The Graphic-Linguistic Distinction Exploring Alternatives

What properties, if any, distinguish graphical representations fromlinguistic representations? This paper looks for answers in the literatureof philosophy, logic, artificial intelligence, and cognitive psychology, andextracts seven alternative binary classifications of representations thatmay characterize the graphic-linguistic boundary. We assess eachalternative by two standards: (a) whether it extensionally fits the graphic-linguistic distinction, and (b) how far it explains the properties commonly attributed to graphic representations but not to linguistic ones.     

Theories of Diagrammatic Reasoning: Distinguishing Component Problems

Theories of diagrams and diagrammatic reasoning typically seek to account for either the formal semantics of diagrams, or for the advantages which diagrammatic representations hold for the reasoner over other forms of representation. Regrettably, almost no theory exists which accounts for both of these issues together, nor how they affect one another. We do not attempt to provide such an account here. We do, however, seek to lay out larger context than is generally used for examining the processes of using diagrams in reasoning or communication. A context in which detailed studies of sub-problems, such as the formal semantics or cognitive impact of specific diagrammatic systems, may be embedded.Accounts of the embedding of sentential logics in the computational processes of reasoners and communicators are relatively well developed from several decades of research in AI. Analogies between the sentential and the graphical cases are quite revealing about both similarities and differences. To provide a structure for the 'grand context' of diagrammatic representation and reasoning, and to clarify the relations between its component problems, we examine carefully these analogies and the decomposition they provide of subproblems for analysing diagrammatic reasoning.     

Using Diagrams to Understand Geometry

This paper describes ARCHIMEDES-STUDENT, a computer program that constructs and modifies its own representations of diagrams from instructions supplied by a human who is demonstrating a theorem of geometry. The program's representation permits it to make inferences from its constructions and to find a justification for the conclusion of the theorem. It is argued that the sort of perceptual reasoning displayed by this program represents one important aspect of understanding because it relates the abstract mathematical theorem to knowledge of spatial relations. For humans this approach grounds abstraction in experience and thus provides a more compelling demonstration than a formal proof. Because ARCHIMEDES-STUDENT is a well-defined computer program, it provides a precise suggestion of how this aspect of understanding can be achieved.     

On Automating Diagrammatic Proofs of Arithmetic Arguments

Theorems in automated theorem proving are usually proved by formal logical proofs. However, there is a subset of problems which humans can prove by the use of geometric operations on diagrams, so called diagrammatic proofs. Insight is often more clearly perceived in these proofs than in the corresponding algebraic proofs; they capture an intuitive notion of truthfulness that humans find easy to see and understand. We are investigating and automating such diagrammatic reasoning about mathematical theorems. Concrete, rather than general diagrams are used to prove particular concrete instances of the universally quantified theorem. The diagrammatic proof is captured by the use of geometric operations on the diagram. These operations are the inference steps of the proof. An abstracted schematic proof of the universally quantified theorem is induced from these proof instances. The constructive -rule provides the mathematical basis for this step from schematic proofs to theoremhood. In this way we avoid the difficulty of treating a general case in a diagram. One method of confirming that the abstraction of the schematic proof from the proof instances is sound is proving the correctness of schematic proofs in the meta-theory of diagrams. These ideas have been implemented in the system, called Diamond, which is presented here.     

基于多层推理机制的机械产品概念设计

将类比推理的方法与基于实例的方法相结合，建立了基于多层推理的机械产品概念设计系统、知识的表示，采用面向应用的规则方法，框架式的知识结构。该系统有自学习的能力，也可以由用户进行知识的更改和完善，用户界面良好。     

Unnatural Language Processing

Computer-based logic proofs are a form of unnatural language in which the process and structure of proof generation can be observed in considerable detail. We have been studying how students respond to multimodal logic teaching, and performance measures have already indicated that students' pre-existing cognitive styles have a significant impact on teaching outcome. Furthermore, a large corpus of proofs has been gathered via automatic logging of proof development. This paper applies a series of techniques, including corpus statistical methods, to the proof logs. The results indicate that students' cognitive styles influence the structure of their logical discourse, via their differing methods of handling abstract information in diagrams, and transferring information between modalities.     

类比转换原理及其实现

类比转换完成将已知情况（称基）的知识引入到相似新情况（称靶），从而求解靶或学习到关于靶的新知识．本文的类比转换原理讨论了如何选择最佳映射，怎样在靶中创建对象和谓词，以及转换基中那些命题到靶．并基于该原理设计了类比转换的计算模型，实现了类比转换器ＡＴＥ．实例分析表明ＡＴＥ生成的类比结论既具创造性，又有较高可信度．     

类比推理协处理器中的流水线技术

流水线是提高当代处理器性能的最重要技术，转移指令的处理策略直接影响流水线效率。文中讨论了类比推理协处理体系结构中采用的流流水线 及转移指令处理策略，它是使该系统处理类比推理问题的速度比通用处理器快一个数量级以上的重要因素之一。     

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.     

A Qualitative Approach to Syllogistic Reasoning

In this paper we present a new approach to a symbolic treatment of quantified statements having the following form Q A's are B's, knowing that A and B are labels denoting sets, and Q is a linguistic quantifier interpreted as a proportion evaluated in a qualitative way. Our model can be viewed as a symbolic generalization of statistical conditional probability notions as well as a symbolic generalization of the classical probabilistic operators. Our approach is founded on a symbolic finite M-valued logic in which the graduation scale of M symbolic quantifiers is translated in terms of truth degrees. Moreover, we propose symbolic inference rules allowing us to manage quantified statements.     

