Probabilistic Dynamic Epistemic Logic

In this paper I combine the dynamic epistemic logic ofGerbrandy (1999) with the probabilistic logic of Fagin and Halpern (1994). The resultis a new probabilistic dynamic epistemic logic, a logic for reasoning aboutprobability, information, and information change that takes higher orderinformation into account. Probabilistic epistemic models are defined, and away to build them for applications is given. Semantics and a proof systemis presented and a number of examples are discussed, including the MontyHall Dilemma.     

Probabilistic Belief Logic and Its Probabilistic Aumann Semantics

In this paper, we present a logic system for probabilistic belief named PBL,which expands the language of belief logic by introducing probabilistic belief. Furthermore, we give the probabilistic Aumann semantics of PBL. We also list some valid properties of belief and probabilistic belief, which form the deduction system of PBL. Finally, we prove the soundness and completeness of these properties with respect to probabilistic Aumann semantics.     

Probabilistic logic under coherence: complexity and algorithms

In previous work [V. Biazzo, A. Gilio, T. Lukasiewicz and G. Sanfilippo, Probabilistic logic under coherence, model-theoretic probabilistic logic, and default reasoning in System P, Journal of Applied Non-Classical Logics 12(2) (2002) 189–213.], we have explored the relationship between probabilistic reasoning under coherence and model-theoretic probabilistic reasoning. In particular, we have shown that the notions of g-coherence and of g-coherent entailment in probabilistic reasoning under coherence can be expressed by combining notions in model-theoretic probabilistic reasoning with concepts from default reasoning. In this paper, we continue this line of research. Based on the above semantic results, we draw a precise picture of the computational complexity of probabilistic reasoning under coherence. Moreover, we introduce transformations for probabilistic reasoning under coherence, which reduce an instance of deciding g-coherence or of computing tight intervals under g-coherent entailment to a smaller problem instance, and which can be done very efficiently. Furthermore, we present new algorithms for deciding g-coherence and for computing tight intervals under g-coherent entailment, which reformulate previous algorithms using terminology from default reasoning. They are based on reductions to standard problems in model-theoretic probabilistic reasoning, which in turn can be reduced to linear optimization problems. Hence, efficient techniques for model-theoretic probabilistic reasoning can immediately be applied for probabilistic reasoning under coherence (for example, column generation techniques). We describe several such techniques, which transform problem instances in model-theoretic probabilistic reasoning into smaller problem instances. We also describe a technique for obtaining a reduced set of variables for the associated linear optimization problems in the conjunctive case, and give new characterizations of this reduced set as a set of non-decomposable variables, and using the concept of random gain. This paper is a substantially extended and revised version of a preliminary paper that appeared in: Proceedings of the Second International Symposium on Imprecise Probabilities and Their Applications (ISIPTA '01), pp. 51–61, 2001.     

Learning probabilistic logic models from probabilistic examples

We revisit an application developed originally using abductive Inductive Logic Programming (ILP) for modeling inhibition in metabolic networks. The example data was derived from studies of the effects of toxins on rats using Nuclear Magnetic Resonance (NMR) time-trace analysis of their biofluids together with background knowledge representing a subset of the Kyoto Encyclopedia of Genes and Genomes (KEGG). We now apply two Probabilistic ILP (PILP) approaches—abductive Stochastic Logic Programs (SLPs) and PRogramming In Statistical modeling (PRISM) to the application. Both approaches support abductive learning and probability predictions. Abductive SLPs are a PILP framework that provides possible worlds semantics to SLPs through abduction. Instead of learning logic models from non-probabilistic examples as done in ILP, the PILP approach applied in this paper is based on a general technique for introducing probability labels within a standard scientific experimental setting involving control and treated data. Our results demonstrate that the PILP approach provides a way of learning probabilistic logic models from probabilistic examples, and the PILP models learned from probabilistic examples lead to a significant decrease in error accompanied by improved insight from the learned results compared with the PILP models learned from non-probabilistic examples.     

人工智能中概率逻辑的研究

概率论是在不完备的、不确定的数据中进行推理的,它是度量不确定性的重要手段。在人工智能中,研究者结合概率和逻辑各自的优点,进行概率逻辑的研究。本文介绍了传统概率逻辑的三大派别,阐述了二值逻辑概率和三值逻辑概率的发展;最后介绍了泛逻辑,通过对概率逻辑和泛逻辑学的研究,将概率逻辑纳入泛逻辑学的框架内。     

人工智能中概率逻辑的研究

概率论是在不完备的、不确定的数据中进行推理的,它是度量不确定性的重要手段。在人工智能中,研究者结合概率和逻辑各自的优点,进行概率逻辑的研究。本文介绍了传统概率逻辑的三大派别,阐述了二值逻辑概率和三值逻辑概率的发展;最后介绍了泛逻辑,通过对概率逻辑和泛逻辑学的研究,将概率逻辑纳入泛逻辑学的框架内。     

概率逻辑的研究

在人工智能科学中,不确定推理扮演着一个非常重要的角色,而其表示方法也很多,文中要讨论的概率逻辑便是其中之一,它是以逻辑表示为基础进行概率推理。首先,从知识表示和概率定义两个不同的角度系统地介绍了概率逻辑的产生及其发展,然后总结了它的一些基本概念,在此基础上给出了求得一致可能世界的一种逻辑系统,为概率逻辑的发展起到推动作用。最后将概率逻辑同与之容易混淆的模糊逻辑加以区分,且提出了概率逻辑的价值及其展望。     

Automated generation of contrapuntal musical compositions using probabilistic logic in Derive

In this work, we present a new application developed in Derive 6 to compose counterpoint for a given melody (“cantus firmus”). The result is non-deterministic, so different counterpoints can be generated for a fixed melody, all of them obeying classical rules of counterpoint. In the case where the counterpoint cannot be generated in a first step, backtracking techniques have been implemented in order to improve the likelihood of obtaining a result. The contrapuntal rules are specified in Derive using probabilistic rules of a probabilistic logic, and the result can be generated for both voices (above and below) of first species counterpoint.     

Probability logic and optimization SAT: The PSAT and CPA models

Both probabilistic satisfiability (PSAT) and the check of coherence of probability assessment (CPA) can be considered as probabilistic counterparts of the classical propositional satisfiability problem (SAT). Actually, CPA turns out to be a particular case of PSAT; in this paper, we compare the computational complexity of these two problems for some classes of instances. First, we point out the relations between these probabilistic problems and two well known optimization counterparts of SAT, namely Max SAT and Min SAT. We then prove that Max SAT with unrestricted weights is NP-hard for the class of graph formulas, where Min SAT can be solved in polynomial time. In light of the aforementioned relations, we conclude that PSAT is NP-complete for ideal formulas, where CPA can be solved in linear time.     

人工智能科学中的概率逻辑

人工智能科学，从其诞生之日起便与逻辑学密不可分。本文首先对逻辑学的分类、相互关系以及泛逻辑的概念等进行了讨论，并对人工智能中逻辑学的应用及发展进行了必要的分析。然后讲述了逻辑学与概率论两大理论基础之上的不确定性推理方法——概率逻辑，重点研究了二值概率逻辑与三值概率逻辑。最后阐述了概率逻辑在人工智能科学中的应用以及对它的思考。