Influence diagrams with super value nodes involving imprecise information

The concept of super value nodes was established to allow dynamic programming to be performed within the theory of influence diagrams and to reduce the computational complexity in solving problems by means of influence diagrams. This paper is focused on how influence diagrams with super value nodes are affected by the presence of imprecise information. We analyze how to reduce the complexity when evaluating an influence diagram in this framework by modelling these kinds of nodes and random magnitudes in terms of fuzzy random variables. Finally, an applied example of the theoretical results is developed.     

Decision making with hybrid influence diagrams using mixtures of truncated exponentials

Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization for representing continuous chance variables in influence diagrams. Also, MTE potentials can be used to approximate utility functions. This paper introduces MTE influence diagrams, which can represent decision problems without restrictions on the relationships between continuous and discrete chance variables, without limitations on the distributions of continuous chance variables, and without limitations on the nature of the utility functions. In MTE influence diagrams, all probability distributions and the joint utility function (or its multiplicative factors) are represented by MTE potentials and decision nodes are assumed to have discrete state spaces. MTE influence diagrams are solved by variable elimination using a fusion algorithm.     

Qualitative possibilistic influence diagrams based on qualitative possibilistic utilities

This paper proposes a new approach for decision making under uncertainty based on influence diagrams and possibility theory. The so-called qualitative possibilistic influence diagrams extend standard influence diagrams in order to avoid difficulties attached to the specification of both probability distributions relative to chance nodes and utilities relative to value nodes. In fact, generally, it is easier for experts to quantify dependencies between chance nodes qualitatively via possibility distributions and to provide a preferential relation between different consequences. In such a case, the possibility theory offers a suitable modeling framework. Different combinations of the quantification between chance and utility nodes offer several kinds of possibilistic influence diagrams. This paper focuses on qualitative ones and proposes an indirect evaluation method based on their transformation into possibilistic networks. The proposed approach is implemented via a possibilistic influence diagram toolbox (PIDT).     

Solving linear-quadratic conditional Gaussian influence diagrams

This paper considers the problem of solving Bayesian decision problems with a mixture of continuous and discrete variables. We focus on exact evaluation of linear-quadratic conditional Gaussian influence diagrams (LQCG influence diagrams) with additively decomposing utility functions. Based on new and existing representations of probability and utility potentials, we derive a method for solving LQCG influence diagrams based on variable elimination. We show how the computations performed during evaluation of a LQCG influence diagram can be organized in message passing schemes based on Shenoy–Shafer and Lazy propagation. The proposed architectures are the first architectures for efficient exact solution of LQCG influence diagrams exploiting an additively decomposing utility function.     

A GRASP algorithm for the multi-criteria minimum spanning tree problem

This paper proposes a GRASP (Greedy Randomized Adaptive Search Procedure) algorithm for the multi-criteria minimum spanning tree problem, which is NP-hard. In this problem a vector of costs is defined for each edge of the graph and the problem is to find all Pareto optimal or efficient spanning trees (solutions). The algorithm is based on the optimization of different weighted utility functions. In each iteration, a weight vector is defined and a solution is built using a greedy randomized constructive procedure. The found solution is submitted to a local search trying to improve the value of the weighted utility function. We use a drop-and-add neighborhood where the spanning trees are represented by Prufer numbers. In order to find a variety of efficient solutions, we use different weight vectors, which are distributed uniformly on the Pareto frontier. The proposed algorithm is tested on problems with r=2 and 3 criteria. For non-complete graphs with n=10, 20 and 30 nodes, the performance of the algorithm is tested against a complete enumeration. For complete graphs with n=20, 30 and 50 nodes the performance of the algorithm is tested using two types of weighted utility functions. The algorithm is also compared with the multi-criteria version of the Kruskal’s algorithm, which generates supported efficient solutions. This work was funded by the Municipal Town Hall of Campos dos Goytacazes city. The used computer was acquired with resource of CNPq.     

Solving influence diagrams with fuzzy chance and value nodes

In this paper we discuss how influence diagrams are affected by the presence of fuzziness in chance and value nodes. In this way, by modelling fuzzy-valued random variables and utilities in terms of fuzzy random variables, we analyze the statistical rules corresponding to the affected value-preserving transformations, namely: chance node removal and decision node removal. Some supporting results on fuzzy random variables introduced in this paper will provide us with the required mathematical tool to formalize the new statistical rules. Finally, an example is included to illustrate the study in the paper.     

