Does Vicarious Instigation Provide Support for Observational
... model and unconditioned response of the dition 1 make significantly more instigated model (suspending consideration of instruc- responses than those who experience Conditions for the moment) results in the four tion 2, the instigation relates to the unconexperimental conditions listed in Table 1. di ...
... model and unconditioned response of the dition 1 make significantly more instigated model (suspending consideration of instruc- responses than those who experience Conditions for the moment) results in the four tion 2, the instigation relates to the unconexperimental conditions listed in Table 1. di ...
Interaction of goal-directed and Pavlovian systems in aversive domains
... The circle was red for half of the trials and yellow for the other half. After two seconds, a ball of the same color of the circle appeared on one side of the screen and moved towards the opposite side, passing through the circle. The speed of the ball varied randomly trial by trial in all three blo ...
... The circle was red for half of the trials and yellow for the other half. After two seconds, a ball of the same color of the circle appeared on one side of the screen and moved towards the opposite side, passing through the circle. The speed of the ball varied randomly trial by trial in all three blo ...
Second-order conditioning of human causal learning
... never presented together would also bear out those associative models proposing a direct connection between causes (CS) and eVects (US), independently of any CR elicitation that could also be inXuenced by other factors, such as context, memory, and emotion. The second and more important objective wa ...
... never presented together would also bear out those associative models proposing a direct connection between causes (CS) and eVects (US), independently of any CR elicitation that could also be inXuenced by other factors, such as context, memory, and emotion. The second and more important objective wa ...
Augmenting Bottom-Up Metamodels with Predicates
... Regression analysis estimates the relationship between an output of the ABM and one or more input variables. The structure of the model is determined by minimizing the variance between the model and the data by using as few variables as possible. In this paper, we focus on creating bottom-up metamod ...
... Regression analysis estimates the relationship between an output of the ABM and one or more input variables. The structure of the model is determined by minimizing the variance between the model and the data by using as few variables as possible. In this paper, we focus on creating bottom-up metamod ...
Ensemble Learning Techniques for Structured
... Ensemble Learning Methods for Pay-Per-Click Campaign Management .................................... 48 1. Introduction ........................................................................................................................... 48 ...
... Ensemble Learning Methods for Pay-Per-Click Campaign Management .................................... 48 1. Introduction ........................................................................................................................... 48 ...
Goal Recognition Design - Association for the Advancement of
... tracking the activity of passengers in an airport may be performed in order to detect where passengers are heading. In addition, it is possible to set up barriers that control the flow of the passengers to improve the goal recognition task, but equally important to minimize the obstruction to the ea ...
... tracking the activity of passengers in an airport may be performed in order to detect where passengers are heading. In addition, it is possible to set up barriers that control the flow of the passengers to improve the goal recognition task, but equally important to minimize the obstruction to the ea ...
EIPB 698A Lecture 9
... Suppose a researcher finds that familial stress (e.g., in the context of a child’s chronic illness) is negatively associated with child psychological adjustment. Although this finding may be of interest to the researcher, it may be that the effect becomes more or less robust in the presence of other ...
... Suppose a researcher finds that familial stress (e.g., in the context of a child’s chronic illness) is negatively associated with child psychological adjustment. Although this finding may be of interest to the researcher, it may be that the effect becomes more or less robust in the presence of other ...
A Computational Intelligence Approach to Modelling Interstate Conflict
... failure of statistical methods might be attributed to the fact that the interstate variables related to MID are non-linear, highly interdependent and context dependent. This means conflict modelling requires more suitable techniques. Neural networks, particularly multilayer perceptrons (MLPs), have ...
... failure of statistical methods might be attributed to the fact that the interstate variables related to MID are non-linear, highly interdependent and context dependent. This means conflict modelling requires more suitable techniques. Neural networks, particularly multilayer perceptrons (MLPs), have ...
discrete variational autoencoders
... denoising and inpainting, and regularizing supervised tasks such as classification (Hinton et al., 2006; Salakhutdinov & Hinton, 2009; Rasmus et al., 2015). Many datasets of practical interest are projections of underlying distributions over real-world objects into an observation space; the pixels o ...
... denoising and inpainting, and regularizing supervised tasks such as classification (Hinton et al., 2006; Salakhutdinov & Hinton, 2009; Rasmus et al., 2015). Many datasets of practical interest are projections of underlying distributions over real-world objects into an observation space; the pixels o ...
