Model-based remotely-sensed imagery interpretation

Abstract

We present a model-based remotely-sensed image interpretation expert system embeded in a knowledge-based geographic information system (K. BIS). The KBIS consists of four sub-systems: a pictorial data base system, an image interpretation expert system, a computer-aided planning system and a computer-aided cartographic system. The image interpretation expert system represents ecological knowledge and other expert knowledge by frames. Its reasoning process consists of a forward reasoning based on the Bayes classification of Landsat imagery, a backward reasoning using frame knowledge and reasoning using a spatial consistency model. A forest inventory study was conducted in Shaxian county, in the southern part of China, using this expert system. The results have shown a significant improvement. Building image interpretation expert systems within knowledge-based pictorial systems is very convenient and efficient because there are well-organized data, knowledge and procedures available.     

NEW METHODOLOGY TO FUZZY REASONING FOR RULE-BASED EXPERT SYSTEMS

Abstract

Much knowledge residing in the knowledge base of an expert system involves fuzzy concepts. A powerful expert system must have the capability of fuzzy reasoning. This paper presents a new methodology for dealing with fuzzy reasoning based on the matching function S. The single-input, single-output (SISO) fuzzy reasoning scheme and the multi-input, single-output (MISO) fuzzy reasoning schemes are discussed in detail. The proposed fuzzy reasoning methodology is conceptually clearer than the compositional rule of inference approach. It can provide an useful way for rule-based systems to deal with fuzzy reasoning.     

Reasoning about cardinal directions between extended objects: The NP-hardness result

The cardinal direction calculus (CDC) proposed by Goyal and Egenhofer is a very expressive qualitative calculus for directional information of extended objects. Early work has shown that consistency checking of complete networks of basic CDC constraints is tractable, while reasoning with the CDC in general is NP-hard. This paper shows, however, that if some constraints are unspecified, then consistency checking of incomplete networks of basic CDC constraints is already intractable. This draws a sharp boundary between the tractable and intractable subclasses of the CDC. The result is achieved by a reduction from the well-known 3-SAT problem.     

Automated reasoning on UML conceptual schemas with derived information and queries

ContextIt is critical to ensure the quality of a software system in the initial stages of development, and several approaches have been proposed to ensure that a conceptual schema correctly describes the user’s requirements.ObjectiveThe main goal of this paper is to perform automated reasoning on UML schemas containing arbitrary constraints, derived roles, derived attributes and queries, all of which must be specified by OCL expressions.MethodThe UML/OCL schema is encoded in a first order logic formalisation, and an existing reasoning procedure is used to check whether the schema satisfies a set of desirable properties. Due to the undecidability of reasoning in highly expressive schemas, such as those considered here, we also provide a set of conditions that, if satisfied by the schema, ensure that all properties can be checked in a finite period of time.ResultsThis paper extends our previous work on reasoning on UML conceptual schemas with OCL constraints by considering derived attributes and roles that can participate in the definition of other constraints, queries and derivation rules. Queries formalised in OCL can also be validated to check their satisfiability and to detect possible equivalences between them. We also provide a set of conditions that ensure finite reasoning when they are satisfied by the schema under consideration.ConclusionThis approach improves upon previous work by allowing automated reasoning for more expressive UML/OCL conceptual schemas than those considered so far.     

Planning and Implementing Kerberos for Large Distributed Systems Part 1

ABSTRACT

For maintaining the consistency of database, the recovery algorithms traditionally depend on complete rollback to a consistent checkpoint. The recovery problem from committed malicious transactions can be solved by determining the dependencies between the transactions in window of vulnerability. Since the size of transactional log may grow very large, recovery becomes a complex and time-consuming process. In this paper, we propose an approach which incorporates application specific information to determine transactional dependencies. The approach is applied to column based transaction dependency to obtain better performance. The system is implemented at application layer where SQL queries are generated. In recovery phase, we consider only affected and malicious transactions for rollback and skip the good transactions.     

