Reasoning support for flexible task resourcing

In many settings, fully automated reasoning about tasks and resources is crucial. This is particularly important in multi-agent systems where tasks are monitored, managed and performed by intelligent agents. For these agents, it is critical to autonomously reason about the types of resources a task may require. However, determining appropriate resource types requires extensive expertise and domain knowledge. In this paper, we propose a means to automate the selection of resource types that are required to fulfil tasks. Our approach combines ontological reasoning and Logic Programming in a novel way for flexible matchmaking of resources to tasks. Using the proposed approach, intelligent agents can autonomously reason about the resources and tasks in various real-life settings and we demonstrate this here through case-studies. Our evaluation shows that the proposed approach equips intelligent agents with flexible reasoning support for task resourcing.     

Visual analogy: Viewing analogical retrieval and mapping as constraint satisfaction problems

The core issue of analogical reasoning is the transfer of relational knowledge from a source case to a target problem. Visual analogical reasoning pertains to problems containing only visual knowledge. Holyoak and Thagard proposed that the retrieval and mapping tasks of analogy in general can be productively viewed as constraint satisfaction problems, and provided connectionist implementations of their proposal. In this paper, we reexamine the retrieval and mapping tasks of analogy in the context of diagrammatic cases, representing the spatial structure of source and target diagrams as semantic networks in which the nodes represent spatial elements and the links represent spatial relations. We use a method of constraint satisfaction with backtracking for the retrieval and mapping tasks, with subgraph isomorphism over a particular domain language as the similarity measure. Results in the domain of 2D line drawings suggest that at least for this domain the above method is quite promising.     

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.     

几何因果定性推理的基本原理和算法

因果定性推理是一种通过分析描述物理系统行为和关系的约束找出系统内部各个成分之间的因果结构的推理方法．本文提出一种基于约束和变量分析的因果定性分析模型和算法．该方法在产品设计中有广泛的应用，利用这个模型和算法可较好地解决参数化设计中的几何推理问题，还可用作概念设计的工具，用于完成复杂系统设计任务的划分及定序、设计变量之间相互依赖关系分析等工作．算法具有应用性强、效率和稳定性好、支持欠约束和多解问题等优点.     

Model-based human-centered task automation: a case study in ACC system design

Engineers, business managers, and governments are increasingly aware of the importance and difficulty of integrating technology and humans. The presence of technology can enhance human comfort, efficiency, and safety, but the absence of human-factors analysis can lead to uncomfortable, inefficient, and unsafe systems. Systematic human-centered design requires a basic understanding of how humans generate and manage tasks. A very useful model of human behavior generation can be obtained by recognizing the task-specific role of mental models in not only guiding execution of skills but also managing initiation and termination of these skills. By identifying the human operator's mental models and using them as templates for automating different tasks, we experimentally support the hypothesis that natural and safe interaction between human operator and automation is facilitated by this model-based human-centered approach. The design of adaptive cruise control (ACC) systems is used as a case study in the design of model-based task automation systems. Such designs include identifying ecologically appropriate perceptual states, identifying perceptual triggering events for managing transitions between skilled behaviors, and coordinating the actions of automation and operator.     

综合推理与智能书法创作

In this paper, we address the demanding task of developing intelligent systems equipped with machine creativity that can perform design tasks automatically. The main challenge is how to model human beings' creativity mathematically and mimic such creativity computationally. We propose a ``synthesis reasoning model" as the underlying mechanism to simulate human beings' creative thinking when they are handling design tasks. We present the theory of the synthesis reasoning model, and the detailed procedure of designing an intelligent system based on the model. We offer a case study of an intelligent Chinese calligraphy generation system which we have developed. Based on implementation experiences of the calligraphy generation system as well as a few other systems for solving real-world problems, we suggest a generic methodology for constructing intelligent systems using the synthesis reasoning model.     

