半边图模型之构造演算

提出一个动态可增殖的多层次自组织认知系统,每个层次具有形式上一致的知识表示方法,各层的自组关联、自组聚合、归约和样本表达四个知识处理模型是实现系统自组织层次增殖的核心模型。指出若要实现层次可自组织增殖的系统,其关键是要设计一个合理的聚合归约演算系统;提出一个适用于各个层次的基于可结合半边的自组图知识表示法,先给出自组图形式化的静态定义和动态定义,然后以自组关联模型为背景给出对应的自组图构造算法。     

Global target aggregation and state agreement of nonlinear multi-agent systems with switching topologies

In this paper, we discuss coordination problems of a group of autonomous agents, including the target aggregation to a convex set and the state agreement. The aggregation of the whole agent group, consisting of leaders (informed agents) and followers, to a given set is investigated with switching interconnection topologies described by the connectivity assumptions on the joint topology in the time interval [t,+∞) for any time t, and then the state agreement problem is studied in a similar way. An approach based on set stability and limit set analysis is given to study the multi-agent convergence problems. With the help of graph theory and convex analysis, coordination conditions are obtained in some important cases, and the results show that simple local rules can make the networked agents with first-order nonlinear individual dynamics achieve desired collective behaviors.     

A Port Graph Calculus for Autonomic Computing and Invariant Verification

From our previous work on biochemical applications, the structure of port graph (or multigraph with ports) and a rewriting calculus have proved to be well-suited formalisms for modeling interactions between proteins. Then port graphs have been proposed as a formal model for distributed resources and grid infrastructures, where each resource is modeled by a node with ports. The lack of global information and the autonomous and distributed behavior of components are modeled by a multiset of port graphs and rewrite rules which are applied locally, concurrently, and non-deterministically. Some computations take place wherever it is possible and in parallel, while others may be controlled by strategies. In this paper, we first introduce port graphs as graphs with multiple edges and loops, with nodes having explicit connection points, called ports, and edges attaching to ports of nodes. We then define an abstract biochemical calculus that instantiates to a rewrite calculus on these graphs. Rules and strategies are themselves port graphs, i.e. first-class objects of the calculus. As a consequence, they can be rewritten as well, and rules can create new rules, providing a way of modeling adaptive systems. This approach also provides a formal framework to reason about computations and to verify useful properties. We show how structural properties of a modeled system can be expressed as strategies and checked for satisfiability at each step of the computation. This provides a way to ensure invariant properties of a system. This work is a contribution to the formal specification and verification of adaptive systems and to theoretical foundations of autonomic computing.     

On judgment aggregation in abstract argumentation

Judgment aggregation is a field in which individuals are required to vote for or against a certain decision (the conclusion) while providing reasons for their choice. The reasons and the conclusion are logically connected propositions. The problem is how a collective judgment on logically interconnected propositions can be defined from individual judgments on the same propositions. It turns out that, despite the fact that the individuals are logically consistent, the aggregation of their judgments may lead to an inconsistent group outcome, where the reasons do not support the conclusion. However, in this paper we claim that collective irrationality should not be the only worry of judgment aggregation. For example, judgment aggregation would not reject a consistent combination of reasons and conclusion that no member voted for. In our view this may not be a desirable solution. This motivates our research about when a social outcome is ‘compatible’ with the individuals’ judgments. The key notion that we want to capture is that any individual member has to be able to defend the collective decision. This is guaranteed when the group outcome is compatible with its members views. Judgment aggregation problems are usually studied using classical propositional logic. However, for our analysis we use an argumentation approach to judgment aggregation problems. Indeed the question of how individual evaluations can be combined into a collective one can also be addressed in abstract argumentation. We introduce three aggregation operators that satisfy the condition above, and we offer two definitions of compatibility. Not only does our proposal satisfy a good number of standard judgment aggregation postulates, but it also avoids the problem of individual members of a group having to become committed to a group judgment that is in conflict with their own individual positions.     

