## Clp(bn): Constraint logic programming for probabilistic knowledge (2003)

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Venue: | In Proceedings of the 19th Conference on Uncertainty in Artificial Intelligence (UAI03 |

Citations: | 49 - 6 self |

### BibTeX

@INPROCEEDINGS{Costa03clp(bn):constraint,

author = {Vítor Santos Costa and James Cussens},

title = {Clp(bn): Constraint logic programming for probabilistic knowledge},

booktitle = {In Proceedings of the 19th Conference on Uncertainty in Artificial Intelligence (UAI03},

year = {2003},

pages = {517--524},

publisher = {Morgan Kaufmann}

}

### Years of Citing Articles

### OpenURL

### Abstract

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

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Citation Context ...sion in Logic provides an elegant framework for modeling sequences of events, such as Markov Models. Next we discuss how the main ideas of CLP(BN) can be used to represent Hidden Markov Models (HMMs) =-=[19]-=-, which are used for a number of applications ranging from Signal Processing, to Natural Language Processing, to Bioinformatics, and Dynamic Bayes Networks (DBNs). This was inspired by prior work on c... |

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Citation Context ...ward at integrating CLP(BN) with some of recent work in generating statistical classifiers [42–46]. Last, it would be interesting to study whether the ideas of CLP(BN) also apply to undirected models =-=[47]-=-. We are also considering directions to improve CLP((BN). Regarding implementation, most effort will focus on tabling [28,29] that avoids repeated invocation of the same literal and can be quite usefu... |

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Citation Context ... Bayesian Networks would avoid wasted work and possible mistakes. Moreover, it would make it easier to learn interesting patterns in data. Work such as Koller’s Probabilistic Relational Models (PRMs) =-=[3]-=-, Sato’s PRISM [4], Ngo and Haddawy’ss2 Vítor Santos Costa and David Page and James Cussens Probabilistic Logic Programs [5], Muggleton and Cussens’ Stochastic Logic Programs [6], and Kersting and De ... |

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Citation Context ...ns to the ILP systems. Induction of clauses can be seen as model generation, and parameter fitting can be seen as generating the CPTs for the constraint of a clause. We show that the ILP system aleph =-=[8]-=- is able to learn CLP(BN) programs. Next, we present the design of CLP(BN) through examples. We then discuss the foundations of CLP(BN), including detailed syntax, proof theory (or operational semanti... |

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Citation Context ...m with both database style and recursive programs. The main focus of our future work will be in learning with CLP(BN) programs. Namely, we are now working with CLP(BN) on inducing regulatory networks =-=[40,41]-=-. We are also looking forward at integrating CLP(BN) with some of recent work in generating statistical classifiers [42–46]. Last, it would be interesting to study whether the ideas of CLP(BN) also ap... |

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Citation Context ...this graph [1]. Evaluating all the relevant evidence is a complex process: first, we need to track down all relevant sources of evidence, through algorithms such as knowledge based model construction =-=[11]-=-. Next, we need to perform probabilistic inference on this graph and marginalize the probabilities on the query variables. To do so would require an extra program, which would have to process both the... |

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Citation Context ...MMs and DBNs: evaluation and learning of HMMs is part of PRISM [20,21], Dynamic Probabilistic Relational Models combine PRMs and DBNs [22], Logical HMMs have been used to model protein structure data =-=[23, 24]-=-. More recently, non-directed models such as LogCRFs have also been proposed toward this goal [25] Next, we discuss how to model HMMs and DBNs in CLP(BN). We present our experience in modeling profile... |

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Citation Context ...d dynamic programming algorithms. To the best of our knowledge, PRISM was the first language to use tabling for this task [20]. The CLP(BN) implementation originally relied on YAP’s tabling mechanism =-=[29]-=-. Unfortunately, the YAP implementation of tabling is optimized for efficient evaluation of non-deterministic goals; we have achieved better performance through a simple program transformation. Given ... |