On the consistency of cardinal direction constraints

We present a formal model for qualitative spatial reasoning with cardinal directions utilizing a co-ordinate system. Then, we study the problem of checking the consistency of a set of cardinal direction constraints. We introduce the first algorithm for this problem, prove its correctness and analyze its computational complexity. Utilizing the above algorithm, we prove that the consistency checking of a set of basic (i.e., non-disjunctive) cardinal direction constraints can be performed in O(n⁵) time. We also show that the consistency checking of a set of unrestricted (i.e., disjunctive and non-disjunctive) cardinal direction constraints is NP-complete. Finally, we briefly discuss an extension to the basic model and outline an algorithm for the consistency checking problem of this extension.     

Version spaces and the consistency problem

A version space is a collection of concepts consistent with a given set of positive and negative examples. Mitchell [Artificial Intelligence 18 (1982) 203-226] proposed representing a version space by its boundary sets: the maximally general (G) and maximally specific consistent concepts (S). For many simple concept classes, the size of G and S is known to grow exponentially in the number of positive and negative examples. This paper argues that previous work on alternative representations of version spaces has disguised the real question underlying version space reasoning. We instead show that tractable reasoning with version spaces turns out to depend on the consistency problem, i.e., determining if there is any concept consistent with a set of positive and negative examples. Indeed, we show that tractable version space reasoning is possible if and only if there is an efficient algorithm for the consistency problem. Our observations give rise to new concept classes for which tractable version space reasoning is now possible, e.g., 1-decision lists, monotone depth two formulas, and halfspaces.     

Spatial reasoning with rectangular cardinal relations

Qualitative spatial representation and reasoning plays a important role in various spatial applications. In this paper we introduce a new formalism, we name RCD calculus, for qualitative spatial reasoning with cardinal direction relations between regions of the plane approximated by rectangles. We believe this calculus leads to an attractive balance between efficiency, simplicity and expressive power, which makes it adequate for spatial applications. We define a constraint algebra and we identify a convex tractable subalgebra allowing efficient reasoning with definite and imprecise knowledge about spatial configurations specified by qualitative constraint networks. For such tractable fragment, we propose several polynomial algorithms based on constraint satisfaction to solve the consistency and minimality problems. Some of them rely on a translation of qualitative networks of the RCD calculus to qualitative networks of the Interval or Rectangle Algebra, and back. We show that the consistency problem for convex networks can also be solved inside the RCD calculus, by applying a suitable adaptation of the path-consistency algorithm. However, path consistency can not be applied to obtain the minimal network, contrary to what happens in the convex fragment of the Rectangle Algebra. Finally, we partially analyze the complexity of the consistency problem when adding non-convex relations, showing that it becomes NP-complete in the cases considered. This analysis may contribute to find a maximal tractable subclass of the RCD calculus and of the Rectangle Algebra, which remains an open problem.     

ASEMIQUAUTATIVE APPROACH TO REASONING IN PROBABILISTIC NETWORKS

Abstract

This paper proposes the use of semiqualitative modeling for reasoning in probabilistic networks. Semiqualitative modeling is a generalization of qualitative modeling that refines the set of intervals in which values may be expressed. The advantage of semiqualitative modeling of probabilistic reasoning over more traditional methods is that a semiqualitative model can cope with incomplete and imprecise information that would prevent a more traditional model from functioning. The semi-qualitative analysis of a well-known example from the literature is presented, and conclusions about the general use of semiqualitative modeling in reasoning under uncertainty is discussed.     

A modular translation from defeasible nets to defeasible logics

Abstract

The sceptical inheritance nets introduced in Horty et al. [Proceedings of AAAI-87 (1987):358-363] are translated into a version of Nute's defeasible logic. Moreover this translation is modular in the sense of Thomason and Horty [Non-Monotonic Reasoning. Springer-Verlag (1989):234]. Apart from the importance of relating two nonmonotonic reasoning formalisms, this result shows that the reasoning mechanisms underlying defeasible logic and defeasible nets are the same. Yet they were invented independently and set in totally different contexts. This is perhaps some evidence that the underlying nonmonotonic reasoning mechanism is mainly correct. We also observe that since defeasible logics can contain both absolute and defeasible rules, they provided a uniform setting for considering nets which contain both strict and defeasible arcs.     