Unexpected universality results for three classes of P systems with symport/antiport

Symport and antiport are biological ways of transporting molecules through membranesin ``collaborating' pairs; in the case of symport the two molecules pass in the same direction, in the case of antiport the two molecules pass in opposite directions. Here we first survey the results about the computing power of membrane systems (P systems) using only symport/antiport rules (hence these systems compute by communication only), then we consider a recently introduced, way of defining the result of a computation in a membrane system: looking for the trace of certain objects in their movement through membranes. Rather unexpected, in this way we get characterizations of recursively enumerable languages by means of membrane systems with symport/antiport which work with multisets of objects (note the qualitative difference between the data structure used by computations – multisets: no ordering– and the data structure of the output – strings: linear ordering). A similar remark holds true for the case of analysing P systems, which work in an automata-like manner: the sequence of certain distinguished objects taken from the environment during acomputation is the string recognized by the computation. We also survey universality results from this area, with sketched proofs. Some open problems are also formulated. This revised version was published online in June 2006 with corrections to the Cover Date.     

基于KNN与矩阵变换的图节点嵌入归纳式学习算法

图节点的低维嵌入在各种预测任务中是非常有用的,如蛋白质功能预测、内容推荐等。然而,多数方法不能自然推广到不可见节点。图采样聚合算法(Graph Sample and Aggregate,Graphsage)虽然可以提高不可见节点生成嵌入的速度,但容易引入噪声数据,且生成的节点嵌入的表示能力不高。为此,文中提出了一种基于KNN与矩阵变换的图节点嵌入归纳式学习算法。首先,通过KNN选取K个邻节点;然后,根据聚合函数生成聚合信息;最后,利用矩阵变换与全连接层对聚合信息和节点信息进行计算,得到新的节点嵌入。为了有效权衡计算时间与性能,文中提出一种新的聚合函数,对邻节点特征运用最大池化作为聚合信息输出,以更多地保留邻节点信息,降低计算代价。在reddit和PPI两个数据集上的实验表明,所提算法在micro-f1和macro-f1两个评价指标上分别获得了4.995%与10.515%的提升。因此,该算法可以大幅减少噪声数据,提高节点嵌入的表示能力,快速有效地为不可见节点及不可见图生成节点嵌入。     

基于判断聚合逻辑的偏好聚合分析

如何将个体理性偏好聚合为群体理性偏好是社会理性认知研究面临的主要难题,判断聚合研究为偏好聚合问题的分析提供了新思路。从逻辑的视角探究判断聚合与偏好聚合之间的关系,分析了判断聚合模态逻辑JAL,证明利用一阶逻辑语言基于JAL可以构建一个偏好聚合的模态逻辑JAL(LK),以将偏好聚合转化为判断聚合来处理,因而判断聚合模型更具一般性。     

A grammatical approach to self-organizing robotic systems

In this paper, we define a class of graph grammars that can be used to model and direct concurrent robotic self-assembly and similar self-organizing processes. We give several detailed examples of the formalism and then focus on the problem of synthesizing a grammar so that it generates a given, prespecified assembly. In particular, to generate an acyclic graph we synthesize a binary grammar (rules involve at most two parts), and for a general graph we synthesize a ternary grammar (rules involve at most three parts). In both cases, we characterize the number of concurrent steps required to achieve the assembly. We also show a general result that implies that no binary grammar can generate a unique stable assembly. We conclude the paper with a discussion of how graph grammars can be used to direct the self-assembly of robotic parts.     

一种新的高效图聚集算法

图聚集是将一个大规模的图用简洁的并能有效反映原始图的结构和属性信息的小规模图来表示的技术.图聚集在图数据管理、分析和可视化中发挥着重要作用.图聚集方面现有研究结果还很少,也很不系统.其主要不足之处是:1)算法依赖于具体应用;2)算法仅考虑了图的某方面信息,如结构信息或属性信息;3)算法对用户提供的交互和反馈信息的约束很强.针对现有图聚集算法存在的主要不足,提出一种有向图新型图聚集算法,该算法采用一种新的聚集图质量函数,全面刻画了聚集图多样性、覆盖性、简洁性和实用性.该算法使用LSH(locality sensitive Hashing)技术和基于熵的划分技术,保证了聚集图的质量.在真实数据集上进行了大量的实验,验证了算法的有效性.     

