querying description logic knowledge bases

... defined syntax and semantics. A Description Logic allows for the specification of concepts (also known as classes), individuals (also known as objects) that are instances of these concepts, and roles (also known as properties) that are interpreted as pairs of individuals that are related by the role ...

... defined syntax and semantics. A Description Logic allows for the specification of concepts (also known as classes), individuals (also known as objects) that are instances of these concepts, and roles (also known as properties) that are interpreted as pairs of individuals that are related by the role ...

Generalising unit-refutation completeness and SLUR via nested

... clause-set F0 (clausal database), for which one seeks an equivalent F such that clausal entailment can be decided quickly. A second development is important here, namely the development of the notion of “hardness” in Kullmann [36, 37], Ansótegui et al. [1]. The first source [36] from 1999 introduce ...

... clause-set F0 (clausal database), for which one seeks an equivalent F such that clausal entailment can be decided quickly. A second development is important here, namely the development of the notion of “hardness” in Kullmann [36, 37], Ansótegui et al. [1]. The first source [36] from 1999 introduce ...

Algorithms for Maximum Satisfiability

... path from S to G that does not pass a blocked square. Such a path will falsify one negative soft clause for every square it passes through. ...

... path from S to G that does not pass a blocked square. Such a path will falsify one negative soft clause for every square it passes through. ...

Cutset sampling for Bayesian networks

... property that conditioning on a subset of variables simplifies the network’s structure allowing efficient query processing by exact algorithms. In general, exact inference by variable elimination (Dechter, 1999a, 2003) or join-tree algorithms (Lauritzen & Spiegelhalter, 1988; Jensen, Lauritzen, & Ol ...

... property that conditioning on a subset of variables simplifies the network’s structure allowing efficient query processing by exact algorithms. In general, exact inference by variable elimination (Dechter, 1999a, 2003) or join-tree algorithms (Lauritzen & Spiegelhalter, 1988; Jensen, Lauritzen, & Ol ...

P - Computing Science

... = P(Toothache | Catch, Cavity) P(Catch, Cavity) = P(Toothache | Catch, Cavity) P(Catch | Cavity) P(Cavity) = P(Toothache | Cavity) P(Catch | Cavity) P(Cavity) ...

... = P(Toothache | Catch, Cavity) P(Catch, Cavity) = P(Toothache | Catch, Cavity) P(Catch | Cavity) P(Cavity) = P(Toothache | Cavity) P(Catch | Cavity) P(Cavity) ...

On reasoning in networks with qualitative

... a number of extensions to the work described here. The motivation behind this work was to integrate dif ferent approaches to reasoning under uncertainty, in particular probability, possibility [Zadeh 1978] [Dubois and Prade 1988a], and evidence [Shafer 1976] [Smets 1988) theories. Thus, our qualita ...

... a number of extensions to the work described here. The motivation behind this work was to integrate dif ferent approaches to reasoning under uncertainty, in particular probability, possibility [Zadeh 1978] [Dubois and Prade 1988a], and evidence [Shafer 1976] [Smets 1988) theories. Thus, our qualita ...

Computational Approaches to Preference Elicitation

... using all-else-being-equal semantics. In a more recent article, McGeachie and Doyle [2004] provide algorithms for computing ordinal value functions based on qualitative ceteris paribus preference statements. One issue not addressed by ceteris paribus logic theories was compact and efficient represen ...

... using all-else-being-equal semantics. In a more recent article, McGeachie and Doyle [2004] provide algorithms for computing ordinal value functions based on qualitative ceteris paribus preference statements. One issue not addressed by ceteris paribus logic theories was compact and efficient represen ...

questions and answers: reasoning and querying in description logic

... Incomplete information can appear in different forms. Firstly, the implied existence of individuals without explicitly mentioning them: the knowledge base in Example 1.1 implies the existence of at least one flyer which is friend of tweety. Secondly, in the form of disjunctive information, for examp ...

... Incomplete information can appear in different forms. Firstly, the implied existence of individuals without explicitly mentioning them: the knowledge base in Example 1.1 implies the existence of at least one flyer which is friend of tweety. Secondly, in the form of disjunctive information, for examp ...

On Revising Fuzzy Belief Bases

... the formulas to create fuzzy belief bases, and (iii) a fuzzy deduction operator D which takes as input a fuzzy base u and returns another fuzzy base D(u) representing its (fuzzy) conclusions. Sometimes a fourth ingredient is included – a fuzzy semantics M – in which case we speak of an abstract fuzz ...

