKA process, through experiments in an oncology domain
Case Base Mining for Adaptation Knowledge Acquisition
In case-based reasoning, the adaptation of a source case in order to solve
the target problem is at the same time crucial and difficult to implement. The
reason for this difficulty is that, in general, adaptation strongly depends on
domain-dependent knowledge. This fact motivates research on adaptation
knowledge acquisition (AKA). This paper presents an approach to AKA based on
the principles and techniques of knowledge discovery from databases and
data-mining. It is implemented in CABAMAKA, a system that explores the
variations within the case base to elicit adaptation knowledge. This system has
been successfully tested in an application of case-based reasoning to decision
support in the domain of breast cancer treatment
Adaptive Mechanisms in an Airline Ticket Demand Forecasting System
Adaptivity is a very important feature for industrial forecast systems. In the airline industry, a reliable forecasting
of a demand for tickets at different fare levels forms a crucial step in a global optimization process, the objective of which is
to sell a restricted number of available seats in a plane with a maximized revenue. Due to continuously changing demand
caused by seasonality, special events like holidays or fairs, changes in the flight schedules or changes of the political or cultural
situation of a country, there is a need for robust, adaptive forecasting techniques able to cope with such changes. In this paper
an overview of various adaptive mechanisms used in the new forecasting system of the Lufthansa Airline is presented
Self-Learning Cloud Controllers: Fuzzy Q-Learning for Knowledge Evolution
Cloud controllers aim at responding to application demands by automatically
scaling the compute resources at runtime to meet performance guarantees and
minimize resource costs. Existing cloud controllers often resort to scaling
strategies that are codified as a set of adaptation rules. However, for a cloud
provider, applications running on top of the cloud infrastructure are more or
less black-boxes, making it difficult at design time to define optimal or
pre-emptive adaptation rules. Thus, the burden of taking adaptation decisions
often is delegated to the cloud application. Yet, in most cases, application
developers in turn have limited knowledge of the cloud infrastructure. In this
paper, we propose learning adaptation rules during runtime. To this end, we
introduce FQL4KE, a self-learning fuzzy cloud controller. In particular, FQL4KE
learns and modifies fuzzy rules at runtime. The benefit is that for designing
cloud controllers, we do not have to rely solely on precise design-time
knowledge, which may be difficult to acquire. FQL4KE empowers users to specify
cloud controllers by simply adjusting weights representing priorities in system
goals instead of specifying complex adaptation rules. The applicability of
FQL4KE has been experimentally assessed as part of the cloud application
framework ElasticBench. The experimental results indicate that FQL4KE
outperforms our previously developed fuzzy controller without learning
mechanisms and the native Azure auto-scaling
Neural Mechanisms for Information Compression by Multiple Alignment, Unification and Search
This article describes how an abstract framework for perception and cognition may be realised in terms of neural mechanisms and neural processing.
This framework â called information compression by multiple alignment, unification and search (ICMAUS) â has been developed in previous research as a generalized model of any system for processing information, either natural or
artificial. It has a range of applications including the analysis and production of natural language, unsupervised inductive learning, recognition of objects and patterns, probabilistic reasoning, and others. The proposals in this article may be seen as an extension and development of
Hebbâs (1949) concept of a âcell assemblyâ.
The article describes how the concept of âpatternâ in the ICMAUS framework may be mapped onto a version of the cell
assembly concept and the way in which neural mechanisms may achieve the effect of âmultiple alignmentâ in the ICMAUS framework.
By contrast with the Hebbian concept of a cell assembly, it is proposed here that any one neuron can belong in one assembly and only one assembly. A key feature of present proposals, which is not part of the Hebbian concept, is that any cell assembly may contain âreferencesâ or âcodesâ that serve to identify one or more other cell assemblies. This mechanism allows information to be stored in a compressed form, it provides a robust mechanism by which assemblies may be connected to form hierarchies and other kinds of structure, it means that assemblies can express
abstract concepts, and it provides solutions to some of the other problems associated with cell assemblies.
Drawing on insights derived from the ICMAUS framework, the article also describes how learning may be achieved with neural mechanisms. This concept of learning is significantly different from the Hebbian concept and appears to provide a better account of what we know about human learning
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A Double Error Dynamic Asymptote Model of Associative Learning
In this paper a formal model of associative learning is presented which incorporates representational and computational mechanisms that, as a coherent corpus, empower it to make accurate predictions of a wide variety of phenomena that so far have eluded a unified account in learning theory. In particular, the Double Error Dynamic Asymptote (DDA) model introduces: 1) a fully-connected network architecture in which stimuli are represented as temporally clustered elements that associate to each other, so that elements of one cluster engender activity on other clusters, which naturally implements neutral stimuli associations and mediated learning; 2) a predictor error term within the traditional error correction rule (the double error), which reduces the rate of learning for expected predictors; 3) a revaluation associability rate that operates on the assumption that the outcome predictiveness is tracked over time so that prolonged uncertainty is learned, reducing the levels of attention to initially surprising outcomes; and critically 4) a biologically plausible variable asymptote, which encapsulates the principle of Hebbian learning, leading to stronger associations for similar levels of cluster activity. The outputs of a set of simulations of the DDA model are presented along with empirical results from the literature. Finally, the predictive scope of the model is discussed
Connecting adaptive behaviour and expectations in models of innovation: The Potential Role of Artificial Neural Networks
In this methodological work I explore the possibility of explicitly modelling expectations conditioning the R&D decisions of firms. In order to isolate this problem from the controversies of cognitive science, I propose a black box strategy through the concept of âinternal modelâ. The last part of the article uses artificial neural networks to model the expectations of firms in a model of industry dynamics based on Nelson & Winter (1982)
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