多主体系统中对其它主体的研究

多主体系统是当前人工智能研究后一个热点，其中，关于知识和动作的推理是一个重要的课题，文中给出了一种知识表示框架，称为ＲＡＯ逻辑，用来对其它主体研究时表示概念和规则，我们从日常推理中抽象出换位原则的规则（ＰＥＰ），ＰＥＰ是ＲＡＯ是的一条公理模式，并且为主体研究其它主体的一个基本规则，它与知识逻辑中的分离规则和（Ｋ）公理具有相似的形式和作用。     

A Constraint-Based Reasoning Framework for Behavioural Animation

Behaviour is a reflection of a reasoning process that must deal with constraints imposed by an external environment, internal knowledge and physical structure. This paper proposes a framework for behavioural animation that is based on the next generation of object-oriented, constraint-based expert systems technology, and applies a control structure of knowledge agents and knowledge units to determine the behaviour of objects to be animated. Knowledge agents are responsible for planning, plan implementation and information extraction from the environment. The activity of an agent is dependent on the knowledge units ascribed to them by the animator. The interaction between agents and knowledge units is resolved by the reasoning engine, and thus, influences the eventual motion displayed. An example given is NSAIL, a pilot implementation using the model-based ECHIDNA constraint logic programming shell. With this approach, the motion for a sailing scenario and other behavioural domains can be specified at a high level through the characterization of the knowledge agents.     

Dynamic Uncertain Causality Graph for Knowledge Representation and Reasoning: Discrete DAG Cases

Developed from the dynamic causality diagram (DCD) model,a new approach for knowledge representation and reasoning named as dynamic uncertain causality graph (DUCG) is presented,which focuses on the co...     

Merging deductive and inductive reasoning for processing textual descriptions of inter-human conflicts

We report on a novel approach to modeling a dynamic domain with limited knowledge. A domain may include participating agents where we are uncertain about motivations and decision-making principles of some of these agents. Our reasoning setting for such domains includes deductive, inductive, and abductive components. The deductive component is based on situation calculus and describes the behavior of agents with complete information. The machine learning-based inductive and abductive components involve the previous experience with the agents, whose actions are uncertain to the system. Suggested reasoning machinery is applied to the problem of processing customer complaints in the form of textual messages that contain a multiagent conflict. The task is to predict the future actions of an opponent agent to determine the required course of action to resolve a multiagent conflict. This study demonstrates that the hybrid reasoning approach outperforms both stand-alone deductive and inductive components. Suggested methodology reflects the general situation of reasoning in dynamic domains in the conditions of uncertainty, merging analytical (rule-based) and analogy-based reasoning.     

事件演算在行动推理中的应用

事件演算是基于一阶谓词演算的行动推理理论.它可作为描述事件的一个工具,在行动推理的应用中显示出其强大的表示能力和实现能力.在事件演算中,可以对行动进行公理化,可以描述行动的时间性、并发性、连续变化及知识,而且还可用Prolog实现.讨论介绍与这些应用相关的基本概念、思想和方法等,并且通过一个送咖啡的例子说明了如何通过事件演算来描述和实现.     

John McCarthy's legacy

This special issue is dedicated to John McCarthy, founding father of Artificial Intelligence. It contains a collection of recent contributions to the field of knowledge representation and reasoning, a field that McCarthy founded and that has been a main focus of his research during the last half century. In this introductory article, we survey some of McCarthy's major contributions to the field of knowledge representation and reasoning, and situate the papers in this special issue in the context of McCarthy's previous work.     

PERSPECTIVES IN KNOWLEDGE REPRESENTATION

In this paper we discuss recent developments in the research on knowledge representation, focusing on hybrid formalisms, nonmonotonic reasoning, and formalisms for reasoning about knowledge and reasoning in a multiagent scenario.     

Legal Case-based Reasoning as Practical Reasoning

In this paper we apply a general account of practical reasoning to arguing about legal cases. In particular, we provide a reconstruction of the reasoning of the majority and dissenting opinions for a particular well-known case from property law. This is done through the use of Belief-Desire-Intention (BDI) agents to replicate the contrasting views involved in the actual decision. This reconstruction suggests that the reasoning involved can be separated into three distinct levels: factual and normative levels and a level connecting the two, with conclusions at one level forming premises at the next. We begin by summarising our general approach, which uses instantiations of an argumentation scheme to provide presumptive justifications for actions, and critical questions to identify arguments which attack these justifications. These arguments and attacks are organised into argumentation frameworks to identify the status of individual arguments. We then discuss the levels of reasoning that occur in this reconstruction and the properties and significance of each of these levels. We illustrate the different levels with short examples and also include a discussion of the role of precedents within these levels of reasoning.     