... the formulas to create fuzzy belief bases, and (iii) a fuzzy deduction operator D which takes as input a fuzzy base u and returns another fuzzy base D(u) representing its (fuzzy) conclusions. Sometimes a fourth ingredient is included – a fuzzy semantics M – in which case we speak of an abstract fuzz ...

Brief Survey on Computational Solutions for Bayesian Inference

... From 2006 to 2011, the research group lead by Professor Viktor Prasanna at the University of Southern California produced a vast body of work contributing with solutions for the implementation of exact inference in multi/manycore CPUs and GPUs. Starting in 2006, Namasivayam et al. presented a study ...

... From 2006 to 2011, the research group lead by Professor Viktor Prasanna at the University of Southern California produced a vast body of work contributing with solutions for the implementation of exact inference in multi/manycore CPUs and GPUs. Starting in 2006, Namasivayam et al. presented a study ...

ABSTRACT HYBRID CAUSAL LOGIC Title:

... familiarization, identification of initiating events, scenario modeling, quantification, uncertainty analysis, sensitivity analysis, importance ranking, and data analysis. Fault trees and event trees are widely used tools for risk scenario analysis in PRAs of technological systems. This methodology ...

... familiarization, identification of initiating events, scenario modeling, quantification, uncertainty analysis, sensitivity analysis, importance ranking, and data analysis. Fault trees and event trees are widely used tools for risk scenario analysis in PRAs of technological systems. This methodology ...

Aalborg Universitet Learning Bayesian Networks with Mixed Variables Bøttcher, Susanne Gammelgaard

... Paper I addresses these issues for Bayesian networks with mixed variables. In this paper, the focus is on learning Bayesian networks, where the joint probability distribution is conditional Gaussian. For an introductory text on learning Bayesian networks, see Heckerman (1999). To learn the parameter ...

... Paper I addresses these issues for Bayesian networks with mixed variables. In this paper, the focus is on learning Bayesian networks, where the joint probability distribution is conditional Gaussian. For an introductory text on learning Bayesian networks, see Heckerman (1999). To learn the parameter ...

A philosophical basis for knowledge acquisition

... philosophical environment for developing knowledge acquisition and representation tools. We outline the features desirable in tools based on this philosophy and the progress we have made towards developing such tools. Introduction We wish to consider the philosophical question of the truth value of ...

... philosophical environment for developing knowledge acquisition and representation tools. We outline the features desirable in tools based on this philosophy and the progress we have made towards developing such tools. Introduction We wish to consider the philosophical question of the truth value of ...

Artificial Intelligence and Expert Systems

... computer exhibits intelligent behavior was designed by Alan Turing and is called the Turing test. A computer can be considered to be smart only when a human interviewer, “conversing” with both an unseen human being and an unseen computer, could not determine which is which The following definitions ...

... computer exhibits intelligent behavior was designed by Alan Turing and is called the Turing test. A computer can be considered to be smart only when a human interviewer, “conversing” with both an unseen human being and an unseen computer, could not determine which is which The following definitions ...

Extending Partial Representations of Interval Graphs

... Interse tion Representations. In this paper, we are interested in intersection representations which assign geometric objects to vertices of graphs and which encode edges by intersections of the objects. Formally, an intersection representation R of G is a collection of sets {Rv : v ∈ V (G)} such th ...

... Interse tion Representations. In this paper, we are interested in intersection representations which assign geometric objects to vertices of graphs and which encode edges by intersections of the objects. Formally, an intersection representation R of G is a collection of sets {Rv : v ∈ V (G)} such th ...

Chapter 6 - Expert Systems and knowledge

... When allowing the user to use natural language in obtaining input, a misunderstanding could result because of the ambiguous characteristic of natural language. The use of natural language in the dialogue with the user relies on the meaning of the user’s utterances. The meaning of what exactly is mea ...

... When allowing the user to use natural language in obtaining input, a misunderstanding could result because of the ambiguous characteristic of natural language. The use of natural language in the dialogue with the user relies on the meaning of the user’s utterances. The meaning of what exactly is mea ...

Progression and Regression - Foundations of Artificial Intelligence

... Definition (FDR Regression). Let (V, A, c, I, G) be an FDR planning task, g be a partial variable assignment, and a ∈ A. We say that g is regressable over a if (i) eff a ∩ g 6= ∅; (ii) there is no v ∈ V s.t. eff a (v) and g(v) are both defined and eff a (v) 6= g(v); and (iii) there is no v ∈ V s.t. ...

... Definition (FDR Regression). Let (V, A, c, I, G) be an FDR planning task, g be a partial variable assignment, and a ∈ A. We say that g is regressable over a if (i) eff a ∩ g 6= ∅; (ii) there is no v ∈ V s.t. eff a (v) and g(v) are both defined and eff a (v) 6= g(v); and (iii) there is no v ∈ V s.t. ...

