Qualitative modelling is a technique integrating the fields of theoretical computer science, artificial intelligence and the physical and biological sciences. The aim is to be able to model the behaviour of systems without estimating parameter values and fixing the exact quantitative dynamics. Traditional applications are the study of the dynamics of physical and biological systems at a higher level of abstraction than that obtained by estimation of numerical parameter values for a fixed quantitative model. Qualitative modelling has been studied and implemented to varying degrees of sophistication in Petri nets, process calculi and constraint programming. In this paper we reflect on the strengths and weaknesses of existing frameworks, we demonstrate how recent advances in constraint programming can be leveraged to produce high quality qualitative models, and we describe the advances in theory and technology that would be needed to make constraint programming the best option for scientific investigation in the broadest sense.

Cultural algorithm is a kind of evolutionary algorithm inspired from societal evolution and is composed of a belief space, a population space and a protocol that enables exchange of knowledge between these sources. Knowledge created in the population space is accepted into the belief space while this collective knowledge from these sources is combined to influence the decisions of the individual agents in solving problems. Classification rules comes under descriptive knowledge discovery in data mining and are the most sought out by users since they represent highly comprehensible form of knowledge. The rules have certain properties which make them useful forms of actionable knowledge to users. The rules are evaluated using these properties namely the rule metrics. In the current study a Cultural Algorithm Toolkit for Classification Rule Mining (CAT-CRM) is proposed which allows the user to control three different set of parameters namely the evolutionary parameters, the rule parameters as well as agent parameters and hence can be used for experimenting with an evolutionary system, a rule mining system or an agent based social system. Results of experiments conducted to observe the effect of different number and type of metrics on the performance of the algorithm on bench mark data sets is reported.

Back-propagation algorithm is one of the most widely used and popular techniques to optimize the feed forward neural network training. Nature inspired meta-heuristic algorithms also provide derivative-free solution to optimize complex problem. Artificial bee colony algorithm is a nature inspired meta-heuristic algorithm, mimicking the foraging or food source searching behaviour of bees in a bee colony and this algorithm is implemented in several applications for an improved optimized outcome. The proposed method in this paper includes an improved artificial bee colony algorithm based back-propagation neural network training method for fast and improved convergence rate of the hybrid neural network learning method. The result is analysed with the genetic algorithm based back-propagation method, and it is another hybridized procedure of its kind. Analysis is performed over standard data sets, reflecting the light of efficiency of proposed method in terms of convergence speed and rate.

We construct an extension of diffusion geometry to multiple modalities through joint approximate diagonalization of Laplacian matrices. This naturally extends classical data analysis tools based on spectral geometry, such as diffusion maps and spectral clustering. We provide several synthetic and real examples of manifold learning, retrieval, and clustering demonstrating that the joint diffusion geometry frequently better captures the inherent structure of multi-modal data. We also show that many previous attempts to construct multimodal spectral clustering can be seen as particular cases of joint approximate diagonalization of the Laplacians.

The aim of this paper is to develop a methodology that is useful for analysing from a microeconomic perspective the incentives to entry, permanence and exit in the market for pharmaceutical generics under fuzzy conditions. In an empirical application of our proposed methodology, the potential towards permanence of labs with different characteristics has been estimated. The case we deal with is set in an open market where global players diversify into different national markets of pharmaceutical generics. Risk issues are significantly important in deterring decision makers from expanding in the generic pharmaceutical business. However, not all players are affected in the same way and/or to the same extent. Small, non-diversified generics labs are in the worse position. We have highlighted that the expected NPV and the number of generics in the portfolio of a pharmaceutical lab are important variables, but that it is also important to consider the degree of diversification. Labs with a higher potential for diversification across markets have an advantage over smaller labs. We have described a fuzzy decision support system based on the Mamdani model in order to determine the incentives for a laboratory to remain in the market both when it is stable and when it is growing.

In this article, I consider the effects of networking on the emergence of intelligence in individuals and societies. The following hypothesis promotes and sustains this investigation: The General Collective Problem Solving Capacity Hypothesis. Society possesses a general, collective problem solving capacity. The General Collective Problem Solving Capacity Hypothesis implies that the same general problem solving capacity that society uses, for example, to develop language, is used to solve problems in mathematics, science, business, musical composition and performance, sports contests, social interactions, politics and daily life. "All life is problem solving" [47]; all problem solving is a strictly analogous process. Let's adopt some notational conventions that will allow us to make the observations in the discussion that follows more precise. The formulas used in the definitions are sometimes modified by a subscript relevant to the context in which they are used.

Latent feature models are widely used to decompose data into a small number of components. Bayesian nonparametric variants of these models, which use the Indian buffet process (IBP) as a prior over latent features, allow the number of features to be determined from the data. We present a generalization of the IBP, the distance dependent Indian buffet process (dd-IBP), for modeling non-exchangeable data. It relies on distances defined between data points, biasing nearby data to share more features. The choice of distance measure allows for many kinds of dependencies, including temporal and spatial. Further, the original IBP is a special case of the dd-IBP. In this paper, we develop the dd-IBP and theoretically characterize its feature-sharing properties. We derive a Markov chain Monte Carlo sampler for a linear Gaussian model with a dd-IBP prior and study its performance on several non-exchangeable data sets.

We offer a novel view of AdaBoost in a statistical setting. We propose a Bayesian model for binary classification in which label noise is modeled hierarchically. Using variational inference to optimize a dynamic evidence lower bound, we derive a new boosting-like algorithm called VIBoost. We show its close connections to AdaBoost and give experimental results from four datasets.

In this article, we address the question of how non-knowledge about future events that influence economic agents' decisions in choice settings has been formally represented in economic theory up to date. To position our discussion within the ongoing debate on uncertainty, we provide a brief review of historical developments in economic theory and decision theory on the description of economic agents' choice behaviour under conditions of uncertainty, understood as either (i) ambiguity, or (ii) unawareness. Accordingly, we identify and discuss two approaches to the formalisation of non-knowledge: one based on decision-making in the context of a state space representing the exogenous world, as in Savage's axiomatisation and some successor concepts (ambiguity as situations with unknown probabilities), and one based on decision-making over a set of menus of potential future opportunities, providing the possibility of derivation of agents' subjective state spaces (unawareness as situation with imperfect subjective knowledge of all future events possible). We also discuss impeding challenges of the formalisation of non-knowledge.

Functional neuroimaging data are currently routinely used to better understand cognitive processes. They rely heavily on previous findings to formulate hypotheses and narrow the search space to regions of interest (ROIs), most often reported as coordinates of activation peaks [1], or from coordinate databases such as BrainMap [2]. However, understanding the literature is increasingly difficult, so that there is a need for more systematic methods, which use the images themselves to characterize the functional specificity of brain regions [3]. Transfer learning is a method that trains a classifier to learn a discriminant model on a source task, and then generalizes on a target task without further training. It can yield insights on some brain mechanisms if the tasks share specific common effects in some brain regions [4]. The goal of this work is to investigate the power of transfer learning procedures applied to pairs of cognitive contrasts, where the discrimination ability of the classifier quantifies the information shared between brain maps, and thus characterizes at which spatial scale functional contrasts can be jointly classified. We show that in many cases, transfer learning gives poor results in terms of spatial selectivity. To address this limitation, we introduce selection transfer, i.e. classification of brain states on the target task following the canonical procedure [5], but using regions defined on the source task.