Emotions shape how we remember our world, how we perceive it, and which decisions we take. This study proposed a novel three-component computation mechanism that enables a robot to reason about emotions. The mechanism...
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ISBN:
(纸本)9781509054435
Emotions shape how we remember our world, how we perceive it, and which decisions we take. This study proposed a novel three-component computation mechanism that enables a robot to reason about emotions. The mechanism contains the following parts: (i) information acquisition, (ii) formal knowledge description in a form of ontology, and (iii) Bayesian Network (BN). An entropy reduction method from information theory is used to design an effective Human-Robot Interaction (HRI) and to gain a deeper understanding about proposed reasoning mechanism. The method also revealed the most influential BN variables that efficiently resolve the reasoning ambiguities. The modified OCC model of emotions in BN is implemented to ensure adaptation of the system to multiple sources of uncertainty. Variables in a hand crafted BN are linked together, where each link is quantified by spreading influences of a different strength that parent nodes have on child nodes. The paper introduces also the system architecture for realization of the physical robot setup.
One prominent method to perform inference on probabilistic graphical models is the probability propagation in trees of clusters (PPTC) algorithm. In this paper, we demonstrate the use of partial evaluation, an establi...
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ISBN:
(纸本)9781665474047
One prominent method to perform inference on probabilistic graphical models is the probability propagation in trees of clusters (PPTC) algorithm. In this paper, we demonstrate the use of partial evaluation, an established technique from the compiler domain, to improve the performance of online Bayesian inference using the PPTC algorithm in the context of observed evidence. We present a metaprogramming-based method to transform a base program into an optimized version by precomputing the static input at compile time while guaranteeing behavioral equivalence. We achieve an inference time reduction of 21% on average for the Promedas benchmark.
Exploiting label correlations is a challenging and crucial problem especially in multi-label learning context. Labels correlations are not necessarily shared by all instances and have generally a local definition. Thi...
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ISBN:
(纸本)9781479929719
Exploiting label correlations is a challenging and crucial problem especially in multi-label learning context. Labels correlations are not necessarily shared by all instances and have generally a local definition. This paper introduces LOC-LDA, which is a latent variable model that adresses the problem of modeling annotated data by locally exploiting correlations between annotations. In particular, we represent explicitly local dependencies to define the correspondence between specific objects, i.e. regions of images and their annotations. We conducted experiments on a collection of pictures provided by the Wikipedia "Picture of the day" website (1), and evaluated our model on the task of "automatic image annotation". The results validate the effectiveness of our approach.
graphicalmodels offer techniques for capturing the structure of many problems in real- world domains and provide means for representation, interpretation, and inference. The modeling framework provides tools for disc...
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graphicalmodels offer techniques for capturing the structure of many problems in real- world domains and provide means for representation, interpretation, and inference. The modeling framework provides tools for discovering rules for solving problems by exploring structural relationships. We present the Structural Affinity method that uses graphicalmodels for first learning and subsequently recognizing the pattern for solving problems on the Raven's Progressive Matrices Test of general human intelligence. Recently there has been considerable work on computational models of addressing the Raven's test using various representations ranging from fractals to symbolic structures. In contrast, our method uses Markov Random Fields parameterized by affinity factors to discover the structure in the geometric problems and induce the rules of Carpenter et al.'s cognitive model of problem-solving on the Raven's Progressive Matrices Test. We provide a computational account that first learns the structure of Raven's problem and then predicts the solution by computing the probability of the correct answer by recognizing patterns corresponding to Carpenter et al.'s rules. We demonstrate that the performance of our model on the Standard Raven Progressive Matrices is comparable with existing state of the art models. In this report, we raise and attempt to address research questions about the knowledge representation that provides a sufficient opportunity to capture a pattern in geometrical intelligence tests such as Raven Progressive Matrices. We show how a minimal representation facilitates pattern extraction process by proposing a method for organizing the representational units using the framework of probabilistic graphical models. By orchestrating techniques from mathematics, data science and computer science, we design an agent that can explain its responses while still predicting accurate solutions. And finally, we discuss the key takeaways about knowledge representation, str
While discrete latent variable models have had great success in self-supervised learning, most models assume that frames are independent. Due to the segmental nature of phonemes in speech perception, modeling dependen...
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Wind farm layout optimization (WFLO) determines the optimal location of wind turbines within a fixed geographical area to maximize the total power capacity of the wind farm, under stochastic wind conditions and non-li...
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Wind farm layout optimization (WFLO) determines the optimal location of wind turbines within a fixed geographical area to maximize the total power capacity of the wind farm, under stochastic wind conditions and non-linear aerodynamic interferences between turbines. This thesis develops optimization approaches to fast approximate (sub-optimal) turbine layouts to aide engineers make design decisions. Building on previous work in discrete quadratic WFLO models, we recast the program as a probabilisticgraphical model incorporating spatial dependencies (i.e., aerodynamic interferences, proximity constraints, and maximum number of turbines) between the variables. Turbine layouts are estimated using message passing inference (BP, TRW-S), which exploit the problema s graph-theoretic structure using decomposition and factorization. We perform an exhaustive computational study comparing TRW-S with branch-and-cut algorithms under varying wind-regime complexity and problem resolutions. We demonstrate the broad applicability of techniques we develop by solving a suite of benchmark quadratic knapsack problems, a general class of problems that arise in many settings.%%%%M.A.S
Gated Bayesian networks (GBNs) are a recently introduced extension of Bayesian networks that aims to model dynamical systems consisting of several distinct phases. In this paper, we present an algorithm for semi-autom...
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ISBN:
(纸本)9783319114330;9783319114323
Gated Bayesian networks (GBNs) are a recently introduced extension of Bayesian networks that aims to model dynamical systems consisting of several distinct phases. In this paper, we present an algorithm for semi-automatic learning of GBNs. We use the algorithm to learn GBNs that output buy and sell decisions for use in algorithmic trading systems. We show how using the learnt GBNs can substantially lower risks towards invested capital, while at the same time generating similar or better rewards, compared to the benchmark investment strategy buy-and-hold.
Preempting attacks targeting supercomputing systems before damage remains the top security priority. The main challenge is that noisy attack attempts and unreliable alerts often mask real attacks, causing permanent da...
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Within the realm of probabilistic graphical models, message-passing algorithms offer a powerful framework for efficient inference. When dealing with discrete variables, these algorithms essentially amount to the addit...
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Transductive semi-supervised learning methods aim at automatically labeling large datasets by leveraging information provided by few manually labeled data points and the intrinsic structure of the dataset. Many such m...
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Transductive semi-supervised learning methods aim at automatically labeling large datasets by leveraging information provided by few manually labeled data points and the intrinsic structure of the dataset. Many such methods based on a graph signal representation of a dataset have been proposed, in which the nodes correspond to the data points, the edges connect similar points, and the graph signal is the mapping between the nodes and the la- bels. Most of the existing methods use deterministic signal models and try to recover the graph signal using a regularized or constrained convex optimiza- tion approach, where the regularization/constraint term enforce some sort of smoothness of the graph signal. This thesis takes a different route and inves- tigates a probabilisticgraphical modeling approach in which the graph signal is considered a Markov random field defined over the underlying network structure. The measurement process, modeling the initial manually obtained labels, and smoothness assumptions are imposed by a probability distribution defined over the Markov network corresponding to the data graph. Various approximate inference methods such as loopy belief propagation and the mean field methods are studied by means of numerical experiments involving both synthetic and real-world datasets.
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