KRR Lectures — Contents

... An inference rule characterises a pattern of valid deduction. In other words, it tells us that if we accept as true a number of propositions — called premisses — which match certain patterns, we can deduce that some further proposition is true — this is called the conclusion. Thus we saw that from t ...

... An inference rule characterises a pattern of valid deduction. In other words, it tells us that if we accept as true a number of propositions — called premisses — which match certain patterns, we can deduce that some further proposition is true — this is called the conclusion. Thus we saw that from t ...

Schematic Invariants by Reduction to Ground Invariants

... Definition 2 (Terms) Let V be a set of schema variables and O a set of objects. Terms (over O and V ) are objects o ∈ O and variables v ∈ V . Each variable has a type τvar (v) ∈ T . Definition 3 (Predicates) Let P be a set of predicate symbols. Each predicate p ∈ P has arity ar(p) ∈ N and an associa ...

... Definition 2 (Terms) Let V be a set of schema variables and O a set of objects. Terms (over O and V ) are objects o ∈ O and variables v ∈ V . Each variable has a type τvar (v) ∈ T . Definition 3 (Predicates) Let P be a set of predicate symbols. Each predicate p ∈ P has arity ar(p) ∈ N and an associa ...

An Introduction to Variational Methods for Graphical Models

... The junction tree algorithm compiles directed graphical models into undirected graphical models; subsequent inferential calculation is carried out in the undirected formalism. The step that converts the directed graph into an undirected graph is called “moralization.” (If the initial graph is alread ...

... The junction tree algorithm compiles directed graphical models into undirected graphical models; subsequent inferential calculation is carried out in the undirected formalism. The step that converts the directed graph into an undirected graph is called “moralization.” (If the initial graph is alread ...

02a Enabling Technology for Knowledge Sharing

... Impediment 2. Dialects within Language Families: Even within a single family of knowledge representation formalisms (for example, the KL-One family), it can be difficult to share knowledge across systems if the knowledge has been encoded in different dialects. Some of the differences between dialect ...

... Impediment 2. Dialects within Language Families: Even within a single family of knowledge representation formalisms (for example, the KL-One family), it can be difficult to share knowledge across systems if the knowledge has been encoded in different dialects. Some of the differences between dialect ...

Context-Dependent Incremental Intention Recognition through Bayesian Network Model Construction

... saying that variables are independent of non-descendants given their parents in the graph [16, 17]. In a BN, associated with each node of its DAG is a specification of the distribution of its variable, say A, conditioned on its parents in the graph (denoted by pa(A))—i.e., P (A|pa(A)) is specified. ...

... saying that variables are independent of non-descendants given their parents in the graph [16, 17]. In a BN, associated with each node of its DAG is a specification of the distribution of its variable, say A, conditioned on its parents in the graph (denoted by pa(A))—i.e., P (A|pa(A)) is specified. ...

Belief Base Change Operations for Answer Set Programming

... been proven to be useful, cf. (Delgrande 2008). For logic programing it also does not have the same undesirable implications as we shall see later on. Due to the non-monotonicity of inconsistency we can strengthen the Consistency postulate for the logic programming case such that they capture the sa ...

... been proven to be useful, cf. (Delgrande 2008). For logic programing it also does not have the same undesirable implications as we shall see later on. Due to the non-monotonicity of inconsistency we can strengthen the Consistency postulate for the logic programming case such that they capture the sa ...

Applications of MaxSAT in Automotive Configuration - CEUR

... there is the following: Given a (sub-)set O = {o1 , . . . , on } of equipment options and a set C = {c1 , . . . , cm } of configuration constraints whose variables are all options, is it possible to configure a car with the options in O such that C is satisfied? This gives us the SAT problem SAT(C ∪ ...

... there is the following: Given a (sub-)set O = {o1 , . . . , on } of equipment options and a set C = {c1 , . . . , cm } of configuration constraints whose variables are all options, is it possible to configure a car with the options in O such that C is satisfied? This gives us the SAT problem SAT(C ∪ ...

What is a Knowledge Representation

... possibility of one more source of error against the gains (e.g., efficiency) it may offer. We do not suggest that unsound reasoning ought to be embraced casually, but do claim that, given the inevitability of error even with sound reasoning, it makes sense to evaluate pragmatically the relative cost ...

... possibility of one more source of error against the gains (e.g., efficiency) it may offer. We do not suggest that unsound reasoning ought to be embraced casually, but do claim that, given the inevitability of error even with sound reasoning, it makes sense to evaluate pragmatically the relative cost ...