D. Dubois, H. Prade, F. Touazi (coopéra7on avec A. Hadjali, S. Kaci)
|
|
- Audra Barber
- 5 years ago
- Views:
Transcription
1 D. Dubois, H. Prade, F. Touazi (coopéra7on avec A. Hadjali, S. Kaci)
2 Mo7va7on Two ways of expressing qualitative preferences Logical statements with priorities (possibilistic logic) Conditional preference networks (CP-nets) Compared advantages The logical approach looks less constrained but suffers from drowning effects CP-nets may be found more convenient to a user for expressing preference but the format is rigid.
3 Ques7ons Is the translation of CP-nets in possibilistic logic possible? Previous results suggested that yes. Here we question these results and suggest that no. We study the difficulties of translation
4 Background on CP-nets A CP-net N is a directed acyclic graph relating variables A CP-net preference is of the form u ij : x i > x i (or u ij : x i >x i ) where u ij is called a context j (instances of parent variables of x i ) The CP-table T i attached to x i is a collection of CP-net preferences for all contexts of x i
5 Background on CP-nets CP-net preferences are understoood ceteris paribus: u ij : x i > x i should be understood as u ij x i y k > u ij x i y k for all instantiations y k of the remaining variables Two solutions x and x are comparable if there is a sequence of one-variable flips transforming one to the other. Father nodes Pa(x i ) seem to be more important than children nodes x i
6 Example 1: Jackets pants and shirt
7 Encoding of CP-nets in possibilistic logic u: x > x becomes ( u x, α) where α is a symbolic necessity weight The CP table in each node i is encoded as a single pair (φ i, α i ) = (( u i x i ) (u i x i ) α i ) If x i is a father of x j, α i > α j is assumed (incomparable otherwise) One can associate a possibilis7c base to a CPnet with one formula per node.
8 Ordering solu7ons for a Π base To each base Σ and each interpreta7on w, define a vector v w (Σ) with as many components as formulas (φ i, α i ) ( = CP net nodes). for component i : 1 > α i si w = φ i, and 1 α i otherwise Ranking solu7ons = ranking vectors v w (Σ).
9 Order relations over vectors Leximin Discrimin Symmetric Pareto : Means refined by Min Pareto
10 Results Contrary to what is claimed in previous papers (Kaci, Hadjali) neither symmetric Pareto nor Leximin orderings can encode Cp net orderings symmetric Pareto is not discriminant enough; and leximin is too discriminant (counter examples) We have strong evidence that the CP net ordering can be bracketed by these two rela7ons symmetric Pareto ordering captures the CP net ordering if father nodes have only one child node. The alleged father node priority in CP nets does not seem to be transi7ve (counter examples).
11 Perspec7ves Complete formal proofs for these conjectures. An apparently unsolved issue: Is a solu7on viola7ng a subset of CP net preferences violated by another solu7on be[er (in the CP net sense) than the la[er. Extend results to more general graphical preference representa7ons. Connec7on with the study of the logic of par7ally ordered logical bases (with C. Cayrol, Touazi) Connec7ons to other logical preference languages. Applica7ons à l interroga7on de BD
12 Publica7ons A. HadjAli, S. Kaci, H. Prade: Database preference queries a possibilis7c logic approach with symbolic priori7es. Ann. Math. Ar5f. Intell. 63(3 4): (2011) D. Dubois, H. Prade, F. Touazi. Condi7onal Preference Nets, Possibilis7c Logic, and the Transi7vity of Priori7es. Dans : SGAI Interna5onal Conference on Innova5ve Techniques and Applica5ons of Ar5ficial Intelligence, Cambridge, UK, 10/12/ /12/2013, Max Bramer, Miltos Petridis (Eds.), Springer, p , D. Dubois, H. Prade, F. Touazi. A Possibilis7c Logic Approach to Condi7onal Preference Queries Dans : Flexible Query Answering (FQAS 2013), Granada, Spain, 18/09/ /09/2013, Springer, Lecture Notes in Computer Science 8132, p , D. Dubois, H. Prade, F. Touazi. Condi7onal Preference Nets and Possibilis7c Logic Dans : European Conference on Symbolic and Quan7ta7ve Approaches to Reasoning with Uncertainty (ECSQARU 2013), Utrecht, 08/07/ /07/2013, Linda Van der Gaag (Eds.), Springer, Lecture Notes in Computer Science 7958, p , 2013.
