The Exploration of Greedy Hill-climbing Search in Markov
... consistent extension of P . If there is at least one consistent extension of a PDAG P , we say that P admits a consistent extension. If the PDAG P c resulting from applying the operators admits a consistent extension, it will be converted to a DAG by the algorithm PDAG-To-DAG. Otherwise, the algorit ...
... consistent extension of P . If there is at least one consistent extension of a PDAG P , we say that P admits a consistent extension. If the PDAG P c resulting from applying the operators admits a consistent extension, it will be converted to a DAG by the algorithm PDAG-To-DAG. Otherwise, the algorit ...
Approximating propositional knowledge with affine formulas
... KBs can be represented exactly by a formula with good properties ; in this case, the formula can give the answer to any query. To summarize, approximations can help saving a lot of time when answering queries (for instance in an on-line framework), especially if they can be reasoned with efficiently ...
... KBs can be represented exactly by a formula with good properties ; in this case, the formula can give the answer to any query. To summarize, approximations can help saving a lot of time when answering queries (for instance in an on-line framework), especially if they can be reasoned with efficiently ...
full paper - Frontiers in Artificial Intelligence and Applications (FAIA)
... KBs can be represented exactly by a formula with good properties ; in this case, the formula can give the answer to any query. To summarize, approximations can help saving a lot of time when answering queries (for instance in an on-line framework), especially if they can be reasoned with efficiently ...
... KBs can be represented exactly by a formula with good properties ; in this case, the formula can give the answer to any query. To summarize, approximations can help saving a lot of time when answering queries (for instance in an on-line framework), especially if they can be reasoned with efficiently ...
Finding the M Most Probable Configurations using Loopy Belief
... nodes and the clique size in a junction tree calculated using standard software [10] can be up to an order of 1042 , so that exact inference is obviously impossible. We showed that loopy max-product belief propagation (BP) achieved excellent results in finding the first MPC for this graph. In the f ...
... nodes and the clique size in a junction tree calculated using standard software [10] can be up to an order of 1042 , so that exact inference is obviously impossible. We showed that loopy max-product belief propagation (BP) achieved excellent results in finding the first MPC for this graph. In the f ...
An Extension of the ICP Algorithm Considering Scale Factor
... 2. PROBLEM STATEMENT AND THE ICP ALGORITHM 2.1. Problem Statement The registration of m-D point sets is a difficult problem. To solve this, a general statement is described first as follows. Given two point sets in \m , one denotes a model shape G G N M {mi }iNm1 and the other is a data shape P ...
... 2. PROBLEM STATEMENT AND THE ICP ALGORITHM 2.1. Problem Statement The registration of m-D point sets is a difficult problem. To solve this, a general statement is described first as follows. Given two point sets in \m , one denotes a model shape G G N M {mi }iNm1 and the other is a data shape P ...
Propositional Fragments for Knowledge
... restrictions are put on the prefix of the input: if no alternations of quantifiers occur, the problem reduces to the satisfiability problem or to the validity problem, and both of them are in P for OBDD< formulae; if the prefix is of the form ∀S1 ∃S2 , the problem is Πp1 -complete (= coNP-complete); ...
... restrictions are put on the prefix of the input: if no alternations of quantifiers occur, the problem reduces to the satisfiability problem or to the validity problem, and both of them are in P for OBDD< formulae; if the prefix is of the form ∀S1 ∃S2 , the problem is Πp1 -complete (= coNP-complete); ...
from Converse PDL - School of Computer Science
... without compromising the soundness and completeness of inference for it. Specifically we present an intuitive encoding of CPDL formulae into PDL that eliminates the converse programs from a CPDL formula, but adds enough information so as not to destroy its original meaning with respect to satisfiabi ...
... without compromising the soundness and completeness of inference for it. Specifically we present an intuitive encoding of CPDL formulae into PDL that eliminates the converse programs from a CPDL formula, but adds enough information so as not to destroy its original meaning with respect to satisfiabi ...
Using fuzzy temporal logic for monitoring behavior
... of thumbs and handle failures in an ad-hoc fashion. While this effectively helps in detecting some failures, it is often difficult to analyze and understand the range of typical failures covered by heuristic monitoring strategies. ...
... of thumbs and handle failures in an ad-hoc fashion. While this effectively helps in detecting some failures, it is often difficult to analyze and understand the range of typical failures covered by heuristic monitoring strategies. ...
A Decision Procedure for a Fragment of Linear Time Mu
... or ¬p. An occurrence of a variable X in a formula is called free if it does not lie in the scope of X; it is called bound otherwise. A formula is called closed if it contains no free variables. We write [ 0 /Y ] for the result of simultaneously substituting 0 for all free occurrences of the variable ...
... or ¬p. An occurrence of a variable X in a formula is called free if it does not lie in the scope of X; it is called bound otherwise. A formula is called closed if it contains no free variables. We write [ 0 /Y ] for the result of simultaneously substituting 0 for all free occurrences of the variable ...
Point set registration
In computer vision and pattern recognition, point set registration, also known as point matching, is the process of finding a spatial transformation that aligns two point sets. The purpose of finding such a transformation includes merging multiple data sets into a globally consistent model, and mapping a new measurement to a known data set to identify features or to estimate its pose. A point set may be raw data from 3D scanning or an array of rangefinders. For use in image processing and feature-based image registration, a point set may be a set of features obtained by feature extraction from an image, for example corner detection. Point set registration is used in optical character recognitionand aligning data from magnetic resonance imaging with computer aided tomography scans.