Probabilistic inductive constraint logic

Machine Learning - Probabilistic logical models deal effectively with uncertain relations and entities typical of many real world domains. In the field of probabilistic logic programming usually...     

Extending constraint logic programming for temporal reasoning

In recent years, several constraint‐based temporal reasoning frameworks have been proposed. They consider temporal points or intervals as domain elements linked by temporal constraints. Temporal reasoning in these systems is based on constraint propagation. In this paper, we argue that a language based on constraint propagation can be a suitable tool for expressing and reasoning about temporal problems. We concentrate on Constraint Logic Programming (CLP) which is a powerful programming paradigm combining the advantages of Logic Programming and the efficiency of constraint solving. However, CLP presents some limitations in dealing with temporal reasoning. First, it uses an “arc consistency” propagation algorithm which is embedded in the inference engine, cannot be changed by the user, and is too weak in many temporal frameworks. Second, CLP is not able to deal with qualitative temporal constraints. We present a general meta CLP architecture which maintains the advantages of CLP, but overcomes these two main limitations. Each architectural level is a finite domain constraint solver(CLP(FD)) that reasons about constraints of the underlying level. Based on this conceptual architecture, we extend the CLP(FD)language and we specialize the extension proposed on Vilain and Kautz’sPoint Algebra, on Allen’s Interval Algebra and on the STP framework by Dechter, Meiri and Pearl. In particular, we show that we can cope effectively with disjunctive constraints even in an interval‐based framework. This revised version was published online in June 2006 with corrections to the Cover Date.     

Complexity classification in qualitative temporal constraint reasoning

We study the computational complexity of the qualitative algebra which is a temporal constraint formalism that combines the point algebra, the point-interval algebra and Allen's interval algebra. We identify all tractable fragments and show that every other fragment is NP-complete.     

规则半自动学习的概率软逻辑推理模型

概率软逻辑（PSL）作为一种基于声明式规则的概率模型,具有极强的扩展性和多领域适应性,目前为止,它需要人为给出大量的常识和领域知识作为规则确立的先决条件,这些知识的获取往往非常昂贵并且其中包含的不正确的信息可能会影响推理的正确性。为了缓解这种困境,将C5.0算法和概率软逻辑相结合,让数据和知识共同驱动推理模型,提出了一种规则半自动学习方法。该方法利用C5.0算法提取规则,再辅以人工规则和优化调节后的规则作为改进的概率软逻辑输入。实验结果表明,在学生成绩预测问题上所提方法比C5.0算法和没有规则学习的概率软逻辑具有更高的精度;和纯手工定义规则的方法相比,所提方法能大幅降低人工成本;和贝叶斯网络（BN）、支持向量机（SVM）等算法相比,该方法也表现出不错的效果。     

Probabilistic models considering dependent relation in reasoning for decision-making

This paper proposes a fuzzy dependence-index for construction of the probabilistic models considering dependent relation for solving the reasoning problem. It is important for constructing the joint probability-distribution to consider the dependency of events. We consider that some vagueness is included in the dependency. Because causal relationship of among events is uncertain, it is difficult to express dependency as definite value. In this paper, we classify the dependent relations, and apply the fuzzy probability to calculation of the dependence-index. Then, the fuzzy dependence-index is defined to consider dependency with fuzziness. Using the fuzzy dependence-index, we calculate the joint probability of multi-events for constructing the probabilistic model. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Collective decision making: a great opportunity for constraint reasoning

Collective decision making is an area of increasingly growing interest, mainly due to the rise of many IT-enabled environments where people connect and share information with others. We believe that constraint reasoning can have a major impact in this field, by providing general and flexible frameworks to model agents’ preferences over the alternative decisions, efficient algorithms to compute the best individual and collective decisions, and innovative approaches to deal with missing information. However, in order to do this, we claim that constraint reasoning should increase its efforts to open up to other research areas, such as voting and game theory, multi-agent systems, machine learning, and reasoning under uncertainty.     

Probabilistic reasoning in an augmented fact-proposition space and its applications

A simple and versatile probabilistic reasoning scheme is presented. Based on an augmentation of a multi-dimensional inference space indexed by a Cartesian product of the fact and proposition sets, the scheme simplifies the processes involved in the representation and computation of a probabilistic reasoning system. In the augmented space, a set of auxiliary fields is utilized in addition to the fact-proposition relations to manipulate the uncertainty and incompleteness of the information presented. The scheme enhances the functionality of a probabilistic reasoning and facilitates the building of practical reasoning systems. The utilization of the augmented space in reasoning is illustrated by two problems in computer-vision applications.     

