WHY WOULD YOU STUDY ARTIFICIAL INTELLIGENCE? (1)
... humans commonly use, how they mentally organize this knowledge, and how they use it efficiently to solve a problem. • Researchers in artificial intelligence have used the results of these studies to develop techniques to best represent different knowledge types in the computer. • Intelligent systems ...
... humans commonly use, how they mentally organize this knowledge, and how they use it efficiently to solve a problem. • Researchers in artificial intelligence have used the results of these studies to develop techniques to best represent different knowledge types in the computer. • Intelligent systems ...
Non-rigid structure from motion using quadratic deformation models
... In their pioneering work to extend structure from motion to the case of non-rigid objects, Bregler et al.’s key insight [5] was to use a low-rank shape model to represent a deforming shape as a linear combination of k basis shapes which encode the main modes of deformation of the object. Based on th ...
... In their pioneering work to extend structure from motion to the case of non-rigid objects, Bregler et al.’s key insight [5] was to use a low-rank shape model to represent a deforming shape as a linear combination of k basis shapes which encode the main modes of deformation of the object. Based on th ...
NEURAL NETWORKS AND FUZZY SYSTEMS
... Two insights about the rate of convergence First,the individual energies decrease nontrivially.the BAM system does not creep arbitrary slowly down the toward the nearest local minimum.the system takes definite hops into the basin of attraction of the fixed point. Second,a synchronous BAM tends to c ...
... Two insights about the rate of convergence First,the individual energies decrease nontrivially.the BAM system does not creep arbitrary slowly down the toward the nearest local minimum.the system takes definite hops into the basin of attraction of the fixed point. Second,a synchronous BAM tends to c ...
Approximate Planning in POMDPs with Macro
... well defined framework for this interaction is the partially observable Markov decision process (POMDP) model. Unfortunately solving POMDPs is an intractable problem mainly due to the fact that exact solutions rely on computing a policy over the entire belief-space [6, 3], which is a simplex of dime ...
... well defined framework for this interaction is the partially observable Markov decision process (POMDP) model. Unfortunately solving POMDPs is an intractable problem mainly due to the fact that exact solutions rely on computing a policy over the entire belief-space [6, 3], which is a simplex of dime ...
Trace optimization and eigenproblems
... to dimension reduction. Given some input high-dimensional data, the goal of dimension reduction is to map them to a lowdimensional space such that certain properties of the initial data are preserved. Optimizing the above properties among the reduced data can be typically posed as a trace optimizati ...
... to dimension reduction. Given some input high-dimensional data, the goal of dimension reduction is to map them to a lowdimensional space such that certain properties of the initial data are preserved. Optimizing the above properties among the reduced data can be typically posed as a trace optimizati ...
Basic Probability
... getting you through most of the AI material on uncertain reasoning. In particular, the boxed results are the really important ones. Random variables (RVs) are by convention given capital letters. Say we have the RVs X1 , . . . , Xn . Their values are given using lower case. So for example X1 might b ...
... getting you through most of the AI material on uncertain reasoning. In particular, the boxed results are the really important ones. Random variables (RVs) are by convention given capital letters. Say we have the RVs X1 , . . . , Xn . Their values are given using lower case. So for example X1 might b ...
Learning Bayesian Networks: A Unification for Discrete and
... positive-definite precision matrix. The transformations between v = { v1 , ... , Vn} and = {bji I j < i} of a given Gaussian network G and the precision matrixW of the normal distribution rep resented by G are well known. In this paper, we need only the transformation fromW to { v,B}. We use the fo ...
... positive-definite precision matrix. The transformations between v = { v1 , ... , Vn} and = {bji I j < i} of a given Gaussian network G and the precision matrixW of the normal distribution rep resented by G are well known. In this paper, we need only the transformation fromW to { v,B}. We use the fo ...
The Automated Mapping of Plans for Plan Recognition* Artificial
... ify how plans are selected given the current goal (its purpose) and situation (its context). PRS KAs also specify a procedure, called the KA body, which it fol lows while attempting to accomplish its intended goal. This procedure is represented as a directed graph in which nodes represent states in ...