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Citation Context ...owledge-based model construction (KBMC) in that it uses logic “as a basis for generating Bayesian networks tailored to particular problem instances” [11]. However, in contrast to many KBMC approaches =-=[11, 36]-=-, a probability in a CLP(BN) program does not give the probability that some first-order rule is true. Instead it is a (soft) constraint on possible instantiations of a variable in a rule. This also d... |

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Citation Context ...ics, and Dynamic Bayes Networks (DBNs). This was inspired by prior work on combining the advantages of multi-relational approaches with HMMs and DBNs: evaluation and learning of HMMs is part of PRISM =-=[20,21]-=-, Dynamic Probabilistic Relational Models combine PRMs and DBNs [22], Logical HMMs have been used to model protein structure data [23, 24]. More recently, non-directed models such as LogCRFs have also... |

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Citation Context ... through different proofs. We combine information using aggregation (see Section 5), and the predicates for aggregation are part of the CLP(BN) program. This contrasts with the approach taken in both =-=[35]-=- and [7] where a combining rule is added on top of the logical representation. CLP(BN) implements Knowledge-based model construction (KBMC) in that it uses logic “as a basis for generating Bayesian ne... |

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Citation Context ...MMs and DBNs: evaluation and learning of HMMs is part of PRISM [20,21], Dynamic Probabilistic Relational Models combine PRMs and DBNs [22], Logical HMMs have been used to model protein structure data =-=[23, 24]-=-. More recently, non-directed models such as LogCRFs have also been proposed toward this goal [25] Next, we discuss how to model HMMs and DBNs in CLP(BN). We present our experience in modeling profile... |

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Citation Context ... point we have a small nice little logic program that fully explains the database. We believe this representation is very attractive (indeed, a similar approach was proposed independently by Blockeel =-=[9]-=-), but it does have one major limitation: it hides the difference between doing inference in first order logic and in Bayesian network, as we discuss next. Evidence We have observed that mary’s grade ... |

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Citation Context ...m with both database style and recursive programs. The main focus of our future work will be in learning with CLP(BN) programs. Namely, we are now working with CLP(BN) on inducing regulatory networks =-=[40,41]-=-. We are also looking forward at integrating CLP(BN) with some of recent work in generating statistical classifiers [42–46]. Last, it would be interesting to study whether the ideas of CLP(BN) also ap... |

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Citation Context ...dom variables with ground atoms—in CLP(BN) they are represented by (Bayesian) variables.s30 Vítor Santos Costa and David Page and James Cussens In Angelopoulos’s probabilistic finite domain Pfd model =-=[38]-=- hard constraints between variables and probability distributions over the same variables are kept deliberately separate, thereby allowing a normal CLP constraint solver to find variable instantiation... |

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Citation Context ...ystem relies on the main principles of Knowledge Based Model Construction (KBMC), and it supports four difference methods: belief propagation, gibbs sampling, junction trees, and variable elimination =-=[1]-=-. The package includes an interface to the Inductive Logic Programming System Aleph that can be used to extend Prolog clauses into CLP(BN ) clauses, simply by declaring special random types. Clause le... |

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Citation Context ... themselves ground terms, and every ground term denotes itself. Because we wish to consider cases where ground Skolem terms denote (non-Skolem) constants, we instead consider Herbrand quotient models =-=[13]-=-. In a Herbrand quotient model, the individuals are equivalence classes of ground terms, and any ground term denotes the equivalence class to which it belongs. Then two ground terms are equal accordin... |

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Citation Context ...alling the same goal repeatedly over and over again. This problem can be addressed by tabling calls so that only the first one is actually executed, and repeated calls just need to lookup a data-base =-=[28]-=-. Tabled execution of these programs has the same complexity as standard dynamic programming algorithms. To the best of our knowledge, PRISM was the first language to use tabling for this task [20]. T... |

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