基于证据推理的确定因子规则库推理方法

不确定性推理方法是人工智能领域的一个主要研究内容,If-then规则是人工智能领域最常见的知识表示方法. 文章针对实际问题往往具有不确定性的特点,提出基于证据推理的确定因子规则库推理方法.首先在If-then规则的基础上给出确定因子结构和确定因子规则库知识表示方法,该方法可以有效利用各种类型的不确定性信息,充分考虑了前提、结论以及规则本身的多种不确定性. 然后,提出了基于证据推理的确定因子规则库推理方法. 该方法通过将已知事实与规则前提进行匹配,推断结论并得到已知事实条件下的前提确定因子;进一步,根据证据推理算法得到结论的确定因子. 文章最后,通过基于证据推理的确定因子规则库推理方法在UCI数据集分类问题的应用算例,说明该方法的可行性和高效性.     

SDSS中空间知识库系统模型的设计

提出并实现了一个适用于智能化空间决策支持系统（SDSS）的空间知识库系统模型。在本知识库系统模型上，知识表示采用一阶谓词逻辑和案例两者相结合的方式，对于不同的知识表示方式，实现了相应的知识推理机制及相互间的切换，在此基础上，还设计实现了对案例知识的学习与获取方法。     

军事仿真想定中的数据冲突消解方法

分析军事仿真想定中的数据冲突问题,提出一种数据冲突消解方法。采用产生式规则表示法,表示军事仿真想定中的事实和规则,建立想定知识库,利用该知识库对编辑好的想定进行推理,从而排查仿真想定数据中的应用冲突。分析结果表明,该方法能发现仿真想定中的数据冲突,可作为仿真训练或评估的依据。     

Cashing in on Caching: An Architecture for Time-Bounded Knowledge-Based Problem Solving

Knowledge-based computing, in general, suffers from an inherent open-endedness that precludes its application in time-bounded domains where an answer must be computed within a stipulated time limit. We examine a two-way improvement of the shortcomings: a knowledge representation scheme that provides easy access to relevant knowledge and thereby reduces search time, and a reasoning scheme that is algorithmic in nature and thus makes computational requirements meaningfully estimable.In this work, we offer a cache-based architecture that is capable of both storing knowledge in different formats (e.g. rules, cases), and invoking an appropriate reasoning scheme to fit the available computing time. The cache helps in retrieving the most relevant pieces of knowledge (not only exact matches) required for solving a given problem. This cache relies on a reasoning tactic, knowledge interpolation, that can generate a solution from two near-matches in an algorithmic way, to generate time-bounded solutions. We illustrate the design of such a cache for solving resource allocation problems in the domain of shortwave radio transmission and evaluate its performance in observing imposed temporal bounds.     

“知识表示与知识推理”知识体的教学设计

知识表示与知识推理是智能信息处理的基础,在计算机科学和人工智能的众多领域有着广泛应用。知识表示与知识推理的教学,在CC2001和CS2008给出的计算机科学知识体中占据了两个知识单元的位置,但在国内尚未得到足够重视。本文首先阐述将知识表示与知识推理作为一门课程进行教学的必要性;接下来以CC2001和CS2008为参照,给出一个32课时的"知识表示与知识推理"教学知识体;最后对教学实践中遇到的主要问题进行分析,并针对这些问题提出相应解决对策。     

Seeing Is Believing

In this paper a formal framework is proposed in which variousinformative actions are combined, corresponding to the different ways in whichrational agents can acquire information. In order to solve the variousconflicts that could possibly occur when acquiring information fromdifferent sources, we propose a classification of the informationthat an agent possesses according to credibility. Based on this classification, we formalize what itmeans for agents to have seen or heard something, or to believesomething by default. We present a formalization of observations,communication actions, and the attempted jumps to conclusions thatconstitutes default reasoning. To implement these informative actionswe use a general belief revision action which satisfies theAGM postulates; dependent on the credibility of the incominginformation this revision action acts on one or more parts ofthe classified belief sets of the agents. The abilities of agents formalizeboth the limited capacities of agents to acquire information, and the preference of one kind of information acquisition to another. A very important feature of our approach is that it shows how to integratevarious aspects of agency, in particular the (informational) attitudesof dealing with information from observation, communication and defaultreasoning into one coherent framework, both model-theoretically andsyntactically.     