提取全局语义信息的场景图生成算法

目的 场景图能够简洁且结构化地描述图像。现有场景图生成方法重点关注图像的视觉特征,忽视了数据集中丰富的语义信息。同时,受到数据集长尾分布的影响,大多数方法不能很好地对出现概率较小的三元组进行推理,而是趋于得到高频三元组。另外,现有大多数方法都采用相同的网络结构来推理目标和关系类别,不具有针对性。为了解决上述问题,本文提出一种提取全局语义信息的场景图生成算法。方法 网络由语义编码、特征编码、目标推断以及关系推理等4个模块组成。语义编码模块从图像区域描述中提取语义信息并计算全局统计知识,融合得到鲁棒的全局语义信息来辅助不常见三元组的推理。目标编码模块提取图像的视觉特征。目标推断和关系推理模块采用不同的特征融合方法,分别利用门控图神经网络和门控循环单元进行特征学习。在此基础上,在全局统计知识的辅助下进行目标类别和关系类别推理。最后利用解析器构造场景图,进而结构化地描述图像。结果 在公开的视觉基因组数据集上与其他10种方法进行比较,分别实现关系分类、场景图元素分类和场景图生成这3个任务,在限制和不限制每对目标只有一种关系的条件下,平均召回率分别达到了44.2%和55.3%。在可视化实验中,相比性能第2的方法,本文方法增强了不常见关系类别的推理能力,同时改善了目标类别与常见关系的推理能力。结论 本文算法能够提高不常见三元组的推理能力,同时对于常见的三元组也具有较好的推理能力,能够有效地生成场景图。     

Toward a comprehensive treatment of temporal constraints about periodic events

In this article, we propose an Allen‐like approach to deal with different types of temporal constraints about periodic events. We consider the different components of such constraints (thus, unlike Allen, we also take into account quantitative constraints) including frame times, user‐defined periods, qualitative temporal constraints, and numeric quantifiers and the interactions between such components. We propose a specialized high‐level formalism to represent temporal constraints about periodic events; temporal reasoning on the formalism is performed by a path‐consistency algorithm repeatedly applying our operations of inversion, intersection, and composition and by a specialized reasoner about periods and numeric quantification. The high‐level formalism has been designed in such a way that different types of temporal constraints about periodic events can be represented in a compact and (hopefully) user‐friendly way and path‐consistency‐based temporal reasoning on the formalism can be performed in polynomial time. We also prove that our definitions of inversion, intersection, and composition and, thus, of our path‐consistency algorithm, are correct. This article also sketches the general architecture of the temporal manager for periodic events (TeMP⁺), that has been designed on the basis of our approach. As a working example, we show an application of our approach to scheduling in a school. © 2003 Wiley Periodicals, Inc.     

On the Complexity of Computing Winning Strategies for Finite Poset Games

This paper is concerned with the complexity of computing winning strategies for poset games. While it is reasonably clear that such strategies can be computed in PSPACE, we give a simple proof of this fact by a reduction to the game of geography. We also show how to formalize the reasoning about poset games in Skelley’s theory W₁¹\mathbf{W}_{1}^{1} for PSPACE reasoning. We conclude that W₁¹\mathbf{W}_{1}^{1} can use the “strategy stealing argument” to prove that in poset games with a supremum the first player always has a winning strategy.     

