Results 1  10
of
128
Lifted firstorder probabilistic inference
 In Proceedings of IJCAI05, 19th International Joint Conference on Artificial Intelligence
, 2005
"... Most probabilistic inference algorithms are specified and processed on a propositional level. In the last decade, many proposals for algorithms accepting firstorder specifications have been presented, but in the inference stage they still operate on a mostly propositional representation level. [Poo ..."
Abstract

Cited by 125 (8 self)
 Add to MetaCart
Most probabilistic inference algorithms are specified and processed on a propositional level. In the last decade, many proposals for algorithms accepting firstorder specifications have been presented, but in the inference stage they still operate on a mostly propositional representation level. [Poole, 2003] presented a method to perform inference directly on the firstorder level, but this method is limited to special cases. In this paper we present the first exact inference algorithm that operates directly on a firstorder level, and that can be applied to any firstorder model (specified in a language that generalizes undirected graphical models). Our experiments show superior performance in comparison with propositional exact inference. 1
Towards Combining Inductive Logic Programming with Bayesian Networks
, 2001
"... Recently, new representation languages that integrate first order logic with Bayesian networks have been developed. Bayesian logic programs are one of these languages. In this paper, we present results on combining Inductive Logic Programming (ILP) with Bayesian networks to learn both the qualitativ ..."
Abstract

Cited by 82 (12 self)
 Add to MetaCart
(Show Context)
Recently, new representation languages that integrate first order logic with Bayesian networks have been developed. Bayesian logic programs are one of these languages. In this paper, we present results on combining Inductive Logic Programming (ILP) with Bayesian networks to learn both the qualitative and the quantitative components of Bayesian logic programs. More precisely, we show how to combine the ILP setting learning from interpretations with scorebased techniques for learning Bayesian networks. Thus, the paper positively answers Koller and Pfeffer's question, whether techniques from ILP could help to learn the logical component of first order probabilistic models.
Logic programs with annotated disjunctions
 In Proc. Int’l Conf. on Logic Programming
, 2004
"... Abstract. Current literature offers a number of different approaches to what could generally be called "probabilistic logic programming". These are usually based on Horn clauses. Here, we introduce a new formalism, Logic Programs with Annotated Disjunctions, based on disjunctive lo ..."
Abstract

Cited by 77 (6 self)
 Add to MetaCart
(Show Context)
Abstract. Current literature offers a number of different approaches to what could generally be called &quot;probabilistic logic programming&quot;. These are usually based on Horn clauses. Here, we introduce a new formalism, Logic Programs with Annotated Disjunctions, based on disjunctive logic programs. In this formalism, each of the disjuncts in the head of a clause is annotated with a probability. Viewing such a set of probabilistic disjunctive clauses as a probabilistic disjunction of normal logic programs allows us to derive a possible world semantics, more precisely, a probability distribution on the set of all Herbrand interpretations. We demonstrate the strength of this formalism by some examples and compare it to related work.
Probabilistic inductive logic programming
 In ALT
, 2004
"... Abstract. Probabilistic inductive logic programming aka. statistical relational learning addresses one of the central questions of artificial intelligence: the integration of probabilistic reasoning with machine learning and first order and relational logic representations. A rich variety of diffe ..."
Abstract

Cited by 70 (9 self)
 Add to MetaCart
(Show Context)
Abstract. Probabilistic inductive logic programming aka. statistical relational learning addresses one of the central questions of artificial intelligence: the integration of probabilistic reasoning with machine learning and first order and relational logic representations. A rich variety of different formalisms and learning techniques have been developed. A unifying characterization of the underlying learning settings, however, is missing so far. In this chapter, we start from inductive logic programming and sketch how the inductive logic programming formalisms, settings and techniques can be extended to the statistical case. More precisely, we outline three classical settings for inductive logic programming, namely learning from entailment, learning from interpretations, and learning from proofs or traces, and show how they can be adapted to cover stateoftheart statistical relational learning approaches. 1
Clp(bn): Constraint logic programming for probabilistic knowledge
 In Proceedings of the 19th Conference on Uncertainty in Artificial Intelligence (UAI03
, 2003
"... Abstract. In Datalog, missing values are represented by Skolem constants. More generally, in logic programming missing values, or existentially quantified variables, are represented by terms built from Skolem functors. The CLP(BN) language represents the joint probability distribution over missing v ..."
Abstract

Cited by 64 (7 self)
 Add to MetaCart
Abstract. In Datalog, missing values are represented by Skolem constants. More generally, in logic programming missing values, or existentially quantified variables, are represented by terms built from Skolem functors. The CLP(BN) language represents the joint probability distribution over missing values in a database or logic program by using constraints to represent Skolem functions. Algorithms from inductive logic programming (ILP) can be used with only minor modification to learn CLP(BN) programs. An implementation of CLP(BN) is publicly available as part of YAP Prolog at
Theorybased causal induction
 In
, 2003
"... Inducing causal relationships from observations is a classic problem in scientific inference, statistics, and machine learning. It is also a central part of human learning, and a task that people perform remarkably well given its notorious difficulties. People can learn causal structure in various s ..."
Abstract

