Reasoning situated in time I: basic concepts*

Abstract

The needs of a real-time reasoner situated in an environment may make it appropriate to view error-correction and non-monotonicity as much the same thing. This has led us to formulate situated (or step) logic, an approach to reasoning in which the formalism has a kind of real-time self-reference that affects the course of deduction itself. Here we seek to motivate this as a useful vehicle for exploring certain issues in commonsense reasoning. In particular, a chief drawback of more traditional logics is avoided: from a contradiction we do not have all wffs swamping the (growing) conclusion set. Rather, we seek potentially inconsistent, but nevertheless useful, logics where the real-time self-referential feature allows a direct contradiction to be spotted and corrective action taken, as part of the same system of reasoning. Some specific inference mechanisms for real-time default reasoning are suggested, notably a form of introspection relevant to default reasoning. Special treatment of ‘now’ and of contradictions are the main technical devices here. We illustrate this with a computer-implemented real time solution to R. Moore's Brother Problem.     

时空推理研究进展

与时态和空间有关的推理问题是人工智能研究中重要的组成部分,在地理信息系统、时空数据库、CAD/CAM等领域有着重要应用.从本体、表示模型和推理方法3个方面分别介绍了时态推理和空间推理的发展,并在此基础上综述了时空结合推理的研究进展.讨论了目前时空推理领域存在的问题,并指出了今后的发展方向.     

A logical approach to representing and reasoning about space

The need for a formal language in which to express and reason about spatial concepts is of crucial importance in many areas of AI and visual systems. For the last five years, spatial reasoning research by the Qualitative Spatial Reasoning Group, University of Leeds, has centred on the development and application of such a language — the RCC spatial logic. Below, we briefly describe the work of the group in this area.     

自动推理技术发展的回顾与展望

介绍了国内外自动推理技术研究的历史,给出了自动推理的分类方法,阐述了各种自动推理技术的逻辑基础和基本思想,对各种推理模型的优缺点进行了系统的比较,并探讨了自动推理技术的发展趋势。     

Modality and interrupts

We suggest that modal operators, in addition to their well-understood semantic role in declarative systems, also mark points at which these systems can be interrupted. We use this idea to describe an interruptible declarative system that gradually refines its responses to queries. Although initial responses may be in error, a correct answer will be provided if arbitrarily large computational resources are available. The ideas presented generalize existing work on stratification of logic programs and the treatment of floundered subgoals.     

Multi-modal diagnosis combining case-based and model-based reasoning: a formal and experimental analysis

Integrating different reasoning modes in the construction of an intelligent system is one of the most interesting and challenging aspects of modern AI. Exploiting the complementarity and the synergy of different approaches is one of the main motivations that led several researchers to investigate the possibilities of building multi-modal reasoning systems, where different reasoning modalities and different knowledge representation formalisms are integrated and combined. Case-Based Reasoning (CBR) is often considered a fundamental modality in several multi-modal reasoning systems; CBR integration has been shown very useful and practical in several domains and tasks. The right way of devising a CBR integration is however very complex and a principled way of combining different modalities is needed to gain the maximum effectiveness and efficiency for a particular task. In this paper we present results (both theoretical and experimental) concerning architectures integrating CBR and Model-Based Reasoning (MBR) in the context of diagnostic problem solving. We first show that both the MBR and CBR approaches to diagnosis may suffer from computational intractability, and therefore a careful combination of the two approaches may be useful to reduce the computational cost in the average case. The most important contribution of the paper is the analysis of the different facets that may influence the entire performance of a multi-modal reasoning system, namely computational complexity, system competence in problem solving and the quality of the sets of produced solutions. We show that an opportunistic and flexible architecture able to estimate the right cooperation among modalities can exhibit a satisfactory behavior with respect to every performance aspect. An analysis of different ways of integrating CBR is performed both at the experimental and at the analytical level. On the analytical side, a cost model and a competence model able to analyze a multi-modal architecture through the analysis of its individual components are introduced and discussed. On the experimental side, a very detailed set of experiments has been carried out, showing that a flexible and opportunistic integration can provide significant advantages in the use of a multi-modal architecture.     