基于IFTL的不确定时间推理方法

针对现有时序逻辑对复杂不确定时间信息描述和推理方面的局限性,定义了直觉模糊不确定时间区间与时间间隔,构造了未知时刻的直觉模糊时序逻辑(IFTL)预测模型,提出了基于IFTL的不确定时间推理方法,较好地解决了时间推理精度不高的问题。同时,定义了直觉模糊集间的重叠度,并提出了基于此的知识模型及时间网络的一致性检验方法。最后通过典型实例验证了所提出的时间推理方法的有效性和优越性。     

行为理解的认知推理方法

目的 人类行为理解是机器智能研究中最富有挑战性的领域。其根本问题是语义获取,即从动作推理得到人的行为,需要跨越两者之间的语义鸿沟,为此提出一种人关于日常行为知识与人体动作行为、环境信息之间的建模方法,以及可扩展的开放式结构环境—行为关系模型,基于该模型提出一种新的行为理解的渐进式认知推理方法。方法 首先根据知识,建立多种特征、复合特征和行为之间的关系模型。系统根据当前的输入流,处理得到当前的特征与复合特征集,推理得到当前的可能行为集。该行为集指导处理模块,更新特征集,得到新的行为集。结果 应用本文渐进式连续推理方法,系统可以把人关于日常行为的知识与人体运动、环境变化等传感器数据处理获取到的信息动态绑定,实现知识辅助的行为理解。结论 提出的推理方法能连续处理长时间、同时发生的行为。     

OSCAR: a general theory of rationality

Abstract

The enterprise is the construction of a general theory of rationality, and its implementation in an automated reasoning system named OSCAR. The paper describes a general architecture for rational thought. This includes both theoretical reasoning and practical reasoning, and builds in important interconnections between them. It is urged that a sophisticated reasoner must be an introspective reasoner, capable of monitoring its own reasoning and reasoning about it. An introspective reasoner is built on top of a non-introspective reasoner that represents the system's default reasoning strategies. The introspective reasoner engages in practical reasoning about reasoning in order to override these default strategies. The paper concludes with a discussion of some aspects of the default reasoner, including the manner in which reasoning is interest driven, and the structure of defeasible reasoning.     

顾及降维的多尺度空间关系广义一致性度量

目的 多尺度空间关系一致性评价是多尺度空间数据冲突检测或数据匹配的重要环节,现有方法主要集中于相同或相近比例尺空间关系的相似性计算,对尺度跨度较大、发生维度变化情形的一致性评价考虑较少,且定性的概念距离度量方法难以适用于具有维度差异的多尺度空间数据。针对上述问题,提出一种顾及降维的多尺度空间关系广义一致性度量方法。方法 首先,引入同名对象概念,分析多尺度范畴下同名对象的表现特征。考虑到维度变化对空间关系的影响,结合并扩展已有的空间关系度量方法,分别提出了广义的拓扑关系、方向关系、距离关系相似性度量方法。然后,构建小尺度场景的同名对象邻近关系图,为减少一致性计算代价,依据不同空间关系特点将邻近关系图化简为各类空间关系邻近图。最后,通过依次计算各类空间关系的相似性值和联合相似性值来判断多尺度场景空间关系表达的一致性。结果 通过对1:1万基础地理数据和1:5万派生数据进行空间关系相似性的定量计算分析,并与现有概念距离方法进行比较,验证了本文方法能更精确地度量尺度跨度较大的空间关系一致性。结论 该评价方法具有广泛适用性,可用于辅助地图综合、多尺度空间数据匹配以及多尺度空间数据建库等过程。     

Automated assume-guarantee reasoning for omega-regular systems and specifications

We develop a learning-based automated assume-guarantee (AG) reasoning framework for verifying ω-regular properties of concurrent systems. We study the applicability of non-circular (AG-NC) and circular (AG-C) AG proof rules in the context of systems with infinite behaviors. In particular, we show that AG-NC is incomplete when assumptions are restricted to strictly infinite behaviors, while AG-C remains complete. We present a general formalization, called LAG, of the learning based automated AG paradigm. We show how existing approaches for automated AG reasoning are special instances of LAG. We develop two learning algorithms for a class of systems, called ∞-regular systems, that combine finite and infinite behaviors. We show that for ∞-regular systems, both AG-NC and AG-C are sound and complete. Finally, we show how to instantiate LAG to do automated AG reasoning for ∞-regular, and ω-regular, systems using both AG-NC and AG-C as proof rules.