Learning to Assign Degrees of Belief in Relational Domains (Extended Abstract) - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Conference Papers Year : 2007

Learning to Assign Degrees of Belief in Relational Domains (Extended Abstract)

Frédéric Koriche

Abstract

A recurrent problem in the development of reasoning agents is how to assign degrees of beliefs to uncertain events in a complex environment. The standard knowledge representation framework imposes a sharp separation between learning and reasoning; the agent starts by acquiring a “model” of its environment, represented into an expressive language, and then uses this model to quantify the likelihood of various queries. Yet, even for simple queries, the problem of evaluating probabilities from a general purpose representation is computationally prohibitive. By contrast, this study embarks on the learning to reason (L2R) framework that aims at eliciting degrees of belief in an inductive manner. The agent is viewed as an anytime reasoner that iteratively improves its performance in light of the knowledge induced from its mistakes. By coupling exponentiated gradient strategies in online learning and weighted model counting techniques in reasoning, the L2R framework is shown to provide efficient solutions to relational probabilistic reasoning problems that are provably intractable in the classical framework.
Fichier principal
Vignette du fichier
Kor-ILP07.pdf (367.31 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

lirmm-00180722 , version 1 (19-10-2007)
lirmm-00180722 , version 2 (01-09-2008)

Identifiers

  • HAL Id : lirmm-00180722 , version 2

Cite

Frédéric Koriche. Learning to Assign Degrees of Belief in Relational Domains (Extended Abstract). ILP: Inductive Logic Programming, 2007, Corvallis, Oregon, United States. pp.15-29. ⟨lirmm-00180722v2⟩
137 View
58 Download

Share

Gmail Facebook X LinkedIn More