INTRODUCTION TO Al AND PRODUCTION SYSTEMS 9
... Artificial Intelligence (AI) is a branch of Science which deals with helping machines finding solutions to complex problems in a more human-like fashion. This generally involves borrowing characteristics from human intelligence, and applying them as algorithms in a computer friendly way. A more or l ...
... Artificial Intelligence (AI) is a branch of Science which deals with helping machines finding solutions to complex problems in a more human-like fashion. This generally involves borrowing characteristics from human intelligence, and applying them as algorithms in a computer friendly way. A more or l ...
PDF - at www.arxiv.org.
... target community is challenging. These evaluations can be completed with lay humans, allowing for both a bigger subject pool and less expenses, since we do not have to compensate highly trained domain experts. Human-grounded evaluation is most appropriate when one wishes to test more general notion ...
... target community is challenging. These evaluations can be completed with lay humans, allowing for both a bigger subject pool and less expenses, since we do not have to compensate highly trained domain experts. Human-grounded evaluation is most appropriate when one wishes to test more general notion ...
Statistical Causal Inference
... but which we cannot now or ever hope to control. For example, the moon’s position exerts a direct effect on the gravitational field over the oceans, which causes the tides. But though the moon is a source that we cannot control, at least we can measure it. In other systems, such ideal sources of var ...
... but which we cannot now or ever hope to control. For example, the moon’s position exerts a direct effect on the gravitational field over the oceans, which causes the tides. But though the moon is a source that we cannot control, at least we can measure it. In other systems, such ideal sources of var ...
A tutorial on using the rminer R package for data mining tasks*
... The rminer package (http://cran.r-project.org/web/packages/rminer/ index.html) goal is to provide a reduced and coherent set of R functions to perform classification and regression. The package is particularly suited for non R expert users, as it allows to perform the full data mining process using ...
... The rminer package (http://cran.r-project.org/web/packages/rminer/ index.html) goal is to provide a reduced and coherent set of R functions to perform classification and regression. The package is particularly suited for non R expert users, as it allows to perform the full data mining process using ...
Probabilistic Latent Variable Model for Sparse
... equations are similar to NMF update equations as we shall point out in Section V. III. S PARSITY IN THE L ATENT VARIABLE M ODEL Sparse coding refers to a representational scheme where, of a set of components that may be combined to compose data, only a small number are combined to represent any part ...
... equations are similar to NMF update equations as we shall point out in Section V. III. S PARSITY IN THE L ATENT VARIABLE M ODEL Sparse coding refers to a representational scheme where, of a set of components that may be combined to compose data, only a small number are combined to represent any part ...
A theoretical study of Y structures for causal discovery
... number, and in the linear case, there are no theoretical or computational problems in scoring mixed ancestral graphs. However, there is no currently known way to score discrete mixed ancestral graphs, and there is no known algorithm for efficiently searching the space of mixed ancestral graphs. Fina ...
... number, and in the linear case, there are no theoretical or computational problems in scoring mixed ancestral graphs. However, there is no currently known way to score discrete mixed ancestral graphs, and there is no known algorithm for efficiently searching the space of mixed ancestral graphs. Fina ...
One-class to multi-class model update using the class
... AI Researcher Symposium (STAIRS). The papers from PAIS are included in this volume, while the papers from STAIRS are published in a separate volume. ECAI 2016 also featured a special topic on Artificial Intelligence for Human Values, with a dedicated track and a public event in the Peace Palace in T ...
... AI Researcher Symposium (STAIRS). The papers from PAIS are included in this volume, while the papers from STAIRS are published in a separate volume. ECAI 2016 also featured a special topic on Artificial Intelligence for Human Values, with a dedicated track and a public event in the Peace Palace in T ...
Artificial Intelligence for Artificial Artificial Intelligence
... that controls the workflow, i.e., makes these decisions automatically. The world state includes the quality of the current artifact, q ∈ [0, 1], and q0 of the improved artifact; true q and q0 are hidden and the controller can only track a belief about them. Intuitively, the extreme value of 0 (or 1) ...
... that controls the workflow, i.e., makes these decisions automatically. The world state includes the quality of the current artifact, q ∈ [0, 1], and q0 of the improved artifact; true q and q0 are hidden and the controller can only track a belief about them. Intuitively, the extreme value of 0 (or 1) ...
Artificial Intelligence for Artificial Artificial Intelligence
... that controls the workflow, i.e., makes these decisions automatically. The world state includes the quality of the current artifact, q ∈ [0, 1], and q0 of the improved artifact; true q and q0 are hidden and the controller can only track a belief about them. Intuitively, the extreme value of 0 (or 1) ...