定性空间推理的分层递阶框架

定性空间推理是定性推理和空间推理的重要组成部分 .拓扑和形状是定性空间推理研究的关键问题 .针对定性空间推理已有一般框架存在的问题 ,提出了定性空间推理的分层递阶框架 ,并结合拓扑和形状方面的定性空间推理研究工作阐述了所提出的框架的有效性和合理性 .最后总结了分层递阶框架的要点并提出了基于该框架的进一步研究工作 .     

A hybrid system applied to epidemic screening

Although many knowledge-based systems (KBSs) focus on single-paradigm approaches to encoding knowledge (such as production rules), human experts rarely use a single type of knowledge to solve a real-world problem. A human expert usually combines a number of reasoning mechanisms. In recent years, rule-based reasoning (RBR), case-based reasoning (CBR) and model-based reasoning (MBR) have emerged as important and complementary reasoning methodologies in the intelligent systems area. For complex problem solving, it is useful to integrate RBR, CBR and MBR. In this paper, a hybrid epidemic screening KBS which integrates a deductive RBR system, an inductive CBR system and a quantitative MBR system is proposed. The system has been tested using real epidemic screening variables and data.     

YARM:基于MapReduce的高效可扩展的语义推理引擎

随着语义网的快速发展,RDF语义数据大量涌现.大规模RDF语义数据推理的一个主要问题是计算量大、完成计算需要消耗很长的时间.显然,传统的单机语义推理引擎难以处理大规模的语义数据.另一方面,现有的基于MapReduce的大规模语义推理引擎,缺乏对算法在分布和并行计算环境下执行效率的优化,使得推理时间仍然较长.此外,现有的推理引擎大多存在可扩展性方面的不足,难以适应大规模语义数据的增长需求.针对现有的语义推理系统在执行效率和可扩展性方面的不足,文中提出了一种基于MapReduce的并行化语义推理算法和引擎YARM.为了实现分布和并行计算环境下的高效推理,YARM做出了以下4点优化:(1)采用合理的数据划分模型和并行化算法,降低计算节点间的通信开销;(2)优化推理规则的执行次序,提升了推理计算速度;(3)设计了简洁的去重策略,避免新增作业处理重复数据;(4)设计实现了一种新的基于MapReduce的并行化推理算法.实验结果表明,在真实数据集和大规模合成数据集上,YARM的执行速度比当前最新的基于MapReduce的推理引擎快10倍左右,同时YARM还表现出更好的数据和系统可扩展性.     

A hybrid knowledge-based system applied to epidemic screening

Although many knowledge-based systems (KBSs) focus on single-paradigm approaches to encoding knowledge (such as production rules), experts rarely use a single type of knowledge in solving a problem. More often, an expert will apply a number of reasoning mechanisms. In recent years, rule-based reasoning (RBR), case-based reasoning (CBR) and model-based reasoning (MBR) have emerged as important and complementary reasoning methodologies in artificial intelligence. For complex problem solving, it is useful to integrate RBR, CBR and MBR. In this paper, a hybrid KBS which integrates a deductive RBR system, an inductive CBR system and a quantitative MBR system is proposed for epidemic screening. The system has been tested using real data, and results are encouraging.     