Conditional Preference Nets and Possibilistic Logic
Conditional Preference Nets and Possibilistic Logic Didier Dubois, Henri Prade, Fayçal Touazi To cite this version: Didier Dubois, Henri Prade, Fayçal Touazi. Conditional Preference Nets and Possibilistic
More informationApproximation of Conditional Preferences Networks CP-nets in Possibilistic Logic
Approximation of Conditional Preferences Networks CP-nets in Possibilistic Logic Didier Dubois, Souhila Kaci and Henri Prade Abstract This paper proposes a first comparative study of the expressive power
More informationQuery Propagation in Possibilistic Information Retrieval Networks
Query Propagation in Possibilistic Information Retrieval Networks Asma H. Brini Université Paul Sabatier brini@irit.fr Luis M. de Campos Universidad de Granada lci@decsai.ugr.es Didier Dubois Université
More informationEncoding formulas with partially constrained weights in a possibilistic-like many-sorted propositional logic
Encoding formulas with partially constrained weights in a possibilistic-like many-sorted propositional logic Salem Benferhat CRIL-CNRS, Université d Artois rue Jean Souvraz 62307 Lens Cedex France benferhat@criluniv-artoisfr
More informationA Psychological Study of Comparative Non-monotonic Preferences Semantics
A Psychological Study of Comparative Non-monotonic Preferences Semantics Rui da Silva Neves Université Toulouse-II, CLLE-LTC, CNRS UMR 5263 5 Allées Machado 31058 Toulouse Cedex 9, France neves@univ-tlse2fr
More informationRela%ons and Their Proper%es. Slides by A. Bloomfield
Rela%ons and Their Proper%es Slides by A. Bloomfield What is a rela%on Let A and B be sets. A binary rela%on R is a subset of A B Example Let A be the students in a the CS major A = {Alice, Bob, Claire,
More informationCardinal and Ordinal Preferences. Introduction to Logic in Computer Science: Autumn Dinner Plans. Preference Modelling
Cardinal and Ordinal Preferences Introduction to Logic in Computer Science: Autumn 2007 Ulle Endriss Institute for Logic, Language and Computation University of Amsterdam A preference structure represents
More informationHard and soft constraints for reasoning about qualitative conditional preferences
J Heuristics (2006) 12: 263 285 DOI 10.1007/s10732-006-7071-x Hard and soft constraints for reasoning about qualitative conditional preferences C. Domshlak S. Prestwich F. Rossi K.B. Venable T. Walsh C
More informationTDT70: Uncertainty in Artificial Intelligence. Chapter 1 and 2
TDT70: Uncertainty in Artificial Intelligence Chapter 1 and 2 Fundamentals of probability theory The sample space is the set of possible outcomes of an experiment. A subset of a sample space is called
More informationRanking Alternatives on the Basis of Generic Constraints and Examples A Possibilistic Approach
Ranking Alternatives on the Basis of Generic Constraints and Examples A Possibilistic Approach Romain Gérard IRIT 118 route de Narbonne 31062 Toulouse, France romain.gerard@hotmail.fr Souhila Kaci CRIL
More informationCompact preference representation for Boolean games
Compact preference representation for Boolean games Elise Bonzon, Marie-Christine Lagasquie-Schiex, and Jérôme Lang IRIT, UPS, F-31062 Toulouse Cédex 9, France {bonzon,lagasq,lang}@irit.fr Abstract. Boolean
More informationIntroduc)on to Ar)ficial Intelligence
Introduc)on to Ar)ficial Intelligence Lecture 9 Logical reasoning CS/CNS/EE 154 Andreas Krause First order logic (FOL)! Proposi)onal logic is about simple facts! There is a breeze at loca)on [1,2]! First
More informationMITOCW Lec 11 MIT 6.042J Mathematics for Computer Science, Fall 2010
MITOCW Lec 11 MIT 6.042J Mathematics for Computer Science, Fall 2010 The following content is provided under a Creative Commons license. Your support will help MIT OpenCourseWare continue to offer high
More informationFrom Distributions to Markov Networks. Sargur Srihari
From Distributions to Markov Networks Sargur srihari@cedar.buffalo.edu 1 Topics The task: How to encode independencies in given distribution P in a graph structure G Theorems concerning What type of Independencies?