... ify how plans are selected given the current goal (its purpose) and situation (its context). PRS KAs also specify a procedure, called the KA body, which it fol lows while attempting to accomplish its intended goal. This procedure is represented as a directed graph in which nodes represent states in ...
Bayesian Network
... E.g. Picking 2 cards from a deck P(card1 is red and card2 is red) P(card1 is red and card2 is black) P(card1 is black and card2 is red) P(card1 is black and card2 is black) ...
... E.g. Picking 2 cards from a deck P(card1 is red and card2 is red) P(card1 is red and card2 is black) P(card1 is black and card2 is red) P(card1 is black and card2 is black) ...
I I I I I I I I I I I I I I I I I I I
... efficient for performing inference using multiply-connected networks that have small clusters of nodes. Unfortunately, inference using either belief networks or influence diagrams is NP-hard [Cooper87]. Therefore, for some complex, multiply-connected networks,. it may be necessary to use approximati ...
... efficient for performing inference using multiply-connected networks that have small clusters of nodes. Unfortunately, inference using either belief networks or influence diagrams is NP-hard [Cooper87]. Therefore, for some complex, multiply-connected networks,. it may be necessary to use approximati ...
P - Computing Science - Thompson Rivers University
... How to answer queries in Burglary net The full joint distribution is defined as the product of the local conditional distributions: P(X1, …, Xn) = πi =n 1 P(Xi | Parents(Xi)) [Q] P(j m a b e) = ??? = P(j | a) P(m | a) P(a | b, e) P(b) P(e) ...
... How to answer queries in Burglary net The full joint distribution is defined as the product of the local conditional distributions: P(X1, …, Xn) = πi =n 1 P(Xi | Parents(Xi)) [Q] P(j m a b e) = ??? = P(j | a) P(m | a) P(a | b, e) P(b) P(e) ...
Discriminative Improvements to Distributional Sentence Similarity
... term-context matrix means throwing away a considerable amount of information, as the original matrix of size M × N (number of instances by number of features) is factored into two smaller matrices of size M × K and N × K, with K M, N . If the factorization does not take into account labeled data a ...
... term-context matrix means throwing away a considerable amount of information, as the original matrix of size M × N (number of instances by number of features) is factored into two smaller matrices of size M × K and N × K, with K M, N . If the factorization does not take into account labeled data a ...
Differentiating features for the Weibull, Gamma, Log
... For each trial, a training set ( xi , p i ) was established, where p is an indicator function representing the density function which generated the sample vector x. An out of sample test set was generated for performance estimation. The data were generated in a similar way to the training data, but ...
... For each trial, a training set ( xi , p i ) was established, where p is an indicator function representing the density function which generated the sample vector x. An out of sample test set was generated for performance estimation. The data were generated in a similar way to the training data, but ...
Spikes, Local Field Potentials, and How to Model Both
... Important to understand the physical origins of what we record and model LFP and Spikes are fundamentally different types of data and require different modeling strategies LFP requires some thought about what to model, but techniques are standard Spikes effectively described by probability but are p ...
... Important to understand the physical origins of what we record and model LFP and Spikes are fundamentally different types of data and require different modeling strategies LFP requires some thought about what to model, but techniques are standard Spikes effectively described by probability but are p ...
Agents with no central representation
... connectives, neurons in a ANN, nodes in a Bayesian Network, nodes and ISA links in a Semantic Network ...
... connectives, neurons in a ANN, nodes in a Bayesian Network, nodes and ISA links in a Semantic Network ...
The Foundations of Cost-Sensitive Learning
... Theorem 1 does not say in what way the number of negative examples should be changed. If a learning algorithm can use weights on training examples, then the weight of each negative example can be set to the factor given by the theorem. Otherwise, we must do oversampling or undersampling. Oversamplin ...
... Theorem 1 does not say in what way the number of negative examples should be changed. If a learning algorithm can use weights on training examples, then the weight of each negative example can be set to the factor given by the theorem. Otherwise, we must do oversampling or undersampling. Oversamplin ...