The IKBALS project: Multi-modal reasoning in legal knowledge based systems

In attempting to build intelligent litigation support tools, we have moved beyond first generation, production rule legal expert systems. Our work integrates rule based and case based reasoning with intelligent information retrieval.When using the case based reasoning methodology, or in our case the specialisation of case based retrieval, we need to be aware of how to retrieve relevant experience. Our research, in the legal domain, specifies an approach to the retrieval problem which relies heavily on an extended object oriented/rule based system architecture that is supplemented with causal background information. We use a distributed agent architecture to help support the reasoning process of lawyers.Our approach to integrating rule based reasoning, case based reasoning and case based retrieval is contrasted to the CABARET and PROLEXS architectures which rely on a centralised blackboard architecture. We discuss in detail how our various cooperating agents interact, and provide examples of the system at work. The IKBALS system uses a specialised induction algorithm to induce rules from cases. These rules are then used as indices during the case based retrieval process.Because we aim to build legal support tools which can be modified to suit various domains rather than single purpose legal expert systems, we focus on principles behind developing legal knowledge based systems. The original domain chosen was theAccident Compensation Act 1989 (Victoria, Australia), which relates to the provision of benefits for employees injured at work. For various reasons, which are indicated in the paper, we changed our domain to that ofCredit Act 1984 (Victoria, Australia). This Act regulates the provision of loans by financial institutions.The rule based part of our system which provides advice on the Credit Act has been commercially developed in conjunction with a legal firm. We indicate how this work has lead to the development of a methodology for constructing rule based legal knowledge based systems. We explain the process of integrating this existing commercial rule based system with the case base reasoning and retrieval architecture.     

Strategies in Human Nonmonotonic Reasoning

Although humans seem adept at drawing nonmonotonic conclusions, the nonmonotonic reasoning systems that researchers develop are complex and do not function with such ease. This paper explores people's reasoning processes in nonmonotonic problems. To avoid the problem of people's conclusions being based on knowledge rather than on some reasoning process, we developed a scenario about life on another planet. Problems were chosen to allow the systematic study of people's understanding of strict and nonstrict rules and their interactions. We found that people had great difficulty reasoning and we identified a number of negative factors influencing their reasoning. We also identified three positive factors which, if used consistently, would yield rational and coherent reasoning—but no subject achieved total consistency. (Another possible positive factor, specificity, was considered but we found no evidence for its use.) It is concluded that nonmonotonic reasoning is hard. When people need to reason in a domain where they have no preconceived ideas, the foundation for their reasoning is neither coherent nor rational. They do not use a nonmonotonic reasoning system that would work regardless of content. Thus, nonmonotonic reasoning systems that researchers develop are expected to do more reasoning than humans actually do!     

Modeling default reasoning using defaults

User modeling research can benefit from formal automated reasoning tools. However existing formal tools may need to be modified to suit the needs of user modeling. Theorist is a simple framework for default reasoning. It can be used as a tool for building and maintaining a user model, and as a model of a user's default reasoning. To apply Theorist to both tasks, we develop Nested Theorist (NT), a simple tool based on Theorist that allows default reasoning on arbitrarily-many levels. We extend NT in two ways: we allow prioritized defaults, and we allow reasoning about agents with limited reasoning capabilities. This paper focusses on applications, and uses wide-ranging examples from user-modeling literature to illustrate the usefulness of the tools presented.     

On nonmonotonic reasoning with the method of sweeping presumptions

Reasoning almost always occurs in the face of incomplete information. Such reasoning is nonmonotonic in the sense that conclusions drawn may later be withdrawn when additional information is obtained. There is an active literature on the problem of modeling such nonmonotonic reasoning, yet no category of method-let alone a single method-has been broadly accepted as the right approach. This paper introduces a new method, called sweeping presumptions, for modeling nonmonotonic reasoning. The main goal of the paper is to provide an example-driven, nontechnical introduction to the method of sweeping presumptions, and thereby to make it plausible that sweeping presumptions can usefully be applied to the problems of nonmonotonic reasoning. The paper discusses a representative sample of examples that have appeared in the literature on nonmonotonic reasoning, and discusses them from the point of view of sweeping presumptions.     