On the logic of preference and judgment aggregation

Agents that must reach agreements with other agents need to reason about how their preferences, judgments, and beliefs might be aggregated with those of others by the social choice mechanisms that govern their interactions. The emerging field of judgment aggregation studies aggregation from a logical perspective, and considers how multiple sets of logical formulae can be aggregated to a single consistent set. As a special case, judgment aggregation can be seen to subsume classical preference aggregation. We present a modal logic that is intended to support reasoning about judgment aggregation scenarios (and hence, as a special case, about preference aggregation): the logical language is interpreted directly in judgment aggregation rules. We present a sound and complete axiomatisation. We show that the logic can express aggregation rules such as majority voting; rule properties such as independence; and results such as the discursive paradox, Arrow’s theorem and Condorcet’s paradox—which are derivable as formal theorems of the logic. The logic is parameterised in such a way that it can be used as a general framework for comparing the logical properties of different types of aggregation—including classical preference aggregation. As a case study we present a logical study of, including a formal proof of, the neutrality lemma, the main ingredient in a well-known proof of Arrow’s theorem.     

A distributed approach for graph mining in massive networks

We propose a novel distributed algorithm for mining frequent subgraphs from a single, very large, labeled network. Our approach is the first distributed method to mine a massive input graph that is too large to fit in the memory of any individual compute node. The input graph thus has to be partitioned among the nodes, which can lead to potential false negatives. Furthermore, for scalable performance it is crucial to minimize the communication among the compute nodes. Our algorithm, DistGraph, ensures that there are no false negatives, and uses a set of optimizations and efficient collective communication operations to minimize information exchange. To our knowledge DistGraph is the first approach demonstrated to scale to graphs with over a billion vertices and edges. Scalability results on up to 2048 IBM Blue Gene/Q compute nodes, with 16 cores each, show very good speedup.     

基于随机图的物联网多节点连通性判定方法

多节点协作已成为物联网的重要应用趋势,为了确保多个节点之间的可靠协作,提出了一种基于随机图的多节点连通性判定方法.首先,根据节点之间的直接连通概率构建节点连通性随机图;然后将所构建的随机图拆分为包含协作组节点的目标组件和不包含协作组节点的中继组件,从而将协作组内多节点之间的连通性转换为组件之间的连通性;最后,提出了测试组件之间连通性的算法,其中定义了与直接连通概率有关的测试规则以对组件之间测试边的测试优先级进行排序.仿真结果表明,相比于现有方法,所提方法能够以较低的成本获得较高的准确度.     

无线传感器网络中基于最小延时的数据汇集树构建与传输调度算法

无线传感器网络的数据通信模式问题是目前的研究热点,针对现有的无线传感器网络数据汇集算法延时较大这一不足,对最小延时数据汇集树和传输调度问题进行了研究。提出一种基于度约束的汇集树构建算法(DCAT)。该算法按照 BFS 方式遍历图,当遍历到每个节点时,通过确定哪些节点与汇点更近来确定潜在母节点集合。然后,选择图中度数最小的潜在母节点作为当前被遍历节点的母节点。此外,为了在给定的汇集树上进行高效地数据汇集,还提出两种新的基于贪婪的TDMA传输调度算法：WIRES-G 和 DCAT-Greedy。利用随机生成的不同规模的传感器网络,参照当前最新算法,对文中方法的性能进行了全面评估。结果表明,与当前最优算法相比,文中调度算法与文中汇集树构建算法结合起来,可显著降低数据汇集的延时。     

P Automata with membrane channels

We investigated a variant of purely communicating P systems that are able to accept multisets or even strings given as sequences of terminal symbols taken from the environment. We showed that such P automata with membrane channels equipped with only one membrane and specific activating and prohibiting rules can already recognize any recursively enumerable language of multisets and strings, respectively. Moreover, using only activating rules of a very special kind, we obtained a characterization of regular languages. This work was presented, in part, at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24#x2013;26, 2003.     

Logichart — Intelligible Program Diagram for Prolog and its Processing System

An intelligible program diagram called Logichart has been developed for Prolog visualization. Its syntax rules and layout rules are formalized in terms of an attribute graph grammar. This grammar is underlain by a context–free graph grammar whose productions are defined to formalize the graph–syntax rules of Logichart diagrams. Semantic rules attached to the productions are defined in such a way that they can extract the layout information needed to display a Logichart diagram as attributes. Our Prolog visualization system, which has been implemented based on the attribute graph grammar, can draw a Logichart diagram for any correct Prolog program (completeness), and any Logichart diagram displayed by the system is always valid for the grammar (soundness). The system can also display the execution of a Prolog program in real time; that is, the Logichart diagram is dynamically updated as the program runs.     