... that controls the workflow, i.e., makes these decisions automatically. The world state includes the quality of the current artifact, q ∈ [0, 1], and q0 of the improved artifact; true q and q0 are hidden and the controller can only track a belief about them. Intuitively, the extreme value of 0 (or 1) ...
An Abstract View on Modularity in Knowledge Representation
... and essential difference is in the way we model the information flow. We do away with bridge rules and instead rely on a simple idea of information sharing via variables (or atoms) that are common in modules. We show that our abstract framework is well suited for representing concrete modular knowle ...
... and essential difference is in the way we model the information flow. We do away with bridge rules and instead rely on a simple idea of information sharing via variables (or atoms) that are common in modules. We show that our abstract framework is well suited for representing concrete modular knowle ...
Classical conditioning via inference over observable
... of the form p(US|CS,US, S) , where S are non-CS environmental factors that can help predict US. Simulation studies demonstrate that such Bayesian latent variable comparator models explain substantially more conditioning effects than earlier less formal comparator accounts. For instance, Courville et ...
... of the form p(US|CS,US, S) , where S are non-CS environmental factors that can help predict US. Simulation studies demonstrate that such Bayesian latent variable comparator models explain substantially more conditioning effects than earlier less formal comparator accounts. For instance, Courville et ...
A New Ensemble Model based Support Vector Machine for
... Normally, confusion matrix is used to estimate the accuracy. A confusion matrix is a matrix that shows the relationships between true class and predicted class. The below figure shows a confusion matrix. Additionally, 5-fold cross validation is presented in order to reduce the variance of the result ...
... Normally, confusion matrix is used to estimate the accuracy. A confusion matrix is a matrix that shows the relationships between true class and predicted class. The below figure shows a confusion matrix. Additionally, 5-fold cross validation is presented in order to reduce the variance of the result ...
Decision Support Systems - University of Pittsburgh
... judgment and decision making has been a major focus of science throughout history. Disciplines such as statistics, economics, and operations research developed various methods for making rational choices. More recently, these methods, often enhanced by a variety of techniques originating from inform ...
... judgment and decision making has been a major focus of science throughout history. Disciplines such as statistics, economics, and operations research developed various methods for making rational choices. More recently, these methods, often enhanced by a variety of techniques originating from inform ...
A Model Counting Characterization of Diagnoses
... of behavior of a component). Such a constraint in this example would be & ' \]"gj `_a_% ' \]/" . Diagnosis can become indiscriminate without fault models. It is also easy to see that the consistency-based approach can exploit fault models (when they are specified) to produce more intuitive diag ...
... of behavior of a component). Such a constraint in this example would be & ' \]"gj `_a_% ' \]/" . Diagnosis can become indiscriminate without fault models. It is also easy to see that the consistency-based approach can exploit fault models (when they are specified) to produce more intuitive diag ...
Structural equation modeling
Structural equation modeling (SEM) is a family of statistical methods designed to test a conceptual or theoretical model. Some common SEM methods include confirmatory factor analysis, path analysis, and latent growth modeling. The term ""structural equation model"" most commonly refers to a combination of two things: a ""measurement model"" that defines latent variables using one or more observed variables, and a ""structural regression model"" that links latent variables together. The parts of a structural equation model are linked to one another using a system of simultaneous regression equations.SEM is widely used in the social sciences because of its ability to isolate observational error from measurement of latent variables. To provide a simple example, the concept of human intelligence cannot be measured directly as one could measure height or weight. Instead, psychologists develop theories of intelligence and write measurement instruments with items (questions) designed to measure intelligence according to their theory. They would then use SEM to test their theory using data gathered from people who took their intelligence test. With SEM, ""intelligence"" would be the latent variable and the test items would be the observed variables.A simplistic model suggesting that intelligence (as measured by five questions) can predict academic performance (as measured by SAT, ACT, and high school GPA) is shown below. In SEM diagrams, latent variables are commonly shown as ovals and observed variables as rectangles. The below diagram shows how error (e) influences each intelligence question and the SAT, ACT, and GPA scores, but does not influence the latent variables. SEM provides numerical estimates for each of the parameters (arrows) in the model to indicate the strength of the relationships. Thus, in addition to testing the overall theory, SEM therefore allows the researcher to diagnose which observed variables are good indicators of the latent variables.Modern studies usually test much more specific models involving several theories, for example, Jansen, Scherer, and Schroeders (2015) studied how students' self-concept and self-efficacy affected educational outcomes. SEM is also used in the sciences, business, education, and many other fields.