An integrated development environment for probabilistic relational reasoning
نویسندگان
چکیده
This paper presents KReator, a versatile integrated development environment for probabilistic inductive logic programming currently under development. The area of probabilistic inductive logic programming (or statistical relational learning) aims at applying probabilistic methods of inference and learning in relational or first-order representations of knowledge. In the past ten years the community brought forth a lot of proposals to deal with problems in that area which mostly extend existing propositional probabilistic methods like Bayes Nets and Markov Networks on relational settings. Only few developers provide prototypical implementations of their approaches and the existing applications are often difficult to install and to use. Furthermore, due to different languages and frameworks used for the development of different systems the task of comparing various approaches becomes hard and tedious. KReator aims at providing a common and simple interface for representing, reasoning, and learning with different relational probabilistic approaches. It is a general integrated development environment which enables the integration of various frameworks within the area of probabilistic inductive logic programming and statistical relational learning. Currently, KReator implements Bayesian logic programs, Markov logic networks, and relational maximum entropy under grounding semantics. More approaches will be implemented in the near future or can be implemented by researchers themselves as KReator is open-source and available under public license. In this paper, we provide some background on probabilistic inductive logic programming and statistical relational learning and illustrate the usage of KReator on several examples using the three approaches currently implemented in KReator. Furthermore, we give an overview on its system architecture.
منابع مشابه
Generation of Parametrically Uniform Knowledge Bases in a Relational Probabilistic Logic with Maximum Entropy Semantics
In a relational setting, the maximum entropy model of a set of probabilistic conditionals can be defined referring to the full set of ground instances of the conditionals. The logic FO-PCL uses the notion of parametric uniformity to ensure that the full grounding of the conditionals can be avoided, thereby greatly simplifying the maximum entropy model computation. In this paper, we describe a s...
متن کاملProbabilistic Backward and Forward Reasoning in Stochastic Relational Worlds
Inference in graphical models has emerged as a promising technique for planning. A recent approach to decision-theoretic planning in relational domains uses forward inference in dynamic Bayesian networks compiled from learned probabilistic relational rules. Inspired by work in non-relational domains with small state spaces, we derive a backpropagation method for such nets in relational domains ...
متن کاملRelational Probabilistic Conditional Reasoning at Maximum Entropy
This paper presents and compares approaches for reasoning with relational probabilistic conditionals, i. e. probabilistic conditionals in a restricted first-order environment. It is well-known that conditionals play a crucial role for default reasoning, however, most formalisms are based on propositional conditionals, which restricts their expressivity. The formalisms discussed in this paper ar...
متن کاملTowards a Toolbox for Relational Probabilistic Knowledge Representation, Reasoning, and Learning
This paper presents KReator, a versatile and easy-to-use toolbox for statistical relational learning currently under development. The research on combining probabilistic models and first-order theory put forth a lot of different approaches in the past few years. While every approach has advantages and disadvantages the variety of prototypical implementations make thorough comparisons of differe...
متن کاملAutomated Engineering of Relational and Algebraic Methods in Isabelle/HOL - (Invited Tutorial)
We present a new integration of relational and algebraic methods in the Isabelle/HOL theorem proving environment. It consists of a fine grained hierarchy of algebraic structures based on Isabelle’s type classes and locales, and a repository of more than 800 facts obtained by automated theorem proving. We demonstrate further benefits of Isabelle for hypothesis learning, duality reasoning, theore...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Logic Journal of the IGPL
دوره 20 شماره
صفحات -
تاریخ انتشار 2012