一种基于角色解决方案的多Agent合作问题求解模型

传统基于角色的方法大都缺少对合作问题求解的具体考虑.结合角色和团队形成机制提出了一种基于角色解决方案的合作问题求解模型.其新颖之处在于将角色解决方案与团队结合起来,致力于分析、设计和实现合作问题求解.深入研究了复杂的CPS循环过程,并给出了一种基于角色解决方案的CPS算法过程.以角色解决方案和团队的观点试图解决传统的合作问题求解过程,提高了对多Agent应用系统的理解,并为多Agent系统的分析与设计人员提供参考.     

基于时序活动逻辑的复杂系统多Agent动态协作模型

动态复杂问题求解是人工智能和复杂自适应系统理论与应用重要研究领域,多Agent动态协作是研究热点和难点之一,如何将问题与任务切分有待进一步地研究.借鉴组织学思想将自适应系统中的自主运行单元抽象为Agent,把复杂自适应系统视为多Agent系统组织,从时间和状态角度对复杂动态系统的行为进行描述,提出了基于时序活动逻辑的多Agent系统动态协作任务求解自适应机制和构造模型,建立了用于协作推理的语义规则、授权规则和行为规则,通过在中国科学院智能信息处理重点实验室开发的MAGE等平台上多方实验和仿真测试,验证了方法的可行性和有效性.     

Agent组织的一种递归模型

Agent组织是多Agent系统(MAS)的一种求解形式.基于Agent组织的问题求解可以减少MAS中Agent之间交互的复杂性,降低求解难度.结合收益和组织规则提出了一种Agent组织的递归模型,并讨论了Agent组织的目标分解、收益计算和组织规则形成等问题.相对于Ferber和Jennings等人的工作,这种模型适于描述不同规模的组织,有利于MAS宏观分析和微观分析的结合,而且模型中效用参量的引入可以在一定程度上表明Agent组织的演化.     

基于规则推理的实时信息物理监控系统

基于规则的CPS监控方法在降低监控复杂度和提升监控灵活性等方面具有显著优势. 目前基于规则的CPS监控方法未考虑CPS监控场景的时间约束, 仅仅利用各种优化技术来缩短监控的响应时间. 为此, 本文基于实时规则引擎建立了一个CPS的实时监控系统RTCPMS. 该系统采用Rete网络表示监控规则, 其核心是一个新的实时推理算法Rete-TC. Rete-TC算法引入了规则截止期, 通过基于优先级的Beta节点调度方法, 使得CPS监控的时间约束尽可能地被满足. 模拟实验与智慧建筑应用案例验证了RTCPMS系统的有效性, 且实验结果表明其核心算法Rete-TC的调度成功率优于传统的规则推理算法Rete.     

关于RBAC模型中约束的研究综述

约束是RBAC模型中的一组强制性规则，是RBAC的重要组成。目前对约束的研究主要集中在关系约束、前提约束、数值约束、职责分离约束、势约束和时间约束等方面。时间约束是时间变化和角色许可的依赖关系的规则表示。该文就连续时间约束和周期时间约束进行了描述。约束的标准化、规范以及应用领域中提出的新的约束。都需要进一步研究。     

一种基于证据理论的概念树结构多Agent合作求解模型

本文针对多Agent系统中Agent之间的盲目交互可能产生的效率低下问题,提出了一种基于慨念树结构的多Agent合作求解模型.在这个模型中,各Agent基于自己的领域知识构造出概念树,通过Agent之间的合作,对概念树从根节点开始使用证据理论实现逐层聚焦,逐步缩小求解范围.为此,本文基于模态、逻辑和关系概念提出了一种面向可能解集的证据理论表示,并探讨了在多Agent环境下应用证据理论可能导致的若干问题.     

基于内部结构MPOMDP模型的策略梯度学习算法

为了提高MPOMDP模型的知识表示能力和推理效率,提出一种基于Agent内部结构的MPOMDP模型。该模型能表示Agent的内部结构及其时间演化,并通过将系统联合概率分布表示成每个Agent内部变量集的局部因式形式,以提高模型的推理效率。将GPI-POMDP算法扩展到基于内部结构的MPOMDP模型中,给出基于内部状态的多Agent策略梯度算法（MIS-GPOMDP）,来求解基于内部结构的MPOMDP。实验结果表明MIS-GPOMDP算法具有较高的推理效率,且算法是收敛的。     

Agent组织规则的再励学习

Agent组织是一种灵活有效的多Agent系统求解方式。Agent组织规则在Agent组织的求解过程中起着重要作用，可以有效地减少冲突提高求解效率。给出了一种基于再励学习的Agent组织规则生成机制和相应的算法，通过实验表明了算法的有效性，改进了Zambonelli和Jennings等人关于Agent组织规则的工作。     

基于主Agent信念修正的推测计算及其资源协商

基于假设推理(abduction-based)的推测计算(speculative computation)是在资源信息不能及时到达时,利用缺省假设进行计算的过程.在计算过程中,如果应答和信念不一致,则主Agent将修正它的信念.为了实现目标,在有限时间内使推测计算的结果更精确,主Agent要通过协商获得尽可能多的实际信息,协商是降低决策风险的主要途径.在介绍假设推理和推测计算的基本原理的基础上,提出了基于时间约束的推测计算扩展框架、基于时间约束的进一步协商框架和基于信念修正的协商算法,并将进一步协商框架和协商算法嵌入到推测计算的过程中,在协商过程中赋予主Agent更强的信念修正能力.最后,在货物运输领域的实验中,证实了基于信念修正的推测计算的有效性.     

