Results 1  10
of
15
CPlogic: A Language of Causal Probabilistic Events and Its Relation to Logic Programming
"... We examine the relation between constructive processes and the concept of causality. We observe that causality has an inherent dynamic aspect, i.e., that, in essence, causal information concerns the evolution of a domain over time. Motivated by this observation, we construct a new representation lan ..."
Abstract

Cited by 33 (4 self)
 Add to MetaCart
We examine the relation between constructive processes and the concept of causality. We observe that causality has an inherent dynamic aspect, i.e., that, in essence, causal information concerns the evolution of a domain over time. Motivated by this observation, we construct a new representation language for causal knowledge, whose semantics is defined explicitly in terms of constructive processes. This is done in a probabilistic context, where the basic steps that make up the process are allowed to have nondeterministic effects. We then show that a theory in this language defines a unique probability distribution over the possible outcomes of such a process. This result offers an appealing explanation for the usefulness of causal information and links our explicitly dynamic approach to more static causal probabilistic modeling languages, such as Bayesian networks. We also show that this language, which we have constructed to be a natural formalization of a certain kind of causal statements, is closely related to logic programming. This result demonstrates that, under an appropriate formal semantics, a rule of a normal, a disjunctive or a certain kind of probabilistic logic program can be interpreted as a description of a causal event.
A top down interpreter for LPAD and CPlogic
 In Congress of the Italian Association for Artificial Intelligence. Number 4733 in LNAI
"... are two different but related languages for expressing probabilistic information in logic programming. The paper presents a top down interpreter for computing the probability of a query from a program in one of these two languages when the program is acyclic. The algorithm is based on the one availa ..."
Abstract

Cited by 17 (12 self)
 Add to MetaCart
(Show Context)
are two different but related languages for expressing probabilistic information in logic programming. The paper presents a top down interpreter for computing the probability of a query from a program in one of these two languages when the program is acyclic. The algorithm is based on the one available for ProbLog. The performances of the algorithm are compared with those of a Bayesian reasoner and with those of the ProbLog interpreter. On programs that have a small grounding, the Bayesian reasoner is more scalable, but programs with a large grounding require the top down interpreter. The comparison with ProbLog shows that, even if the added expressiveness effectively requires more computation resources, the top down interpreter can still solve problem of significant size. 1
L.D.: A simple model for sequences of relational state descriptions
 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2008) Part II. Lecture Notes in Computer Science
, 2008
"... Abstract. Artificial intelligence aims at developing agents that learn and act in complex environments. Realistic environments typically feature a variable number of objects, relations amongst them, and nondeterministic transition behavior. Standard probabilistic sequence models provide efficient ..."
Abstract

Cited by 9 (3 self)
 Add to MetaCart
(Show Context)
Abstract. Artificial intelligence aims at developing agents that learn and act in complex environments. Realistic environments typically feature a variable number of objects, relations amongst them, and nondeterministic transition behavior. Standard probabilistic sequence models provide efficient inference and learning techniques, but typically cannot fully capture the relational complexity. On the other hand, statistical relational learning techniques are often too inefficient. In this paper, we present a simple model that occupies an intermediate position in this expressiveness/efficiency tradeoff. It is based on CPlogic, an expressive probabilistic logic for modeling causality. However, by specializing CPlogic to represent a probability distribution over sequences of relational state descriptions, and employing a Markov assumption, inference and learning become more tractable and effective. We show that the resulting model is able to handle probabilistic relational domains with a substantial number of objects and relations. 1
Building a knowledge base system for an integration of logic programming and classical logic
 In ICLP. 71–76
"... Abstract. This paper presents a Knowledge Base project for FO(ID), an extension of classical logic with inductive definitions. This logic is a natural integration of classical logic and logic programming based on the view of a logic program as a definition. We discuss the relationship between induc ..."
Abstract

Cited by 3 (3 self)
 Add to MetaCart
(Show Context)
Abstract. This paper presents a Knowledge Base project for FO(ID), an extension of classical logic with inductive definitions. This logic is a natural integration of classical logic and logic programming based on the view of a logic program as a definition. We discuss the relationship between inductive definitions and common sense reasoning and the strong similarities and striking differences with ASP and Abductive LP. We report on inference systems that combine stateoftheart techniques of SAT and ASP. Experiments show that FO(ID) model expansion systems are competitive with the best ASPsolvers. 1
CHRiSM: CHance Rules induce Statistical Models
 In: Proceedings of the Sixth International Workshop on Constraint Handling Rules
, 2009
"... Abstract. A new probabilisticlogic formalism, called CHRiSM, is introduced. CHRiSM is based on a combination of CHR and PRISM. It can be used for highlevel rapid prototyping of complex statistical models by means of chance rules. The underlying PRISM system can then be used for several probabilist ..."
Abstract