More informationA Preference Logic With Four Kinds of Preferences
A Preference Logic With Four Kinds of Preferences Zhang Zhizheng and Xing Hancheng School of Computer Science and Engineering, Southeast University No.2 Sipailou, Nanjing, China {seu_zzz; xhc}@seu.edu.cn
More informationPreferences over Objects, Sets and Sequences
Preferences over Objects, Sets and Sequences 4 Sandra de Amo and Arnaud Giacometti Universidade Federal de Uberlândia Université de Tours Brazil France 1. Introduction Recently, a lot of interest arose
More informationarxiv:physics/ v1 [physics.soc-ph] 25 Jul 2006
Bayesian networks for enterprise risk assessment C. E. Bonafede, P. Giudici University of Pavia arxiv:physics/0607226v1 [physics.soc-ph] 25 Jul 2006 (Dated: October 19, 2018) Abstract According to different
More informationOn analysis of the unicity of Jeffrey s rule of conditioning in a possibilistic framework
Abstract Conditioning is an important task for designing intelligent systems in artificial intelligence. This paper addresses an issue related to the possibilistic counterparts of Jeffrey s rule of conditioning.
More informationThe computational complexity of dominance and consistency in CP-nets
The computational complexity of dominance and consistency in CP-nets Judy Goldsmith Dept. of Comp. Sci. University of Kentucky Lexington, KY 40506-0046, USA goldsmit@cs.uky.edu Abstract Jérôme Lang IRIT
More informationCS Lecture 3. More Bayesian Networks
CS 6347 Lecture 3 More Bayesian Networks Recap Last time: Complexity challenges Representing distributions Computing probabilities/doing inference Introduction to Bayesian networks Today: D-separation,
More informationAnnouncements. CS 188: Artificial Intelligence Spring Probability recap. Outline. Bayes Nets: Big Picture. Graphical Model Notation
CS 188: Artificial Intelligence Spring 2010 Lecture 15: Bayes Nets II Independence 3/9/2010 Pieter Abbeel UC Berkeley Many slides over the course adapted from Dan Klein, Stuart Russell, Andrew Moore Current
More informationOn Graphical Modeling of Preference and Importance
Journal of Artificial Intelligence Research 25 (2006) 389 424 Submitted 09/05; published 03/06 On Graphical Modeling of Preference and Importance Ronen I. Brafman Department of Computer Science Stanford
More informationIntroduc)on to Ar)ficial Intelligence
Introduc)on to Ar)ficial Intelligence Lecture 10 Probability CS/CNS/EE 154 Andreas Krause Announcements! Milestone due Nov 3. Please submit code to TAs! Grading: PacMan! Compiles?! Correct? (Will clear
More informationA set theoretic view of the ISA hierarchy
Loughborough University Institutional Repository A set theoretic view of the ISA hierarchy This item was submitted to Loughborough University's Institutional Repository by the/an author. Citation: CHEUNG,
More informationDirected Graphical Models
CS 2750: Machine Learning Directed Graphical Models Prof. Adriana Kovashka University of Pittsburgh March 28, 2017 Graphical Models If no assumption of independence is made, must estimate an exponential
More informationOn flexible database querying via extensions to fuzzy sets
On flexible database querying via extensions to fuzzy sets Guy de Tré, Rita de Caluwe Computer Science Laboratory Ghent University Sint-Pietersnieuwstraat 41, B-9000 Ghent, Belgium {guy.detre,rita.decaluwe}@ugent.be
More informationProbabilistic Reasoning: Graphical Models
Probabilistic Reasoning: Graphical Models Christian Borgelt Intelligent Data Analysis and Graphical Models Research Unit European Center for Soft Computing c/ Gonzalo Gutiérrez Quirós s/n, 33600 Mieres
More informationDependencies between players in Boolean games
Dependencies between players in Boolean games Elise Bonzon, Marie-Christine Lagasquie-Schiex, and Jérôme Lang IRIT, UPS, F-31062 Toulouse Cédex 9, France {bonzon,lagasq,lang}@irit.fr Copyright c 2007:
More informationA Possibilistic Extension of Description Logics
A Possibilistic Extension of Description Logics Guilin Qi 1, Jeff Z. Pan 2, and Qiu Ji 1 1 Institute AIFB, University of Karlsruhe, Germany {gqi,qiji}@aifb.uni-karlsruhe.de 2 Department of Computing Science,