Comparing Alternatives in the law*

This paper argues the thesis that a particular style of reasoning, qualitative comparative reasoning (QCR), plays a role in at least three areas of legal reasoning that are central in AI and law research, namely legal theory construction, case-based reasoning in the form of case comparison, and legal proof. The paper gives an informal exposition of one particular way to deal with QCR, based on the author’s previous work on reason-based logic (RBL). Then it contains a substantially adapted formalisation of RBL, to make RBL suitable for dealing with QCR. The paper concludes with a brief discussion of related work. *This paper is based on the chapters 3 and 4 of Hage 2005.     

Case-Based Learning: Predictive Features in Indexing

Interest in psychological experimentation from the Artificial Intelligence community often takes the form of rigorous post-hoc evaluation of completed computer models. Through an example of our own collaborative research, we advocate a different view of how psychology and AI may be mutually relevant, and propose an integrated approach to the study of learning in humans and machines. We begin with the problem of learning appropriate indices for storing and retrieving information from memory. From a planning task perspective, the most useful indices may be those that predict potential problems and access relevant plans in memory, improving the planner's ability to predict and avoid planning failures. This predictive features hypothesis is then supported as a psychological claim, with results showing that such features offer an advantage in terms of the selectivity of reminding because they more distinctively characterize planning situations where differing plans are appropriate.We present a specific case-based model of plan execution, RUNNER, along with its indices for recognizing when to select particular plans—appropriateness conditions—and how these predictive indices serve to enhance learning. We then discuss how this predictive features claim as implemented in the RUNNER model is then tested in a second set of psychological studies. The results show that learning appropriateness conditions results in greater success in recognizing when a past plan is in fact relevant in current processing, and produces more reliable recall of the related information. This form of collaboration has resulted in a unique integration of computational and empirical efforts to create a model of case-based learning.     

基于广义相关性的泛类比度和泛逻辑组合规则

传统的关于事物间类比关系的研究过于简单化,且缺乏目的性.首先对事物间的类比关系进行了详细分析,既考虑了类比事物间的公共属性,又考虑了它们的冗余属性和冲突属性,并用广义相关性对其进行了解释.然后定义了不同决策原则下的泛类比度,使类比度定义符合不同的决策要求.最后给出了类比推理中的泛逻辑组合运算规则,从而弥补了传统类比推理研究中运算规则固定不变的缺陷.     

对象的类比推理研究

本文首先提出对象与对象抽象、规则序与规则映射的理论，为对象的类比推理奠定了一定的基础，在此基础上，建造了对象类比推理模型ＯＡＭ．     

一种类比匹配原理及其实现

本文给出了一种类比匹配原理及其计算模型，并在此基础上实现了类比匹配器ＡＭＥ．其中类比匹配原理包括一致性、谓词相似和支持性原则；计算模型采用以命题为中心，以命题映射为基本单位，从而ＡＭＥ的复杂性低于同类系统．另外，证明了本文的实现算法具完备性和正确性．     

Learning to Think and Communicate with Diagrams: 14 Questions to Consider

This paper looks at the particular role which diagrammatic representations, and external representations more generally, play within an educational context. In particular, it considers the way in which the demands on diagrammatic representational systems in educational settings differ with respect to other settings (e.g. professional): in some instances, these demands are increased, while in others, the demands are markedly different.The paper considers three key issues: the question of whether diagrams make certain tasks easier (and whether this is desirable from an educational point of view), the generalisation and transfer of diagrammatic skills once learnt, and the possible problems associated with simultaneously learning domain knowledge and a novel representational system.The paper then considers a number of sub-issues, and concludes by highlighting areas of particular interest for future AI research.     

SIMILARITY‐BASED RETRIEVAL WITH STRUCTURE‐SENSITIVE SPARSE BINARY DISTRIBUTED REPRESENTATIONS

We present an approach to similarity‐based retrieval from knowledge bases that takes into account both the structure and semantics of knowledge base fragments. Those fragments, or analogues, are represented as sparse binary vectors that allow a computationally efficient estimation of structural and semantic similarity by the vector dot product. We present the representation scheme and experimental results for the knowledge base that was previously used for testing of leading analogical retrieval models MAC/FAC and ARCS. The experiments show that the proposed single‐stage approach provides results compatible with or better than the results of two‐stage models MAC/FAC and ARCS in terms of recall and precision. We argue that the proposed representation scheme is useful for large‐scale knowledge bases and free‐structured database applications.     

Cognitive Factors in Programming with Diagrams

Visual programming languages aim to broaden the use ofdiagrams within the software industry, to the extentthat they are integrated into the programming languageitself. As a result, they provide an ideal opportunityto study the benefits of diagrams as an externalrepresentation during problem solving: not only isprogramming a challenging problem-solving activity,but the effect of diagram usage can be directlyassessed by comparing performance while using a visualprogramming language to performance with a standardtextual language. There have been severalmisconceptions amongst visual language researchersregarding the role of diagrams in software design, butthese are being addressed by empirical studies and bynew theories of notation design derived from studiesof visual programming. Based on this research, theauthors are able to recommend several new directionsfor research into thinking with diagrams.     

Computerized dynamic testing of children's potential for reasoning by analogy: The role of executive functioning

This study aimed to investigate children's potential for reasoning by analogy utilizing a newly-developed computerized dynamic test, and the potential differential influence of executive functions (cognitive flexibility, attention, and planning) on static and dynamic measures of analogical reasoning. Participants included 64 children (mean age = 7.55). The study employed a two-session experimental test-training-test design. Based on randomized blocking, half of the children received a graduated prompts training between pre-test and post-test, and the other half did not. Trained children improved more than control children in both their accuracy scores and number of accurately applied transformations from pre-test to post-test. It was further found that cognitive flexibility, attention and planning, is associated with successful solving of analogies. Training children in analogical reasoning seemed to reduce the effect of executive functions. It was also found that that children who were more cognitively flexible needed more prompts during the training.