Cited by 2 (1 self)
 Add to MetaCart
(Show Context)
Abstract. A new probabilisticlogic formalism, called CHRiSM, is introduced. CHRiSM is based on a combination of CHR and PRISM. It can be used for highlevel rapid prototyping of complex statistical models by means of chance rules. The underlying PRISM system can then be used for several probabilistic inference tasks, including parameter learning. We describe a sourcetosource transformation from CHRiSM rules to PRISM, via CHR(PRISM). Finally we discuss the relation between CHRiSM and probabilistic logic programming, in particular, CPlogic. 1
cplint and PITA
"... programs [9, 10]. They share the following syntax for input programs. 1 Syntax Disjunction in the head is represented with a semicolon and atoms in the head are separated from probabilities by a colon. For the rest, the usual syntax of Prolog is used. For example, the LPAD clause h1: p1 ∨... ∨ hn: p ..."
Abstract
 Add to MetaCart
(Show Context)
programs [9, 10]. They share the following syntax for input programs. 1 Syntax Disjunction in the head is represented with a semicolon and atoms in the head are separated from probabilities by a colon. For the rest, the usual syntax of Prolog is used. For example, the LPAD clause h1: p1 ∨... ∨ hn: pn ← b1,..., bm, ¬c1,..., ¬cl is represented by h1:p1;...; hn:pn: b1,...,bm,\+ c1,....,\+ cl No parentheses are necessary. The pi are numeric expressions. It is up to the user to ensure that the numeric expressions are legal, i.e. that they sum up to less than one. If the clause has a single head with probability 1, the annotation can be omitted and the clause takes the form of a normal prolog clause. The coin example of [12] is represented as heads(Coin):1/2; tails(Coin):1/2:heads(Coin):0.6; tails(Coin):0.4:fair(Coin):0.9; biased(Coin):0.1. toss(coin). toss(Coin),\+biased(Coin). toss(Coin),biased(Coin). 2 cplint cplint consists of three Prolog modules for answering queries using goaloriented procedures. lpadsld.pl: computes the probability of a query using the topdown procedure described in in [5] and [6]. It is based on SLDNF resolution and is an adaptation of the interpreter for ProbLog [2]. lpad.pl: computes the probability of a query using a topdown procedure based on SLG resolution [1]. As a consequence, it works for any sound LPADs, i.e., any LPAD such that each of its instances has a two valued well founded model. cpl.pl: computes the probability of a query using a topdown procedure based on SLG resolution and moreover checks that the CPlogic program is valid, i.e., that it has at least an execution model. 2.1 Installation cplint is distributed in source code in the git version of Yap. It includes Prolog and C files. Download it by following the instruction in
Celestijnenlaan 200A – B3001 Heverlee (Belgium) On the equivalence between CPlogic
, 2008
"... We give a detailed proof of the fact that the probabilistic logics of Logic Programs with Annotated Disjunctions (LPADs) and CPlogic are equivalent. This report contains a detailed proof of the fact that Logic Programs with Annotated Disjunctions (LPADs) (6) and CPlogic (5) are equivalent. Before ..."
Abstract
 Add to MetaCart
We give a detailed proof of the fact that the probabilistic logics of Logic Programs with Annotated Disjunctions (LPADs) and CPlogic are equivalent. This report contains a detailed proof of the fact that Logic Programs with Annotated Disjunctions (LPADs) (6) and CPlogic (5) are equivalent. Before moving on to this proof, we first present some preliminaries from lattice theory and logic programming, and summarize the definition of LPADs and CPlogic. 1
Biclustering: A Case Study with Surprising Results
, 2010
"... Many approaches to probabilistic logical learning have been proposed by now, and several of these have been implemented into powerful learning and inference systems. Given this state of the art, it appears natural to start using these systems for solving concrete problems. This paper presents some r ..."
Abstract
 Add to MetaCart
(Show Context)
Many approaches to probabilistic logical learning have been proposed by now, and several of these have been implemented into powerful learning and inference systems. Given this state of the art, it appears natural to start using these systems for solving concrete problems. This paper presents some results of a case study where several probabilistic logical learning systems have been applied to a seemingly simple problem that exhibits both probabilistic and relational aspects. The results are surprisingly negative: none of the systems we have tried could adequately handle the problem at hand. We discuss the reasons for this. This leads to several conclusions. First, still more effort must be invested in developing fullfledged implementations that can handle a wide range of realistic problems. Second, the intrinsic limitations of certain approaches may not yet be fully understood. Third, the problem we discuss here may be an interesting application for probabilistic logical learning systems, and we invite other researchers to use it as a benchmark for evaluating the applicability of their favorite systems.
CPTL: an Efficient Model for Relational Stochastic Processes
"... Agents that learn and act in realworld environments have to cope with both complex state descriptions and nondeterministic transition behavior of the world. Standard statistical relational learning techniques can capture this complexity, but are often inefficient. We present a simple probabilistic ..."
Abstract
 Add to MetaCart
(Show Context)
Agents that learn and act in realworld environments have to cope with both complex state descriptions and nondeterministic transition behavior of the world. Standard statistical relational learning techniques can capture this complexity, but are often inefficient. We present a simple probabilistic model for such environments based on CPLogic. Efficiency is maintained by restriction to a fully observable setting and the use of efficient inference algorithms based on binary decision diagrams. 1.