More informationCSE 473: Ar+ficial Intelligence
CSE 473: Ar+ficial Intelligence Hidden Markov Models Luke Ze@lemoyer - University of Washington [These slides were created by Dan Klein and Pieter Abbeel for CS188 Intro to AI at UC Berkeley. All CS188
More informationA Notion of Distance Between CP-nets
A Notion of Distance Between CP-nets Andrea Loreggia University of Padova andrea.loreggia@gmail.com Nicholas Mattei IBM Research n.mattei@ibm.com Francesca Rossi IBM Research University of Padova francesca.rossi2@ibm.com
More informationA Semantic Approach for Iterated Revision in Possibilistic Logic
Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (2008) A Semantic Approach for Iterated Revision in Possibilistic Logic Guilin Qi AIFB Universität Karlsruhe D-76128 Karlsruhe,
More informationComplexity Results for Enhanced Qualitative Probabilistic Networks
Complexity Results for Enhanced Qualitative Probabilistic Networks Johan Kwisthout and Gerard Tel Department of Information and Computer Sciences University of Utrecht Utrecht, The Netherlands Abstract
More informationMerging Stratified Knowledge Bases under Constraints
Merging Stratified Knowledge Bases under Constraints Guilin Qi, Weiru Liu, David A. Bell School of Electronics, Electrical Engineering and Computer Science Queen s University Belfast Belfast, BT7 1NN,
More informationAdditive Consistency of Fuzzy Preference Relations: Characterization and Construction. Extended Abstract
Additive Consistency of Fuzzy Preference Relations: Characterization and Construction F. Herrera a, E. Herrera-Viedma a, F. Chiclana b Dept. of Computer Science and Artificial Intelligence a University
More informationEliciting, Modeling, and Reasoning about Preferences using CP-nets
Eliciting, Modeling, and Reasoning about Preferences using CP-nets Ronen I. Brafman Ben-Gurion University Decisions Decisions L i k l i h o o d o o d L i k l i h P r e f e r e n c e s Preferences Are Important
More informationComputational Aspects of Abstract Argumentation
Computational Aspects of Abstract Argumentation PhD Defense, TU Wien (Vienna) Wolfgang Dvo ák supervised by Stefan Woltran Institute of Information Systems, Database and Articial Intelligence Group Vienna
More informationGroup Decision-Making with Incomplete Fuzzy Linguistic Preference Relations
Group Decision-Making with Incomplete Fuzzy Linguistic Preference Relations S. Alonso Department of Software Engineering University of Granada, 18071, Granada, Spain; salonso@decsai.ugr.es, F.J. Cabrerizo
More informationMachine Learning Lecture 14
Many slides adapted from B. Schiele, S. Roth, Z. Gharahmani Machine Learning Lecture 14 Undirected Graphical Models & Inference 23.06.2015 Bastian Leibe RWTH Aachen http://www.vision.rwth-aachen.de/ leibe@vision.rwth-aachen.de
More informationProbabilistic Reasoning. (Mostly using Bayesian Networks)
Probabilistic Reasoning (Mostly using Bayesian Networks) Introduction: Why probabilistic reasoning? The world is not deterministic. (Usually because information is limited.) Ways of coping with uncertainty
More informationRepresenting Independence Models with Elementary Triplets
Representing Independence Models with Elementary Triplets Jose M. Peña ADIT, IDA, Linköping University, Sweden jose.m.pena@liu.se Abstract An elementary triplet in an independence model represents a conditional
More informationIntelligent Systems: Reasoning and Recognition. Reasoning with Bayesian Networks
Intelligent Systems: Reasoning and Recognition James L. Crowley ENSIMAG 2 / MoSIG M1 Second Semester 2016/2017 Lesson 13 24 march 2017 Reasoning with Bayesian Networks Naïve Bayesian Systems...2 Example
More informationPredicate Logic 1. The Need for Predicate Logic. The Need for Predicate Logic. The Need for Predicate Logic. The Need for Predicate Logic
Predicate Logic 1 Background to Logic Paradigm Joseph Spring School of Computer Science This Lecture We consider the following topics: The Closed World Assumption Predicates in Extension The Universal
More informationModal Logics. Most applications of modal logic require a refined version of basic modal logic.
