The synthesis of cognitive procedures and the problem of induction

The JSM-method of automated hypotheses generation realizing the automated plausible reasoning in Intelligent Systems is described. It is shown that the method represents specially organized interaction of induction, analogy and abduction. The foundations of exact epistemology for JSM- Intelligent Systems are formulated.     

Spontaneous assimilation of continuous values and temporal information in causal induction.

Existing models of causal induction primarily rely on the contingency between the presence and the absence of a causal candidate and an effect. Yet, classification of observations into these four types of covariation data may not be straightforward because (a) most causal candidates, in real life, are continuous with ambiguous, intermediate values and because (b) effects may unfold after some temporal lag, providing ambiguous contingency information. Although past studies suggested various reasons why ambiguous information may not be used during causal induction, the authors examined whether learners spontaneously use ambiguous information through a process called causal assimilation. In particular, the authors examined whether learners willingly place ambiguous observations into one of the categories relevant to the causal hypothesis, in accordance with their current causal beliefs. In Experiment 1, people's frequency estimates of contingency data reflected that information ambiguous along a continuous quantity dimension was spontaneously categorized and assimilated in a causal induction task. This assimilation process was moderated by the strength of the upheld causal hypothesis (Experiment 2), could alter the overall perception of a causal relationship (Experiment 3), and could occur over temporal sequences (Experiment 4). (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Image segmentation by a contrario simulation

Segmenting an image into homogeneous regions generally involves a decision criterion to establish whether two adjacent regions are similar. Decisions should be adaptive to get robust and accurate segmentation algorithms, avoid hazardous a priori and have a clear interpretation. We propose a decision process based on a contrario reasoning: two regions are meaningfully different if the probability of observing such a difference in pure noise is very low. Since the existing analytical methods are intractable in our case, we extend them to allow a mixed use of analytical computations and Monte-Carlo simulations. The resulting decision criterion is tested experimentally through a simple merging algorithm, which can be used as a post-filtering and validation step for existing segmentation methods.     

A novel sequence representation for unsupervised analysis of human activities

Formalizing computational models for everyday human activities remains an open challenge. Many previous approaches towards this end assume prior knowledge about the structure of activities, using which explicitly defined models are learned in a completely supervised manner. For a majority of everyday environments however, the structure of the in situ activities is generally not known a priori. In this paper we investigate knowledge representations and manipulation techniques that facilitate learning of human activities in a minimally supervised manner. The key contribution of this work is the idea that global structural information of human activities can be encoded using a subset of their local event subsequences, and that this encoding is sufficient for activity-class discovery and classification.In particular, we investigate modeling activity sequences in terms of their constituent subsequences that we call event n-grams. Exploiting this representation, we propose a computational framework to automatically discover the various activity-classes taking place in an environment. We model these activity-classes as maximally similar activity-cliques in a completely connected graph of activities, and describe how to discover them efficiently. Moreover, we propose methods for finding characterizations of these discovered classes from a holistic as well as a by-parts perspective. Using such characterizations, we present a method to classify a new activity to one of the discovered activity-classes, and to automatically detect whether it is anomalous with respect to the general characteristics of its membership class. Our results show the efficacy of our approach in a variety of everyday environments.     

Medical knowledge management for specific hospital departments

Medical knowledge representation and management is concerned with how to organise the often vague clinical experience of medical staff required for computable models. However, few knowledge management and acquisition tools have entered routine use, since such tools are not perceived by physicians as part of the clinical information process. An attempt to partially solve this problem, we identify two key aspects of knowledge representation and management tasks. The first is to adopt a medical knowledge standardisation to provide a consistent terminology control and to simplify the integration between knowledge management tools and the health information system. The second is to establish an effective knowledge acquisition process in specific medical fields by adapting knowledge acquisition tools. Therefore, the main goal of this work is to define computational models and to design mechanisms for the effective acquisition and management of medical knowledge in real-life hospital departments. To this end, we analyse the representation of medical knowledge (based on deep-causal models) and the development of knowledge management tools (based on ontologies), integrated within the information processing activities of the clinical user. Finally, we illustrate its applicability in the Intensive Care Unit and Pediatry scenarios.     

基于案例推理的房地产投资风险评价的研究

房地产项目是高利润、高风险的投资。为更加科学地评价其风险程度以便有效预控，提出将人工智能领域的新方法——案例推理应用到房地产投资风险评价的问题中。通过分析影响房地产投资风险的因素，结合案例推理的基本原理，提出了基于案例推理的房地产投资风险评价指标体系，并初步解决案例的表示、检索匹配以及案例学习等主要技术。     

Representing Epistemic Uncertainty by Means of Dialectical Argumentation

We articulate a dialectical argumentation framework for qualitative representation of epistemic uncertainty in scientific domains. The framework is grounded in specific philosophies of science and theories of rational mutual discourse. We study the formal properties of our framework and provide it with a game theoretic semantics. With this semantics, we examine the relationship between the snaphots of the debate in the framework and the long run position of the debate, and prove a result directly analogous to the standard (Neyman–Pearson) approach to statistical hypothesis testing. We believe this formalism for representating uncertainty has value in domains with only limited knowledge, where experimental evidence is ambiguous or conflicting, or where agreement between different stakeholders on the quantification of uncertainty is difficult to achieve. All three of these conditions are found in assessments of carcinogenic risk for new chemicals.     

建设工程施工合同争议案例知识管理研究

以建设工程施工合同争议案例分解为基础,提出了建设工程施工合同争议案例知识管理模型架构,分析了架构组成.本文将知识元理论和矩阵模型引入到合同争议中,并运用相似度实现案例检索.依据提出的架构及关键技术,承包商企业可实现施工合同争议案例的知识管理,从而提高知识资源的利用率,降低工程成本.     

空间数据智能:概念、技术与挑战

随着空间数据体量的持续增长,空间数据所蕴含的价值巨大.传统的数据感知存储技术和处理分析方法已然不能充分挖掘海量空间数据的价值.因此,空间数据智能,一个专注于海量空间数据的研究与应用的多学科交叉的领域,正扮演着越来越重要的角色.介绍了空间数据智能的概念、空间数据智能领域所面临的技术挑战及空间数据智能的关键技术,同时介绍了...     

基于案例推理的层流冷却自学习方法研究

 采用案例推理技术研究了热轧带钢层流冷却数学模型中的长期自学习系数的确定方法。基于现场大量生产数据,从如何有效利用经验知识入手,对层流冷却工况和所采用的自学习系数进行案例构造,采用绝对过滤和相对过滤方法进行案例检索,根据当前工况和历史案例工况的相似度决定是否进行自学习系数的重用或修正。现场实际应用表明：对已轧过的钢种规格带钢,该方法能有效地避免再次轧制时带钢头部过冷现象,能显著提高带钢头部卷取温度的设定精度,能有效地提高换规格轧制时带钢头部卷取温度的控制精度。