一种基于资源约束的Agent组织规则生成机制

Agent组织是多Agent系统的一种求解结构，可以有效地降低求解难度和Agent之间的交互复杂性，对Agent组织的抽象包括组织结构，组织规则和组织模式，Agent组织规则的形成是Agent组织设计的重要问题之一，基于资源约束给出了Agent组织规则的形式描述和产生机制，设计了Agent组织规则形成的静态算法和动态算法，从而改进了Zambonelli和Jennings关于Agent组织规则的研究。     

An inquiry into computer understanding

This essay addresses a number of issues centered around the question of what is the best method for representing and reasoning about common sense (sometimes called plausible inference). Drew McDermott has shown that a direct translation of commonsense reasoning into logical form leads to insurmountable difficulties, from which McDermott concluded that we must resort to procedural ad hocery. This paper shows that the difficulties McDermott described are a result of insisting on using logic as the language of commonsense reasoning. If, instead, (Bayesian) probability is used, none of the technical difficulties found in using logic arise. For example, in probability, the problem of referential opacity cannot occur and nonmonotonic logics (which McDermott showed don't work anyway) are not necessary. The difficulties in applying logic to the real world are shown to arise from the limitations of truth semantics built into logic–probability substitutes the more reasonable notion of belief. In Bayesian inference, many pieces of evidence are combined to get an overall measure of belief in a proposition. This is much closer to commonsense patterns of thought than long chains of logical inference to the true conclusions. Also it is shown that English expressions of the “IF A THEN B” form are best interpreted as conditional probabilities rather than universally quantified expressions. Bayesian inference is applied to a simple example of linguistic information to illustrate the potential of this type of inference for AI. This example also shows how to deal with vague information, which has so far been the province of fuzzy logic. It is further shown that Bayesian inference gives a theoretical basis for inductive inference that is borne out in practice. Instead of insisting that probability is the best language for commonsense reasoning, a major point of this essay is to show that real inference is a complex interaction between probability, logic, and other formal representation and reasoning systems.     

A neural network model of causality

This paper proposes a model for commonsense causal reasoning, based on the basic idea of neural networks. After an analysis of the advantages and limitations of existing accounts of causality, a fuzzy logic based formalism FEL is proposed that takes into account the inexactness and the cumulative evidentiality of commonsense causal reasoning, overcoming the limitations of existing accounts. Analyses concerning how FEL handles various aspects of commonsense causal reasoning are performed, in an abstract way. FEL can be implemented (naturally) in a neural (connectionist) network. This work also serves to link rule-based reasoning with neural network models, in that a rule-encoding scheme (FEL) is equated directly to a neural network model.     

A user modeling approach for reasoning about interaction sensitive to bother and its application to hospital decision scenarios

In this paper, we present a framework for interacting with users that is sensitive to the cost of bother and then focus on its application to decision making in hospital emergency room scenarios. We begin with a model designed for reasoning about interaction in a single-agent single-user setting and then expand to the environment of multiagent systems. In this setting, agents consider both whether to ask other agents to perform decision making and at the same time whether to ask questions of these agents. With this fundamental research as a backdrop, we project the framework into the application of reasoning about which medical experts to interact with, sensitive to possible bother, during hospital decision scenarios, in order to deliver the best care for the patients that arrive. Due to the real-time nature of the application and the knowledge-intensive nature of the decisions, we propose new parameters to include in the reasoning about interaction and sketch their usefulness through a series of examples. We then include a set of experimental results confirming the value of our proposed approach for reasoning about interaction in hospital settings, through simulations of patient care in those environments. We conclude by pointing to future research to continue to extend the model for reasoning about interaction in multiagent environments for the setting of time-critical care in hospital settings.     

DOMAIN-INDEPENDENT TEMPORAL REASONING WITH RECURRING EVENTS

Numerous examples of temporal reasoning involve a process of abstraction from the number of times an event is to occur or the number of times events stand in a temporal relation. For example, scheduling a recurring event such as one's office hours may consider things like the relative temporal ordering of the office hours and a number of other events in a given work day. The number of times office hours will actually be held may be unknown, even irrelevant, at the time of scheduling them. The objective of this article is to formulate a domain-independent framework for reasoning about recurring events and their relations. To achieve this end, we propose an ontology of recurrence based on the model-theoretic structure underlying collective predication using plural noun phrases. We offer a calculus of binary temporal relations for temporal collections based on a well-defined transformation of interval temporal relations into recurrence relations. Finally, we describe a reasoning framework based on manipulating knowledge stored in temporal relation networks, which is in turn a specialization of the CSP (constraint satisfaction problem) framework. The reasoner manipulates recurrence relations in the network to determine the network's consistency or to generate scenarios.     