基于若干直觉模糊关系的近似推理方法

通过定义一种直觉模糊加法算子,修正Ra的直觉化,研究直觉模糊三角模及其剩余蕴涵算子。将Mizumoto定义的系列模糊关系自然地扩展为直觉模糊关系,通过算例,从8个方面量化比较研究了9种基于不同直觉模糊关系的推理性能,对于直觉模糊假言推理和直觉模糊拒取式推理,Rs, Rg和Rgg都是性能比较好的直觉模糊关系,Rsg, Rm, Ra和Rgs次之,Rc, Rss性能最差。     

A multi-viewpoint system to support abductive reasoning

The paper proposes a multi-viewpoint system to support human abductive reasoning for diagnosis, prognosis and trial-and-error activities for supervising automated systems. This multi-viewpoint approach interprets the same set of events from the different viewpoints in this set. The algorithms for managing these viewpoints and the set of events are related to hypothetical reasoning, and they use several main functions to (1) select or reject certain events, (2) cancel or recover these events, and (3) manage the consistency of the viewpoints. This approach is applied to diagnosis and trial-and-error activities related to the phone troubleshooting problem.     

MARS: A mergers and acquisitions reasoning system

Mergers and acquisitions (M&A) are currently revolutionizing the structure of corporate U.S.A. and annually involve deals totalling billions of dollars. Consequently, it is an area of intense activity and interest within the financial community. The process of planning an M&A is enormously complex and involves sophisticated reasoning and planning, by several parties such as the raider, the target company, investment banks, etc. Computer based tools are often invaluable for planning several stages of an M&A, such as generating forecasted cash flows. Current computer aids for M&A however do not provide adequate support for many essential features such as real time planning, reasoning under uncertainty, nonmonotonic inference, case-based reasoning, etc. MARS is a prototype M&A reasoning tool developed at General Electric Corporate R&D that attempts to provide such features in an integrated environment. MARS both simulates and provides advice regarding the complex reasoning and planning involved in an M&A deal. In doing so, it provides an excellent test bed architecture for the testing, development and integration of several ideas from artificial intelligence. MARS is implemented in COMMON LISP using RUM [15] on top of KEE [18]. RUM, a development environment for reasoning under uncertainty is based on Bonissone's theory of plausible reasoning [2–4] and was also developed at General Electric Corporate R&D.     

基于定性模型的定性仿真方法

定性仿真在定性推理中起着核心作用．本文将介绍基于定性模型的定性仿真法的基本内容，讨论支持其走向实用的主要方法和技术，并展示其在若干领域中的应用．     

The epistemic basis of defeasible reasoning

This article argues that: (i) Defeasible reasoning is the use of distinctive procedures for belief revision when new evidence or new authoritative judgment is interpolated into a system of beliefs about an application domain. (ii) These procedures can be explicated and implemented using standard higher-order logic combined with epistemic assumptions about the system of beliefs. The procedures mentioned in (i) depend on the explication in (ii), which is largely described in terms of a Prolog program, EVID, which implements a system for interactive, defeasible reasoning when combined with an application knowledge base. It is shown that defeasible reasoning depends on a meta-level Closed World Assumption applied to the relationship between supporting evidence and a defeasible conclusion based on this evidence. Thesis (i) is then further defended by showing that the EVID explication of defeasible reasoning has sufficient representational power to cover a wide variety of practical applications of defeasible reasoning, especially in the context of decision making.     

On Construction and Evaluation of Analogical Arguments for Persuasive Reasoning

ABSTRACT

Analogical reasoning is a complex process based on a comparison between two pairs of concepts or states of affairs (aka. the source and the target) for characterizing certain features from one to another. Arguments which employ this process to support their claims are called analogical arguments. Our goals are to study the structure and the computation for their defeasibility in light of the argumentation theory. Our proposed assumption-based argumentation with predicate similarity ABA^(p) framework can be seen as an extension of assumption-based argumentation framework (ABA), in which not only assumptions can be used but also similarity of predicates is used to support a claim. ABA ^(p) labels each argument tree with an analogical degree and different ways to aggregate numerical values are studied toward gullible/skeptical characteristics in agent reasoning. The acceptability of analogical arguments is evaluated w.r.t. the semantics of abstract argumentation. Finally, we demonstrate that ABA ^(p) captures the argumentation scheme for argument from analogy and provides an explanation when it is used for persuasion.