Modal Logics Most applications of modal logic require a refined version of basic modal logic. Definition. A set L of formulas of basic modal logic is called a (normal) modal logic if the following closure
More informationReasoning with DL-Based CP-Nets
Reasoning with DL-Based CP-Nets Tommaso Di Noia 1, Thomas Lukasiewicz 2, and Gerardo I. Simari 2 1 Dipartimento di Ingegneria Elettrica e dell Informazione, Politecnico di Bari, Italy t.dinoia@poliba.it
More informationGraphical models and causality: Directed acyclic graphs (DAGs) and conditional (in)dependence
Graphical models and causality: Directed acyclic graphs (DAGs) and conditional (in)dependence General overview Introduction Directed acyclic graphs (DAGs) and conditional independence DAGs and causal effects
More informationPositive and negative preferences
Positive and negative preferences Stefano Bistarelli 1,2, Maria Silvia Pini 3, Francesca Rossi 3, and K. Brent Venable 3 1 University G D Annunzio, Pescara, Italy bista@sci.unich.it 2 Istituto di Informatica
More informationFair allocation of indivisible goods
Fair allocation of indivisible goods Gabriel Sebastian von Conta Technische Universitt Mnchen mrgabral@gmail.com November 17, 2015 Gabriel Sebastian von Conta (TUM) Chapter 12 November 17, 2015 1 / 44
More informationCS839: Probabilistic Graphical Models. Lecture 2: Directed Graphical Models. Theo Rekatsinas
CS839: Probabilistic Graphical Models Lecture 2: Directed Graphical Models Theo Rekatsinas 1 Questions Questions? Waiting list Questions on other logistics 2 Section 1 1. Intro to Bayes Nets 3 Section
More informationCSE 473: Ar+ficial Intelligence. Hidden Markov Models. Bayes Nets. Two random variable at each +me step Hidden state, X i Observa+on, E i
CSE 473: Ar+ficial Intelligence Bayes Nets Daniel Weld [Most slides were created by Dan Klein and Pieter Abbeel for CS188 Intro to AI at UC Berkeley. All CS188 materials are available at hnp://ai.berkeley.edu.]
More information3 : Representation of Undirected GM
10-708: Probabilistic Graphical Models 10-708, Spring 2016 3 : Representation of Undirected GM Lecturer: Eric P. Xing Scribes: Longqi Cai, Man-Chia Chang 1 MRF vs BN There are two types of graphical models:
More informationFrom Crisp to Fuzzy Constraint Networks
From Crisp to Fuzzy Constraint Networks Massimiliano Giacomin Università di Brescia Dipartimento di Elettronica per l Automazione Via Branze 38, I-25123 Brescia, Italy giacomin@ing.unibs.it Abstract. Several
More informationLecture 7: Relations
Lecture 7: Relations 1 Relation Relation between two objects signify some connection between them. For example, relation of one person being biological parent of another. If we take any two people at random,
More informationMachine Learning for Data Science (CS4786) Lecture 19
Machine Learning for Data Science (CS4786) Lecture 19 Hidden Markov Models Course Webpage : http://www.cs.cornell.edu/courses/cs4786/2017fa/ Quiz Quiz Two variables can be marginally independent but not
More informationSolution of Fuzzy Maximal Flow Network Problem Based on Generalized Trapezoidal Fuzzy Numbers with Rank and Mode
International Journal of Engineering Research and Development e-issn: 2278-067X, p-issn: 2278-800X, www.ijerd.com Volume 9, Issue 7 (January 2014), PP. 40-49 Solution of Fuzzy Maximal Flow Network Problem
More informationBayes Nets. CS 188: Artificial Intelligence Fall Example: Alarm Network. Bayes Net Semantics. Building the (Entire) Joint. Size of a Bayes Net
CS 188: Artificial Intelligence Fall 2010 Lecture 15: ayes Nets II Independence 10/14/2010 an Klein UC erkeley A ayes net is an efficient encoding of a probabilistic model of a domain ayes Nets Questions
More informationUpdates and Uncertainty in CP-Nets
Updates and Uncertainty in CP-Nets Cristina Cornelio 1,JudyGoldsmith 2,NicholasMattei 3,FrancescaRossi 1, and K. Brent Venable 4 1 University of Padova, Italy {cornelio,frossi}@math.unipd.it 2 University
