A Syntax-based Framework for Merging Imprecise Probabilistic Logic Programs
نویسندگان
چکیده
In this paper, we address the problem of merging multiple imprecise probabilistic beliefs represented as Probabilistic Logic Programs (PLPs) obtained from multiple sources. Beliefs in each PLP are modeled as conditional events attached with probability bounds. The major task of syntax-based merging is to obtain the most rational probability bound for each conditional event from the original PLPs to form a new PLP. We require the minimal change principle to be followed so that each source gives up its beliefs as little as possible. Some instantiated merging operators are derived from our merging framework. Furthermore, we propose a set of postulates for merging PLPs, some of which extend the postulates for merging classical knowledge bases, whilst others are specific to the merging of probabilistic beliefs.
منابع مشابه
A Hybrid Approach to Inference in Probabilistic Non-Monotonic Logic Programming
We present a probabilistic inductive logic programming framework which integrates non-monotonic reasoning, probabilistic inference and parameter learning. In contrast to traditional approaches to probabilistic Answer Set Programming (ASP), our framework imposes only comparatively little restrictions on probabilistic logic programs in particular, it allows for ASP as well as FOL syntax, and for ...
متن کاملAnnotated Linguistic Logic Programs for Soft Computing
—Several approaches have been proposed for modeling and computing with linguistic information in natural language, among which Lawry's label semantics provides a clear interpretation of linguistic expressions and thus a transparent model for real-world applications. Meanwhile annotated logic programs have been developed as an extension of classical logic programs offering a powerful computation...
متن کاملPrASP Report
This technical report describes the usage, syntax, semantics and core algorithms of the probabilistic inductive logic programming framework PrASP. PrASP is a research software which integrates non-monotonic reasoning based on Answer Set Programming (ASP), probabilistic inference and parameter learning. In contrast to traditional approaches to Probabilistic (Inductive) Logic Programming, our fra...
متن کاملA Design Methodology for Reliable MRF-Based Logic Gates
Probabilistic-based methods have been used for designing noise tolerant circuits recently. In these methods, however, there is not any reliability mechanism that is essential for nanometer digital VLSI circuits. In this paper, we propose a novel method for designing reliable probabilistic-based logic gates. The advantage of the proposed method in comparison with previous probabilistic-based met...
متن کاملPossible Worlds Semantics for Probabilistic Logic Programs
In this paper we consider a logic programming framework for reasoning about imprecise probabilities. In particular, we propose a new semantics, for the Probabilistic Logic Programs (p-programs) of Ng and Subrahmanian. Pprograms represent imprecision using probability intervals. Our semantics, based on the possible worlds semantics, considers all point probability distributions that satisfy a gi...
متن کامل