Branching and pruning: An optimal temporal POCL planner based on constraint programming

A key feature of modern optimal planners such as graphplan and blackbox is their ability to prune large parts of the search space. Previous Partial Order Causal Link (POCL) planners provide an alternative branching scheme but lacking comparable pruning mechanisms do not perform as well. In this paper, a domain-independent formulation of temporal planning based on Constraint Programming is introduced that successfully combines a POCL branching scheme with powerful and sound pruning rules. The key novelty in the formulation is the ability to reason about supports, precedences, and causal links involving actions that are not in the plan. Experiments over a wide range of benchmarks show that the resulting optimal temporal planner is much faster than current ones and is competitive with the best parallel planners in the special case in which actions have all the same duration.¹     

Overcoming intuition: Metacognitive difficulty activates analytic reasoning.

Humans appear to reason using two processing styles: System 1 processes that are quick, intuitive, and effortless and System 2 processes that are slow, analytical, and deliberate that occasionally correct the output of System 1. Four experiments suggest that System 2 processes are activated by metacognitive experiences of difficulty or disfluency during the process of reasoning. Incidental experiences of difficulty or disfluency--receiving information in a degraded font (Experiments 1 and 4), in difficult-to-read lettering (Experiment 2), or while furrowing one's brow (Experiment 3)--reduced the impact of heuristics and defaults in judgment (Experiments 1 and 3), reduced reliance on peripheral cues in persuasion (Experiment 2), and improved syllogistic reasoning (Experiment 4). Metacognitive experiences of difficulty or disfluency appear to serve as an alarm that activates analytic forms of reasoning that assess and sometimes correct the output of more intuitive forms of reasoning. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

主动推送模式下的情报需求预测模型

针对主动推送模式下的情报需求预测问题,提出了基于案例推理的解决方案。设计了进行情报需求预测案例分析的工作流程,建立了案例属性描述模型;应用最近邻法进行案例的相似度评价,并通过信息增益的计算确定每个属性的权值,获得相似案例(集);提出了方案调整和推理策略。     

基于ESTA构建逆向推理专家系统

介绍专家系统工具ESTA和逆向推理机制的基本概念,阐述使用ESTA构建逆向推理专家系统的基本方法。     

基于工作流与Agent技术的选课系统研究

将工作流技术与Agent技术相结合用于选课系统的研究,充分利用工作流灵活性、软件重用和异构软件的集成特点,发挥Agent技术自组织、自学习和协同分配任务的能力.将多Agent工作流管理系统进行实例应用,设计了一个智能选课系统模型,协调系统中不同角色人员的不同活动,尽量避免冲突,使选课系统更加科学化、高效化、智能化和安全化.     

基于多模态推理图神经网络的场景文本视觉问答模型

文本阅读能力差和视觉推理能力不足是现有视觉问答(visual question answering, VQA)模型效果不好的主要原因,针对以上问题,设计了一个基于图神经网络的多模态推理(multi-modal reasoning graph neural network, MRGNN)模型。利用图像中多种形式的信息帮助理解场景文本内容,将场景文本图片分别预处理成视觉对象图和文本图的形式,并且在问题自注意力模块下过滤多余的信息;使用加入注意力的聚合器完善子图之间相互的节点特征,从而融合不同模态之间的信息,更新后的节点利用不同模态的上下文信息为答疑模块提供了更好的功能。在ST-VQA和TextVQA数据集上验证了有效性,实验结果表明,相比较此任务的一些其他模型,MRGNN模型在此任务上有明显的提升。     

基于标签推理和注意力融合的多标签文本分类方法

目前许多多标签文本分类方法主要关注文档表示,而丢失了大量标签相关的语义信息,导致分类效果不理想。针对以上问题,提出一种基于标签推理和注意力融合的分类方法,挖掘文档中与标签相关的特征以及相似标签之间的相关性,学习标签信息进行标签推理,同时采用注意力机制自学习地融合文档表示和标签表示,最终完成多标签分类任务。在AAPD和RCV1-V2数据集上进行实例验证,该方法的F1值分别达到了0.732和0.887,与其他最新方法相比其准确度均有提升,实验结果证明了标签推理和注意力融合策略的有效性。     

Ontological reasoning in the design space exploration of advanced cyber–physical systems

Cyber–physical systems are becoming increasingly complex. In these advanced systems, the different engineering domains involved in the design process become more and more intertwined. Therefore, a traditional (sequential) design process becomes inefficient in finding good design options. Instead, an integrated approach is needed where parameters in multiple different engineering domains can be chosen, evaluated, and optimized to achieve a good overall solution. However, in such an approach, the combined design space becomes vast. As such, methods are needed to mitigate this problem.In this paper, we show a method for systematically capturing and updating domain knowledge in the context of a co-design process involving different engineering domains, i.e. control and embedded. We rely on ontologies to reason about the relationships between parameters in the different domains. This allows us to derive a stepwise design space exploration workflow where this domain knowledge is used to quickly reduce the design space to a subset of likely good candidates. We illustrate our approach by applying it to the design space exploration process for an advanced electric motor control system and its deployment on embedded hardware.     

Why does language matter to artificial intelligence?

Artificial intelligence, conceived either as an attempt to provide models of human cognition or as the development of programs able to perform intelligent tasks, is primarily interested in theuses of language. It should be concerned, therefore, withpragmatics. But its concern with pragmatics should not be restricted to the narrow, traditional conception of pragmatics as the theory of communication (or of the social uses of language). In addition to that, AI should take into account also the mental uses of language (in reasoning, for example) and the existential dimensions of language as a determiner of the world we (and our computers) live in. In this paper, the relevance of these three branches of pragmatics-sociopragmatics, psychopragmatics, and ontopragmatics-for AI are explored.     

Higher-Order and Modal Logic as a Framework for Explanation-Based Generalization

Certain tasks, such as formal program development and theorem proving, fundamentally rely upon the manipulation of higher-order objects such as functions and predicates. Computing tools intended to assist in performing these tasks are at present inadequate in both the amount of knowledge they contain (i.e., the level of support they provide) and in their ability to learn (i.e., their capacity to enhance that support over time). The application of a relevant machine learning technique—explanation-based generalization (EBG)—has thus far been limited to first-order problem representations. We extend EBG to generalize higher-order values, thereby enabling its application to higher-order problem encodings.Logic programming provides a uniform framework in which all aspects of explanation-based generalization and learning may be defined and carried out. First-order Horn logics (e.g., Prolog) are not, however, well suited to higher-order applications. Instead, we employ Prolog, a higher-order logic programming language, as our basic framework for realizing higher-order EBG. In order to capture the distinction between domain theory and training instance upon which EBG relies, we extend Prolog with the necessity operator of modal logic. We develop a meta-interpreter realizing EBG for the extended language, Prolog, and provide examples of higher-order EBG.