基于整数规划的几何因果定性推理建模和求解

因果定性推理是一种通过分析，描述物理系统行为和关系的约束，找出系统内部各个成分之间的因果结构的推理方法，本文提出了一种基于整数 几何因果定性分析模型和算法，该方法在产品设计中有广泛的应用，利用这个模型和算法可以较好地解决参数化设计中的几何推理问题，还可以用作概念设计的工具，用于完成复杂系统设计任务的划分以及定序、设计变量之间相互依赖关系分析等工作，算法具有约束处理能力强、应用范围广、求解效率和稳定     

多核环境下基于图模型的实时规则调度方法

安全攸关反应式系统的核心要求是：必须在指定时间期限内完成对外部事件的检测和目标事件的响应,否则会产生灾难性的后果.随着安全攸关反应式系统对智能化需求的日益增加,将规则推理应用于这类系统成为必然趋势.规则调度是保证规则推理硬实时约束的关键.为此,提出了一种基于图模型的实时规则调度方法（graph-based real-time rule scheduling,简称GBRRS）.该方法对基于事件图的实时规则推理过程进行建模,提出了基于图的端到端推理任务模型,并给出了端到端推理任务的调度算法,保证了规则调度的安全性.采用模拟实验对GBRRS方法进行了验证,实验结果表明,与DM-EDF方法（通过直接映射把规则上的推理操作转成推理任务后,用全局EDF算法对其进行调度的方法）相比,GBRRS方法在规则调度成功率上平均高出13%~15%,且在规则集的平均负载较高时,仍保持着80%以上的调度成功率.     

功能树相似推理过程的可视化方法研究

相似推理能够丰富功能树包含的设计信息,提高获得创新性设计解的可能 性。该文对可视化技术如何更好地辅助相似推理展开了研究。首先,确定了功能树与可视化 模型之间的映射规则;其次,针对相似推理不同阶段提供了相应的可视化人机交互手段,以 实时跟踪相似推理过程;最后,通过系统实例进一步验证了相似推理的有效性以及可视化辅 助下相似推理效果的提高。     

A Concurrent,Distributed Architecture for Diagnostic Reasoning

This paper demonstrates the feasibility of modeling concurrent diagnostic reasoning (CDR) by means of the computational model of actors. Actors have a value added on top of objects, because they include the properties of abstraction, modularity and reuse of objects but allow really concurrent and distributed architectures, in the sense that memory (the environment) is assumed not to be shared among actors. Whether concurrency really implies efficiency is still debated. We are more concerned here with the actor-based design of the diagnostic reasoning model. As a testimony of the feasibility of our proposal, a concrete, actor-based diagnostic program is presented as a module for an Intelligent Tutoring System in the domain of school algebra. CDR is obtained from the coordinated behaviour of actors which possess limited local knowledge and accomplish the global goal of diagnostic reasoning by interacting with each other. We examine how the traditional approaches to student modeling, such as overlay and bug models, can be re-visited in a distributed perspective of computational actors and how the latter view outperforms the previous ones.     

基于模型的用户界面变压器设计?

当前基于模型的用户界面设计不能较好处理界面和语义的隔离问题。为此,提出一种基于模型的用户界面变压器设计方法。将设计过程分为任务和领域、界面设计、抽象用户界面、具体用户界面、最终用户界面5个层次,利用相关的用户界面描述语言进行抽象描述,导出用户界面的抽象模型,经逐层模型映射后,通过客户端将其解析并显示。应用结果表明,该设计方法具有较好的可用性,能达到跨平台的目的。     

KM4: Visual reasoning via Knowledge Embedding Memory Model with Mutual Modulation