Leaf Vein Extraction Using Independent Component Analysis
... contrast) in an image, or in practice, a small image patch is an observation, xi. We consider each image patch as a linear superposition of some features or basis vectors aij that are constant for an image. The S are stochastic coefficients, different from patch to patch. In a neuronscientific inter ...
... contrast) in an image, or in practice, a small image patch is an observation, xi. We consider each image patch as a linear superposition of some features or basis vectors aij that are constant for an image. The S are stochastic coefficients, different from patch to patch. In a neuronscientific inter ...
Translation: Probabilistic Conformant Planning to DBN
... – classical planning + initial belief state/ stochasitic actions • probabilistic planning without observation ...
... – classical planning + initial belief state/ stochasitic actions • probabilistic planning without observation ...
A sentential view of implicit and explicit belief
... inferential capabilities will play a role in their definition. For suppose that Ralph can believe ~b and ~ ~ ~ without inferring ~. Then there will be epistemic alternatives in which ~ and D ~ are true, but ~ is not. On the other hand, if Ralph always infers ~ from ~ and ~ D ~, then all epistemic al ...
... inferential capabilities will play a role in their definition. For suppose that Ralph can believe ~b and ~ ~ ~ without inferring ~. Then there will be epistemic alternatives in which ~ and D ~ are true, but ~ is not. On the other hand, if Ralph always infers ~ from ~ and ~ D ~, then all epistemic al ...
O A
... the interest of using ANNs instead of linear statistical models (Özesmi and Özesmi, 1999). The main application of ANNs is the development of predictive models to predict future values of a particular dependent variable from a given set of independent variables. The contribution of the input variabl ...
... the interest of using ANNs instead of linear statistical models (Özesmi and Özesmi, 1999). The main application of ANNs is the development of predictive models to predict future values of a particular dependent variable from a given set of independent variables. The contribution of the input variabl ...
nπ nπ - Department of Computer Science
... Definition 2.2 Given a set of variables C , a context on C is an assignment of one value to each variable in C . Usually C is left implicit, and we simply talk about a context. Two contexts are incompatible if there exists a variable that is assigned different values in the contexts; otherwise they ...
... Definition 2.2 Given a set of variables C , a context on C is an assignment of one value to each variable in C . Usually C is left implicit, and we simply talk about a context. Two contexts are incompatible if there exists a variable that is assigned different values in the contexts; otherwise they ...
Knowledge Representation Knowledge Representation
... thought experiment. – Suppose that a non -Chinese speaking person is put in a room and given three sets of Chinese characters (a script, a story, and questions about the story). ...
... thought experiment. – Suppose that a non -Chinese speaking person is put in a room and given three sets of Chinese characters (a script, a story, and questions about the story). ...
PARKA: A System for Massively Parallel Knowledge Representation
... PARKA is a frame-based knowledge representation system implemented on massively parallel hardware--the Connection Machine (CM-2). PARKA provides a representation language consisting of concept descriptions (frames) and binary relations on those descriptions (slots). The system is designed explicitly ...
... PARKA is a frame-based knowledge representation system implemented on massively parallel hardware--the Connection Machine (CM-2). PARKA provides a representation language consisting of concept descriptions (frames) and binary relations on those descriptions (slots). The system is designed explicitly ...
Minimizing Road Network Breakdown Probability
... In our algorithm, we decompose the global problem after the interior point method (IPM) [21]. In this way, we always guarantee a feasible solution during the update. Moreover, since we make use of the second order derivative during the update, the convergence rate of our algorithm is much faster tha ...
... In our algorithm, we decompose the global problem after the interior point method (IPM) [21]. In this way, we always guarantee a feasible solution during the update. Moreover, since we make use of the second order derivative during the update, the convergence rate of our algorithm is much faster tha ...
USI3
... • Brainstorming Phase: • Organizing Phase: create groups and subgroups of related items. • Layout Phase: • Linking Phase: lines with arrows ...
... • Brainstorming Phase: • Organizing Phase: create groups and subgroups of related items. • Layout Phase: • Linking Phase: lines with arrows ...