More informationRepresenting Preferences Among Sets
Representing Preferences Among Sets Gerhard Brewka University of Leipzig Department of Computer Science Augustusplatz 10-11 D-04109 Leipzig, Germany brewka@informatik.uni-leipzig.de Mirosław Truszczyński
More informationBayesian Networks. Motivation
Bayesian Networks Computer Sciences 760 Spring 2014 http://pages.cs.wisc.edu/~dpage/cs760/ Motivation Assume we have five Boolean variables,,,, The joint probability is,,,, How many state configurations
More informationReading 11 : Relations and Functions
CS/Math 240: Introduction to Discrete Mathematics Fall 2015 Reading 11 : Relations and Functions Instructor: Beck Hasti and Gautam Prakriya In reading 3, we described a correspondence between predicates
More informationarxiv: v1 [cs.cc] 5 Dec 2018
Consistency for 0 1 Programming Danial Davarnia 1 and J. N. Hooker 2 1 Iowa state University davarnia@iastate.edu 2 Carnegie Mellon University jh38@andrew.cmu.edu arxiv:1812.02215v1 [cs.cc] 5 Dec 2018
More informationA Combined Approach for Outliers Detection in Fuzzy Functional Dependency through the Typology of Fuzzy Rules
A ombined Approach for Outliers Detection in Fuzzy Functional Dependency through the Typology of Fuzzy ules Shaheera ashwan Soheir Fouad and isham Sewelam Department of omputer Science Faculty of Engineering
More informationEfficient Sensitivity Analysis in Hidden Markov Models
Efficient Sensitivity Analysis in Hidden Markov Models Silja Renooij Department of Information and Computing Sciences, Utrecht University P.O. Box 80.089, 3508 TB Utrecht, The Netherlands silja@cs.uu.nl
More informationOhio Department of Education Academic Content Standards Mathematics Detailed Checklist ~Grade 9~
Ohio Department of Education Academic Content Standards Mathematics Detailed Checklist ~Grade 9~ Number, Number Sense and Operations Standard Students demonstrate number sense, including an understanding
More informationArtificial Intelligence Bayes Nets: Independence
Artificial Intelligence Bayes Nets: Independence Instructors: David Suter and Qince Li Course Delivered @ Harbin Institute of Technology [Many slides adapted from those created by Dan Klein and Pieter
More informationMolecular Similarity Searching Using Inference Network
Molecular Similarity Searching Using Inference Network Ammar Abdo, Naomie Salim* Faculty of Computer Science & Information Systems Universiti Teknologi Malaysia Molecular Similarity Searching Search for
More informationLearning preference relations over combinatorial domains
Learning preference relations over combinatorial domains Jérôme Lang and Jérôme Mengin Institut de Recherche en Informatique de Toulouse 31062 Toulouse Cedex, France Abstract. We address the problem of
More informationCharacterization of Semantics for Argument Systems
Characterization of Semantics for Argument Systems Philippe Besnard and Sylvie Doutre IRIT Université Paul Sabatier 118, route de Narbonne 31062 Toulouse Cedex 4 France besnard, doutre}@irit.fr Abstract
More informationLecture 10: Predicate Logic and Its Language
Discrete Mathematics (II) Spring 2017 Lecture 10: Predicate Logic and Its Language Lecturer: Yi Li 1 Predicates and Quantifiers In this action, we show you why a richer language should be introduced than
More informationLecture 1. ABC of Probability
Math 408 - Mathematical Statistics Lecture 1. ABC of Probability January 16, 2013 Konstantin Zuev (USC) Math 408, Lecture 1 January 16, 2013 1 / 9 Agenda Sample Spaces Realizations, Events Axioms of Probability
More informationAdapting the DF-QuAD Algorithm to Bipolar Argumentation
Adapting the DF-QuAD Algorithm to Bipolar Argumentation Antonio RAGO a,1, Kristijonas ČYRAS a and Francesca TONI a a Department of Computing, Imperial College London, UK Abstract. We define a quantitative
More informationConflict-Based Belief Revision Operators in Possibilistic Logic