基于蕴涵算子族的模糊推理的FMP三I约束算法

提出了基于蕴涵算子族L-λ-R₀的模糊推理的思想,这将有助于提高推理结果的可靠性。针对蕴涵算子族L-λ-R₀给出了模糊推理的FMP模型的α-三I约束算法、三I约束算法,并说明其现实意义。     

基于蕴涵算子族的模糊推理的FMP三I约束算法

提出了基于蕴涵算子族L-λ-R0的模糊推理的思想,这将有助于提高推理结果的可靠性。针对蕴涵算子族L-λ-R0给出了模糊推理的FMP模型的α-三I约束算法、三I约束算法,并说明其现实意义。     

Probabilistic reasoning with a Bayesian DNA device based on strand displacement

We present a computing model based on the DNA strand displacement technique, which performs Bayesian inference. The model will take single-stranded DNA as input data, that represents the presence or absence of a specific molecular signal (evidence). The program logic encodes the prior probability of a disease and the conditional probability of a signal given the disease affecting a set of different DNA complexes and their ratios. When the input and program molecules interact, they release a different pair of single-stranded DNA species whose ratio represents the application of Bayes’ law: the conditional probability of the disease given the signal. The models presented in this paper can have the potential to enable the application of probabilistic reasoning in genetic diagnosis in vitro.     

Probabilistic temporal reasoning and its application to fossil power plant operation

Many real-world applications, such as industrial diagnosis, require an adequate representation and inference mechanism that combines uncertainty and time. In this work, we propose a novel approach for representing dynamic domains under uncertainty based on a probabilistic framework, called temporal nodes Bayesian networks (TNBN). The TNBN model is an extension of a standard Bayesian network, in which each temporal node represents an event or state change of a variable and the arcs represent causal–temporal relationships between nodes. A temporal node has associated a probability distribution for its time of occurrence, where time is discretized in a finite number of temporal intervals; allowing a different number of intervals for each node and a different duration for the intervals within a node (multiple granularity). The main difference with previous probabilistic temporal models is that the representation is based on state changes at different times instead of state values at different times. Given this model, we can reason about the probability of occurrence of certain events, for diagnosis or prediction, using standard probability propagation techniques developed for Bayesian networks. The proposed approach is applied to fossil power plant diagnosis through two detailed case studies: power load increment and control level system failure. The results show that the proposed formalism could help to improve power plant availability through early diagnosis of events and disturbances.     

Integrating domain and constraint privacy reasoning in the distributed stochastic algorithm with breakouts

Privacy has traditionally been a major motivation of distributed problem solving. One popular approach to enable privacy in distributed environments is to implement complex cryptographic protocols. In this paper, we propose a different, orthogonal approach, which is to control the quality and the quantity of publicized data. We consider the Open Constraint Programming model and focus on algorithms that solve Distributed Constraint Optimization Problems (DCOPs) using a local search approach. Two such popular algorithms exist to find good solutions to DCOP: DSA and GDBA. In this paper, we propose DSAB, a new algorithm that merges ideas from both algorithms to allow extensive handling of constraint privacy. We also study how algorithms behave when solving Utilitarian DCOPs, where utilitarian agents want to reach an agreement while reducing the privacy loss. We experimentally study how the utilitarian approach impacts the quality of the solution and of publicized data.

     

基于蕴涵算子族L-λ-R0的反向三I约束算法

提出基于蕴涵算子族L-λ-R₀的模糊推理的思想,这将有助于提高推理结果的可靠性。针对蕴涵算子族L-λ-R₀给出了模糊推理的FMP模型及FMT模型的反向三I约束算法。     

Probabilistic contracts: a compositional reasoning methodology for the design of systems with stochastic and/or non-deterministic aspects

A contract allows to distinguish hypotheses made on a system (the guarantees) from those made on its environment (the assumptions). In this paper, we focus on models of Assume/Guarantee contracts for (stochastic) systems. We consider contracts capable of capturing reliability and availability properties of such systems. We also show that classical notions of Satisfaction and Refinement can be checked by effective methods thanks to a reduction to classical verification problems. Finally, theorems supporting compositional reasoning and enabling the scalable analysis of complex systems are also studied.     