Meta-path reasoning of knowledge graph for commonsense question answering

Commonsense question answering (CQA) requires understanding and reasoning over QA context and related commonsense knowledge, such as a structured Knowledge Graph (KG). Existing studies combine language models and graph neural networks to model inference. However, traditional knowledge graph are mostly concept-based, ignoring direct path evidence necessary for accurate reasoning. In this paper, we propose MRGNN (Meta-path Reasoning Graph Neural Network), a novel model that comprehensively captures sequential semantic information from concepts and paths. In MRGNN, meta-paths are introduced as direct inference evidence and an original graph neural network is adopted to aggregate features from both concepts and paths simultaneously. We conduct sufficient experiments on the CommonsenceQA and OpenBookQA datasets, showing the effectiveness of MRGNN. Also, we conduct further ablation experiments and explain the reasoning behavior through the case study.     

支持规则推理的神经网络模型

本文研究了支持规则推理的神经网络模型，表明通常执行的推理与符号系统在方法上确实相似，只是它们对常识推理提供了更多的方法。CONSYDERR是一种支持常识推理的连接结构，其目的是给出常识推理的一种模型，并纠正传统规则系统中的脆弱性问题。本项工作表明，推理的连接模型不仅实现了符号推理，而且是一种更好的常识推理的计算模型。     

Pouring liquids: A study in commonsense physical reasoning

This paper presents a theory that supports commonsense, qualitative reasoning about the flow of liquid around slowly moving solid objects; specifically, inferring that liquid can be poured from one container to another, given only qualitative information about the shapes and motions of the containers. It shows how the theory and the problem specification can be expressed in a first-order language; and demonstrates that this inference and other similar inferences can be justified as deductive conclusions from the theory and the problem specification.     

Inference in multiply sectioned Bayesian networks: methods and performance comparison.

This paper extends lazy propagation for inference in single-agent Bayesian networks (BNs) to multiagent lazy inference in multiply sectioned BNs (MSBNs). Two methods are proposed using distinct runtime structures. It was proved that the new methods are exact and efficient when the domain structure is sparse. Both improve space and time complexity more than the existing method, which allows multiagent probabilistic reasoning to be performed in much larger domains given the computational resource. The relative performances of the three methods are compared analytically and experimentally.     

Privacy Loss in Distributed Constraint Reasoning: A Quantitative Framework for Analysis and its Applications

It is critical that agents deployed in real-world settings, such as businesses, offices, universities and research laboratories, protect their individual users’ privacy when interacting with other entities. Indeed, privacy is recognized as a key motivating factor in the design of several multiagent algorithms, such as in distributed constraint reasoning (including both algorithms for distributed constraint optimization (DCOP) and distributed constraint satisfaction (DisCSPs)), and researchers have begun to propose metrics for analysis of privacy loss in such multiagent algorithms. Unfortunately, a general quantitative framework to compare these existing metrics for privacy loss or to identify dimensions along which to construct new metrics is currently lacking. This paper presents three key contributions to address this shortcoming. First, the paper presents VPS (Valuations of Possible States), a general quantitative framework to express, analyze and compare existing metrics of privacy loss. Based on a state-space model, VPS is shown to capture various existing measures of privacy created for specific domains of DisCSPs. The utility of VPS is further illustrated through analysis of privacy loss in DCOP algorithms, when such algorithms are used by personal assistant agents to schedule meetings among users. In addition, VPS helps identify dimensions along which to classify and construct new privacy metrics and it also supports their quantitative comparison. Second, the article presents key inference rules that may be used in analysis of privacy loss in DCOP algorithms under different assumptions. Third, detailed experiments based on the VPS-driven analysis lead to the following key results: (i) decentralization by itself does not provide superior protection of privacy in DisCSP/DCOP algorithms when compared with centralization; instead, privacy protection also requires the presence of uncertainty about agents’ knowledge of the constraint graph. (ii) one needs to carefully examine the metrics chosen to measure privacy loss; the qualitative properties of privacy loss and hence the conclusions that can be drawn about an algorithm can vary widely based on the metric chosen. This paper should thus serve as a call to arms for further privacy research, particularly within the DisCSP/DCOP arena.     

基于视觉和语言的跨媒体问答与推理研究综述

基于视觉和语言的跨媒体问答与推理是人工智能领域的研究热点之一,其目的是基于给定的视觉内容和相关问题,模型能够返回正确的答案。随着深度学习的飞速发展及其在计算机视觉和自然语言处理领域的广泛应用,基于视觉和语言的跨媒体问答与推理也取得了较快的发展。文中首先系统地梳理了当前基于视觉和语言的跨媒体问答与推理的相关工作,具体介绍了基于图像的视觉问答与推理、基于视频的视觉问答与推理以及基于视觉常识推理模型与算法的研究进展,并将基于图像的视觉问答与推理细分为基于多模态融合、基于注意力机制和基于推理3类,将基于视觉常识推理细分为基于推理和基于预训练2类;然后总结了目前常用的问答与推理数据集,以及代表性的问答与推理模型在这些数据集上的实验结果;最后展望了基于视觉和语言的跨媒体问答与推理的未来发展方向。