Cited by 51 (18 self)
 Add to MetaCart
Inducing causal relationships from observations is a classic problem in scientific inference, statistics, and machine learning. It is also a central part of human learning, and a task that people perform remarkably well given its notorious difficulties. People can learn causal structure in various settings, from diverse forms of data: observations of the cooccurrence frequencies between causes and effects, interactions between physical objects, or patterns of spatial or temporal coincidence. These different modes of learning are typically thought of as distinct psychological processes and are rarely studied together, but at heart they present the same inductive challenge—identifying the unobservable mechanisms that generate observable relations between variables, objects, or events, given only sparse and limited data. We present a computationallevel analysis of this inductive problem and a framework for its solution, which allows us to model all these forms of causal learning in a common language. In this framework, causal induction is the product of domaingeneral statistical inference guided by domainspecific prior knowledge, in the form of an abstract causal theory. We identify 3 key aspects of abstract prior knowledge—the ontology of entities, properties, and relations that organizes a domain; the plausibility of specific causal relationships; and the functional form of those relationships—and show how they provide the constraints that people need to induce useful causal models from sparse data.
Building Large Knowledge Bases by Mass Collaboration
, 2003
"... Acquiring knowledge has long been the major bottleneck preventing the rapid spread of AI systems. Manual approaches are slow and costly. Machinelearning approaches have limitations in the depth and breadth of knowledge they can acquire. The spread of the Internet has made possible a third solu ..."
Abstract

Cited by 44 (4 self)
 Add to MetaCart
Acquiring knowledge has long been the major bottleneck preventing the rapid spread of AI systems. Manual approaches are slow and costly. Machinelearning approaches have limitations in the depth and breadth of knowledge they can acquire. The spread of the Internet has made possible a third solution: building knowledge bases by mass collaboration, with thousands of volunteers contributing simultaneously. While this approach promises large improvements in the speed and cost of knowledge base development, it can only succeed if the problem of ensuring the quality, relevance and consistency of the knowledge is addressed, if contributors are properly motivated, and if the underlying algorithms scale. In this paper we propose an architecture that meets all these desiderata. It uses firstorder probabilistic reasoning techniques to combine potentially inconsistent knowledge sources of varying quality, and it uses machinelearning techniques to estimate the quality of knowledge. We evaluate the approach using a series of synthetic knowledge bases and a pilot study in the domain of printer troubleshooting.
Probabilistic Logic Learning
 ACMSIGKDD Explorations: Special issue on MultiRelational Data Mining
, 2004
"... The past few years have witnessed an significant interest in probabilistic logic learning, i.e. in research lying at the intersection of probabilistic reasoning, logical representations, and machine learning. A rich variety of di#erent formalisms and learning techniques have been developed. This pap ..."
Abstract

Cited by 43 (10 self)
 Add to MetaCart
The past few years have witnessed an significant interest in probabilistic logic learning, i.e. in research lying at the intersection of probabilistic reasoning, logical representations, and machine learning. A rich variety of di#erent formalisms and learning techniques have been developed. This paper provides an introductory survey and overview of the stateof theart in probabilistic logic learning through the identification of a number of important probabilistic, logical and learning concepts.
Speeding up Relational Reinforcement Learning Through the Use of an Incremental First Order Decision Tree Learner
 Proceedings of the 13th European Conference on Machine Learning
, 2001
"... Relational reinforcement learning (RRL) is a learning technique that combines standard reinforcement learning with inductive logic programming to enable the learning system to exploit structural knowledge about the application domain. ..."
Abstract

Cited by 43 (23 self)
 Add to MetaCart
(Show Context)
Relational reinforcement learning (RRL) is a learning technique that combines standard reinforcement learning with inductive logic programming to enable the learning system to exploit structural knowledge about the application domain.
MRDTL: A multirelational decision tree learning algorithm
 Proceedings of the 13th International Conference on Inductive Logic Programming (ILP 2003
, 2002
"... this paper, we have described an implementation of multirelational decision tree learning (MRDTL) algorithm based on the techniques proposed by Knobbe et el. (Knobbe et el., 1999a, Knobbe et el., 1999b) ..."
Abstract

Cited by 38 (2 self)
 Add to MetaCart
(Show Context)
this paper, we have described an implementation of multirelational decision tree learning (MRDTL) algorithm based on the techniques proposed by Knobbe et el. (Knobbe et el., 1999a, Knobbe et el., 1999b)