Conflict-Based Belief Revision Operators in Possibilistic Logic Author Qi, Guilin, Wang, Kewen Published 2012 Conference Title Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence
More informationOn Conditional Independence in Evidence Theory
6th International Symposium on Imprecise Probability: Theories and Applications, Durham, United Kingdom, 2009 On Conditional Independence in Evidence Theory Jiřina Vejnarová Institute of Information Theory
More informationCompact preference representation and Boolean games
Compact preference representation and Boolean games Elise Bonzon Marie-Christine Lagasquie-Schiex Jérôme Lang Bruno Zanuttini Abstract Game theory is a widely used formal model for studying strategical
More informationSec$on Summary. Sequences. Recurrence Relations. Summations. Ex: Geometric Progression, Arithmetic Progression. Ex: Fibonacci Sequence
Section 2.4 Sec$on Summary Sequences Ex: Geometric Progression, Arithmetic Progression Recurrence Relations Ex: Fibonacci Sequence Summations 2 Introduc$on Sequences are ordered lists of elements. 1, 2,
More informationThe Necessity of Bounded Treewidth for Efficient Inference in Bayesian Networks
The Necessity of Bounded Treewidth for Efficient Inference in Bayesian Networks Johan H.P. Kwisthout and Hans L. Bodlaender and L.C. van der Gaag 1 Abstract. Algorithms for probabilistic inference in Bayesian
More informationComplexity Classes in Membrane Computing
Complexity Classes in Membrane Computing Fernando Sancho Caparrini Research Group on Natural Computing Dpt. Computer Science and Artificial Intelligence University of Seville, Spain Goal Main Object of
More informationCS 188: Artificial Intelligence Spring Announcements
CS 188: Artificial Intelligence Spring 2011 Lecture 16: Bayes Nets IV Inference 3/28/2011 Pieter Abbeel UC Berkeley Many slides over this course adapted from Dan Klein, Stuart Russell, Andrew Moore Announcements
More informationLexicographic Refinements in the Context of Possibilistic Decision Theory
Lexicographic Refinements in the Context of Possibilistic Decision Theory Lluis Godo IIIA - CSIC 08193 Bellaterra, Spain godo@iiia.csic.es Adriana Zapico Universidad Nacional de Río Cuarto - CONICET 5800
More informationCSE 200 Lecture Notes Turing machine vs. RAM machine vs. circuits
CSE 200 Lecture Notes Turing machine vs. RAM machine vs. circuits Chris Calabro January 13, 2016 1 RAM model There are many possible, roughly equivalent RAM models. Below we will define one in the fashion
More informationBusiness Process Technology Master Seminar
Business Process Technology Master Seminar BPT Group Summer Semester 2008 Agenda 2 Official Information Seminar Timeline Tasks Outline Topics Sergey Smirnov 17 April 2008 Official Information 3 Title:
More informationCS 188: Artificial Intelligence Fall 2008
CS 188: Artificial Intelligence Fall 2008 Lecture 14: Bayes Nets 10/14/2008 Dan Klein UC Berkeley 1 1 Announcements Midterm 10/21! One page note sheet Review sessions Friday and Sunday (similar) OHs on
More informationDecisiveness in Loopy Propagation
Decisiveness in Loopy Propagation Janneke H. Bolt and Linda C. van der Gaag Department of Information and Computing Sciences, Utrecht University P.O. Box 80.089, 3508 TB Utrecht, The Netherlands Abstract.
More informationMODELING, LEARNING AND REASONING ABOUT PREFERENCE TREES OVER COMBINATORIAL DOMAINS
University of Kentucky UKnowledge Theses and Dissertations--Computer Science Computer Science 2016 MODELING, LEARNING AND REASONING ABOUT PREFERENCE TREES OVER COMBINATORIAL DOMAINS Xudong Liu University
More informationTractable Inference in Hybrid Bayesian Networks with Deterministic Conditionals using Re-approximations
Tractable Inference in Hybrid Bayesian Networks with Deterministic Conditionals using Re-approximations Rafael Rumí, Antonio Salmerón Department of Statistics and Applied Mathematics University of Almería,
More informationCan Vector Space Bases Model Context?