Visual reasoning is a special kind of visual question answering, which is essentially multi-step and compositional, and also requires intensive text-visual interaction. The most important and challenging problem of visual reasoning is to design an effective and robust visual reasoning model. To this end, there are two challenges to overcome. The first is that textual and visual information must be jointly considered to make accurate inferences about reasoning. The second is that existing deep learning-based works are often too specific to a particular task. To address these issues, we propose a knowledge memory embedding model with mutual modulation for visual reasoning. This approach learns not only knowledge-based embeddings derived from key–value memory network to make the full and joint of textual and visual information, but also exploits the prior knowledge to improve the performance with knowledge-based representation learning for applying other general reasoning tasks. Experimental results on four benchmarks show that the proposed approach significantly improves performance compared with other state-of-the-art methods, guarantees the robustness with our model. Most importantly, we apply our model to four reasoning tasks, and experimentally show that our model effectively supports relational reasoning and improves performance in several tasks and datasets.     

Integrating Case-Based and Rule-Based Reasoning to Meet Multiple Design Constraints

Although case-based reasoning (CBR) was introduced as an alternative to rule-based reasoning (RBR), there is a growing interest in integrating it with other reasoning paradigms, including RBR. New hybrid approaches are being piloted to achieve new synergies and improve problem-solving capabilities. In our approach to integration, CBR is used to satisfy multiple numeric constraints, and RBR allows the performance of "what if" analysis needed for creative design.
The domain of our investigation is nutritional menu planning. The task of designing nutritious, yet appetizing, menus is one at which human experts consistently outperform computer systems. Tailoring a menu to the needs of an individual requires satisfaction of multiple numeric nutrition constraints plus personal preference goals and aesthetic criteria.
We first constructed and evaluated independent CBR and RBR menu planning systems, then built a hybrid system incorporating the strengths of each system. The hybrid outperforms either single strategy system, designing superior menus, while synergistically providing functionality that neither single strategy system could provide. In this paper, we present our hybrid approach, which has applicability to other design tasks in which both physical constraints and aesthetic criteria must be met.     

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.     

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.     

面向开放文本的逻辑推理知识抽取与事件影响推理探索

开放文本中蕴含着大量的逻辑性知识,以刻画事物之间逻辑传导关系的逻辑类知识库是推动知识推理发展的重要基础,研发大规模逻辑推理知识库有助于支持由实体或事件等传导驱动的决策任务。该文围绕逻辑推理知识库,论述了知识库的概念、类别和基本构成,提出了一种面向大规模开放文本的实体描述、事件因果逻辑知识快速抽取方法;面向金融领域,探索了一套基于逻辑推理知识库的可解释性路径推理方法和金融实体影响生成系统。算法模型和系统均取得了不错的效果。     

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.     

Partial order semantics for use case and task models

Use case models are the specification medium of choice for functional requirements, while task models are employed to capture User Interface (UI) requirements and design information. In current practice, both entities are treated independently and are often developed by different teams, which have their own philosophies and lifecycles. This lack of integration is problematic and often results in inconsistent functional and UI design specifications causing duplication of effort while increasing the maintenance overhead. To address these shortcomings, we propose a formal semantic framework for the integrated development of use case and task models. The semantic mapping is defined in a two step manner from a particular use case or task model notation to the common semantic domain of sets of partially ordered sets. This two-step mapping results in a semantic framework that can be more easily reused and extended. The intermediate semantic domains have been carefully chosen by taking into consideration the intrinsic characteristics of use case and task models. As a concrete example, we provide a semantics for our own DSRG use case formalism and an extended version of ConcurTaskTrees, one of the most popular task model notations. Furthermore, we use the common semantic model to formally define a set of refinement relations for use case and task models.     

一种用矩形代数表示主方向关系的方法

论文重点介绍了基于主方向物体MBR(MinimumBoundRectangle)与矩形代数(Rectanglealgebra)理论相结合对物体空间方向关系进行表述的一个新型模型。通过将物体方向和矩形代数有机结合,利用矩形代数良好的计算性质可以为以后的主方向空间推理以及一致性检验提供更为简便快捷的算法,为GIS和人工智能领域中的方向关系推理提供一个新的思路。