Modeling Massive RFID Data Sets: A Gateway-Based Movement Graph Approach

Massive Radio Frequency Identification (RFID) data sets are expected to become commonplace in supply chain management systems. Warehousing and mining this data is an essential problem with great potential benefits for inventory management, object tracking, and product procurement processes. Since RFID tags can be used to identify each individual item, enormous amounts of location-tracking data are generated. With such data, object movements can be modeled by movement graphs, where nodes correspond to locations and edges record the history of item transitions between locations. In this study, we develop a movement graph model as a compact representation of RFID data sets. Since spatiotemporal as well as item information can be associated with the objects in such a model, the movement graph can be huge, complex, and multidimensional in nature. We show that such a graph can be better organized around gateway nodes, which serve as bridges connecting different regions of the movement graph. A graph-based object movement cube can be constructed by merging and collapsing nodes and edges according to an application-oriented topological structure. Moreover, we propose an efficient cubing algorithm that performs simultaneous aggregation of both spatiotemporal and item dimensions on a partitioned movement graph, guided by such a topological structure.     

仿高阶矩的结点不变量及其组成的图不变量

借鉴高阶矩的方法,采用层序的计算框架,依据结点的连接距离和层序信息定义了20种结点不变量。这些结点不变量体现图整体的上下偏分布特性、整体不均匀性和整体平滑性,结点不变量中的每层结点度数平方之和反映了层内结点度数的分布情况。通过比较这些结点不变量的可区分结点数,发现每层结点度数平方之和明显改善了结点不变量的细分能力。把排序后的结点不变量组成一个矢量后作为图的不变量。计算结果表明,共有9种图不变量可以区分所有结点数N<25的非同构树和N<34的非同构同胚不可约树(没有度数为2的树),对于更多结点的树,还没有发现非同构树有相同图不变量的例子;把这些图不变量应用到非同构图(N<10),区分结果好于 文献[8]中列出的22种图不变量的19种,而且文中9种图不变量的简并度不大,提高了随机图的同构测试性能。     

WSN中基于最小延时的数据汇集树构建与传输调度算法

针对现有的无线传感器网络数据汇集算法延时较大的不足,对最小延时数据汇集树和传输调度问题进行了研究。提出一种基于度约束的汇集树构建算法(DCAT)。该算法按照BFS方式遍历图,当遍历到每个节点时,通过确定哪些节点与汇点更近来确定潜在母节点集合。然后,选择图中度数最小的潜在母节点作为当前被遍历节点的母节点。此外,为了在给定的汇集树上进行高效的数据汇集,还提出两种新的基于贪婪的TDMA传输调度算法:WIRES-G和DCAT-Greedy。利用随机生成的不同规模的传感器网络,参照当前最新算法,对所提方法的性能进行了全面评估。结果表明,与当前最优算法相比,将所提调度算法与所提汇集树构建算法结合起来,可显著降低数据汇集的延时。     

A Hierarchical Approach to Judgment Aggregation with Abstentions

Judgment aggregation deals with the problem of how collective judgments on logically connected propositions can be formed based on individual judgments on the same propositions. The existing literature on judgment aggregation mainly focuses on the anonymity condition requiring that individual judgments be treated equally. However, in many real‐world situations, a group making collective judgments may assign individual members or subgroups different priorities to determine the collective judgment. Based on this consideration, this article relaxes the anonymity condition by giving a hierarchy over individuals so as to investigate how the judgment from each individual affects the group judgment in such a hierarchical environment. Moreover, we assume that an individual can abstain from voting on a proposition and the collective judgment on a proposition can be undetermined, which means that we do not require completeness at both individual and collective levels. In this new setting, we first identify an impossibility result and explore a set of plausible conditions in terms of abstentions. Secondly, we develop an aggregation rule based on the hierarchy of individuals and show that the aggregation rule satisfies those plausible conditions. The computational complexity of this rule is also investigated. Finally, we show that the proposed rule is (weakly) oligarchic over a subset of agenda. This is by no means a negative result. In fact, our result reveals that with abstentions, oligarchic aggregation is not necessary to be a single‐level determination but can be a multiple‐level collective decision making, which partially explains its ubiquity in the real world.