Can Vector Space Bases Model Context? Massimo Melucci University of Padua Department of Information Engineering Via Gradenigo, 6/a 35031 Padova Italy melo@dei.unipd.it Abstract Current Information Retrieval
More informationRepresenting and Querying Correlated Tuples in Probabilistic Databases
Representing and Querying Correlated Tuples in Probabilistic Databases Prithviraj Sen Amol Deshpande Department of Computer Science University of Maryland, College Park. International Conference on Data
More informationLecture 9: PGM Learning
13 Oct 2014 Intro. to Stats. Machine Learning COMP SCI 4401/7401 Table of Contents I Learning parameters in MRFs 1 Learning parameters in MRFs Inference and Learning Given parameters (of potentials) and
More informationPreferential Query Answering in the Semantic Web with Possibilistic Networks
Preferential Query Answering in the Semantic Web with Possibilistic Networks Stefan Borgwardt Faculty of Computer Science Technische Universität Dresden, Germany stefan.borgwardt@tu-dresden.de Bettina
More informationCS 2750: Machine Learning. Bayesian Networks. Prof. Adriana Kovashka University of Pittsburgh March 14, 2016
CS 2750: Machine Learning Bayesian Networks Prof. Adriana Kovashka University of Pittsburgh March 14, 2016 Plan for today and next week Today and next time: Bayesian networks (Bishop Sec. 8.1) Conditional
More informationMassachusetts Institute of Technology Department of Electrical Engineering and Computer Science Algorithms For Inference Fall 2014
Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science 6.438 Algorithms For Inference Fall 2014 Recitation 3 1 Gaussian Graphical Models: Schur s Complement Consider
More informationNonmonotonic Tools for Argumentation
Nonmonotonic Tools for Argumentation Gerhard Brewka Computer Science Institute University of Leipzig brewka@informatik.uni-leipzig.de joint work with Stefan Woltran G. Brewka (Leipzig) CILC 2010 1 / 38
More informationBiologically Inspired Compu4ng: Neural Computa4on. Lecture 5. Patricia A. Vargas
Biologically Inspired Compu4ng: Neural Computa4on Lecture 5 Patricia A. Vargas Lecture 5 I. Lecture 4 Revision II. Ar4ficial Neural Networks (Part IV) I. Recurrent Ar4ficial Networks I. GasNet models II.
More informationLearning Bayesian Networks (part 1) Goals for the lecture
Learning Bayesian Networks (part 1) Mark Craven and David Page Computer Scices 760 Spring 2018 www.biostat.wisc.edu/~craven/cs760/ Some ohe slides in these lectures have been adapted/borrowed from materials
More informationHandout Lecture 8: Non-monotonic logics
Handout Lecture 8: Non-monotonic logics Xavier Parent and Leon van der Torre University of Luxembourg April 27, 2016 Abstract This handout is devoted to non-monotonic logics a family of logics devised
More informationTCP-nets - Introducing Variable Importance Tradeoffs into CP-nets
TCP-nets - Introducing Variable Importance Tradeoffs into CP-nets Ronen I. Brafman Department of Computer Science Ben-Gurion University Beer Sheva, Israel 84105 Carmel Domshlak Department of Computer Science
More informationProduct rule. Chain rule
Probability Recap CS 188: Artificial Intelligence ayes Nets: Independence Conditional probability Product rule Chain rule, independent if and only if: and are conditionally independent given if and only
More informationDialectical Frameworks: Argumentation Beyond Dung
Dialectical Frameworks: Argumentation Beyond Dung Gerhard Brewka Computer Science Institute University of Leipzig brewka@informatik.uni-leipzig.de joint work with Stefan Woltran G. Brewka (Leipzig) NMR
More informationExact distribution theory for belief net responses
Exact distribution theory for belief net responses Peter M. Hooper Department of Mathematical and Statistical Sciences University of Alberta Edmonton, Canada, T6G 2G1 hooper@stat.ualberta.ca May 2, 2008
More informationComputing the acceptability semantics. London SW7 2BZ, UK, Nicosia P.O. Box 537, Cyprus,
Computing the acceptability semantics Francesca Toni 1 and Antonios C. Kakas 2 1 Department of Computing, Imperial College, 180 Queen's Gate, London SW7 2BZ, UK, ft@doc.ic.ac.uk 2 Department of Computer
More information