From case-based reasoning to traces-based reasoning

CBR is an original AI paradigm based on the adaptation of solutions of past problems in order to solve new similar problems. Hence, a case is a problem with its solution and cases are stored in a case library. The reasoning process follows a cycle that facilitates “learning” from new solved cases. This approach can be also viewed as a lazy learning method when applied for task classification. CBR is applied for various tasks as design, planning, diagnosis, information retrieval, etc. The paper is the occasion to go a step further in reusing past unstructured experience, by considering traces of computer use as experience knowledge containers for situation based problem solving.     

Uncertain reasoning about agents' beliefs and reasoning

Reasoning about mental states and processes is important in varioussubareas of the legal domain. A trial lawyer might need to reason andthe beliefs, reasoning and other mental states and processes of membersof a jury; a police officer might need to reason about the conjecturedbeliefs and reasoning of perpetrators; a judge may need to consider adefendant's mental states and processes for the purposes of sentencing;and so on. Further, the mental states in question may themselves beabout the mental states and processes of other people. Therefore, if AIsystems are to assist with reasoning tasks in law, they may need to beable to reason about mental states and processes. Such reasoning isriddled with uncertainty, and this is true in particular in the legaldomain. The article discusses how various different types ofuncertainty arise, and shows how they greatly complicate the task ofreasoning about mental states and processes. The article concentrates onthe special case of states of belief and processes of reasoning, andsketches an implemented, prototype computer program (ATT-Meta) thatcopes with the various types of uncertainty in reasoning about beliefsand reasoning. In particular, the article outlines the system'sfacilities for handling conflict between different lines of argument,especially when these lie within the reasoning of different people. Thesystem's approach is illustrated by application to a real-life muggingexample.     

Introspective reasoning for index refinement in case-based reasoning

Introspective reasoning can enable a reasoner to learn by refining its own reasoning processes. In order to perform this learning, the system must monitor the course of its reasoning to detect learning opportunities and then apply appropriate learning strategies. This article describes lessons learned from research on a computer model of how introspective reasoning can guide failure-driven learning. The computer model monitors its own reasoning by comparing it to a model of the desired behaviour of its reasoning, and learns in response to deviations from the ideal defined by the model. The approach is applied to the problem of determining indices for selecting cases from a case-based planner's memory. Experiments show that learning driven by this introspective reasoning both decreases retrieval effort and improves the quality of plans retrieved, increasing the overall performance of the planning system compared to case learning alone.     

Modelling default and likelihood reasoning as probabilistic reasoning

This paper presents a probabilistic analysis of plausible reasoning about defaults and about likelihood. Likely and by default are in fact treated as duals in the same sense as possibility and necessity. To model these four forms probabilistically, a logicQDP and its quantitative counterpartDP are derived that allow qualitative and corresponding quantitative reasoning. Consistency and consequence results for subsets of the logics are given that require at most a quadratic number of satisfiability tests in the underlying prepositional logic. The quantitative logic shows how to track the propagation error inherent in these reasoning forms. The methodology and sound framework of the system highlights their approximate nature, the dualities, and the need for complementary reasoning about relevance.Much of this research was done while at the University of Technology, Sydney, Broadway, NSW, Australia, and some at the Turing Institute, 36 Nth. Hanover Str., Glasgow, Scotland.     

Model-based reasoning

In this paper, there will be a particular focus on mental models and their application to inductive reasoning within the realm of instruction. A basic assumption of this study is the observation that the construction of mental models and related reasoning is a slowly developing capability of cognitive systems that emerges effectively with proper contextual and social support. More specifically, we first will identify some key elements of the structure and function of mental models in contrast to schemas. Next, these key elements of modeling will be used to generate some conjectures about the foundations of model-based reasoning. In the next section, we will describe the learning-dependent progression of mental models as a suitable approach for understanding the basics of deductive and inductive reasoning based on models as “tools for thought.” The rationale of mental models as tools for reasoning will be supported by empirical research to be described in a particular section of this paper. Finally, we will turn to the instructional implications of model-based reasoning by discussing appropriate instructional methods to affect the construction of mental models for performing deductive and inductive reasoning.     

Circular reasoning

The author discusses two interesting relationships between circles and lines. There should be a computer graphics application that can use the first topic to run faster or better. The second topic is Ptolemy's Theorem, which is a generalization of the triangle inequality. The author shows how it can be used to derive the angle addition formulas. He considers how Ptolemy's Theorem can be used to prove that Snell's Law and Fermat's Principle of Least Time both lead to the same geometry of refraction