视觉知识：跨媒体智能进化的新支点

回顾跨媒体智能的发展历程,分析跨媒体智能的新趋势与现实瓶颈,展望跨媒体智能的未来前景。跨媒体智能旨在融合多来源、多模态数据,并试图利用不同媒体数据间的关系进行高层次语义理解与逻辑推理。现有跨媒体算法主要遵循了单媒体表达到多媒体融合的范式,其中特征学习与逻辑推理两个过程相对割裂,无法综合多源多层次的语义信息以获得统一特征,阻碍了推理和学习过程的相互促进和修正。这类范式缺乏显式知识积累与多级结构理解的过程,同时限制了模型可信度与鲁棒性。在这样的背景下,本文转向一种新的智能表达方式——视觉知识。以视觉知识驱动的跨媒体智能具有多层次建模和知识推理的特点,并易于进行视觉操作与重建。本文介绍了视觉知识的3个基本要素,即视觉概念、视觉关系和视觉推理,并对每个要素展开详细讨论与分析。视觉知识有助于实现数据与知识驱动的统一框架,学习可归因可溯源的结构化表达,推动跨媒体知识关联与智能推理。视觉知识具有强大的知识抽象表达能力和多重知识互补能力,为跨媒体智能进化提供了新的有力支点。     

基于改进多嵌入空间的实时语义数据流推理

将语义数据流处理引擎与知识图谱嵌入表示学习相结合,可以有效提高实时数据流推理查询性能,但是现有的知识表示学习模型更多关注静态知识图谱嵌入,忽略了知识图谱的动态特性,导致难以应用于实时动态语义数据流推理任务。为了使知识表示学习模型适应知识图谱的在线更新并能够应用于语义数据流引擎,建立一种基于改进多嵌入空间的动态知识图谱嵌入模型PUKALE。针对传递闭包等复杂推理场景,提出3种嵌入空间生成算法。为了在进行增量更新时更合理地选择嵌入空间,设计2种嵌入空间选择算法。基于上述算法实现PUKALE模型,并将其嵌入数据流推理引擎CSPARQL-engine中,以实现实时语义数据流推理查询。实验结果表明,与传统的CSPARQL和KALE推理相比,PUKALE模型的推理查询时间分别约降低85%和93%,其在支持动态图谱嵌入的同时能够提升实时语义数据流推理准确率。     

基于表示学习的开放域中文知识推理

知识库通常以网络的形式被组织起来,网络中每个节点代表实体,而每条连边则代表实体间的关系。为了利用这种网状知识库中的知识,往往需要设计专门的、复杂度较高的图算法。然而这些算法并不能很好适用于知识推理,尤其是随着知识库的知识规模不断扩大,基于网状结构知识库的推理很难较好地满足实时计算的需求。该文使用基于TransE模型的知识表示学习进行知识推理,包括对实体关系三元组中关系指示词以及尾实体的推理,其中关系指示词推理的实验取得了较好的结果,且推理过程无需设计复杂的算法,仅涉及向量的简单运算。另外,该文对原始TransE模型的代价函数进行改进,以更好地适用于开放域中文知识库表示学习。     

融合知识表示和深度强化学习的知识推理方法

知识推理是解决知识图谱中知识缺失问题的重要方法,针对大规模知识图谱中知识推理方法仍存在可解释性差、推理准确率和效率偏低的问题,提出了一种将知识表示和深度强化学习相结合的方法RLPTransE。利用知识表示学习方法,将知识图谱映射到含有三元组语义信息的向量空间中,并在该空间中建立强化学习环境。通过单步择优策略网络和多步推理策略网络的训练,使强化学习智能体在与环境交互过程中,高效挖掘推理规则进而完成推理。在公开数据集上的实验结果表明,相比于其他先进方法,该方法在大规模数据集推理任务中取得更好的表现。     

基于场景及规则获取模板的知识获取方法

产生式规则作为知识库系统进行推理的常用的、可读性好的知识表示形式,在构建知识库系统时有极大的优越性.提出一种基于场景及规则获取模板的知识获取方法,并以某高分子复合材料的加工专家为知识获取对象.该方法通过分析、记录领域专家进行设计的过程、解决问题的过程和动作,将领域问题按层次细化为一系列子问题,并在子问题场景下结合场景模型及知识获取模板来获取规则性知识.采用该方法可以辅助领域专家在明晰领域知识结构的基础上,逐步挖掘领域中细粒度的规则性知识.