基于网络拓扑结构的重要节点发现算法

复杂网络中的重要节点发现在现实生活中有着广泛的应用价值。传统重要节点发现方法可分为局部发现和全局发现两类算法,全局发现算法中最具代表性的是特征向量中心性算法(Eigenvector Centrality, EC),EC算法将所有节点归为一个社区并利用邻居节点重要性反馈计算节点的影响力大小,具有较高的计算效率和识别精度。但是,EC算法忽略了网络的拓扑结构,未考虑到真实网络中节点所在社区的结构特征。为此,本文提出一种基于网络拓扑结构的可达中心性算法(Accessibility Centrality, AC),首先利用邻接矩阵作为反馈路径,在反馈过程中计算不同路径下的节点整体影响力。同时,利用影响力传递过程中的噪音干扰特性,修正每一路径长度下节点整体影响力大小,最后利用修正结果得到AC值。为评估AC算法,本文利用两种传染病模型模拟节点影响力在四组真实网络中的传播过程,并引入其他四种算法进行对比验证。实验结果表明,与其他算法相比,AC算法可以更准确、有效地识别出有具有影响力的重要节点。     

Optimising a nonlinear utility function in multi-objective integer programming

In this paper we develop an algorithm to optimise a nonlinear utility function of multiple objectives over the integer efficient set. Our approach is based on identifying and updating bounds on the individual objectives as well as the optimal utility value. This is done using already known solutions, linear programming relaxations, utility function inversion, and integer programming. We develop a general optimisation algorithm for use with k objectives, and we illustrate our approach using a tri-objective integer programming problem.     

Algebraic—geometric quantization of interacting strings and superstrings

The method of algebraic-geometric quantization is used to find an explicit expression for the measure on the (super)moduli space for all possible interaction diagrams of open and closed (super)strings.Translated from Zapiski Nauchnykh Seminarov Leningradskogo Otdeleniya Matematicheskogo Instituta im. V. A. Steklova AN SSSR, Vol. 189, pp. 122–145, 1991.     

Influence diagrams for Bayesian decision analysis

Influence diagrams for representing Bayesian decision problems are redefined in a formal way using conditional independence. This makes the graphs somewhat more helpful for exploring the consequences of a clients state beliefs. Some important results about the manipulation of influence diagrams are extended and reviewed as is an algorithm for computing an optimal policy. Two new results about the manipulation of influence diagrams are derived. A novel influence diagram representing a practical decision problem is used to illustrate the methodologies presented in this paper.     

Maximizing Queueing Network Utility Subject to Stability: Greedy Primal-Dual Algorithm

We study a model of controlled queueing network, which operates and makes control decisions in discrete time. An underlying random network mode determines the set of available controls in each time slot. Each control decision “produces” a certain vector of “commodities”; it also has associated “traditional” queueing control effect, i.e., it determines traffic (customer) arrival rates, service rates at the nodes, and random routing of processed customers among the nodes. The problem is to find a dynamic control strategy which maximizes a concave utility function H(X), where X is the average value of commodity vector, subject to the constraint that network queues remain stable.We introduce a dynamic control algorithm, which we call Greedy Primal-Dual (GPD) algorithm, and prove its asymptotic optimality. We show that our network model and GPD algorithm accommodate a wide range of applications. As one example, we consider the problem of congestion control of networks where both traffic sources and network processing nodes may be randomly time-varying and interdependent. We also discuss a variety of resource allocation problems in wireless networks, which in particular involve average power consumption constraints and/or optimization, as well as traffic rate constraints.     

Computing the NTU-Shapley value of NTU-games defined by multiple objective linear programs

In this paper we study a class of cooperative games with non-transferable utility (NTU) derived from multiple objective linear programs (MOLP's). This is done in order to introduce the NTU-Shapley value as a solution to multiple objective linear programming.  We present an algorithm for the computation of the set of all NTU-Shapley values for the bicriterion cases, which is based on the simplex algorithm. Furthermore, we give a new optimal recursive procedure for the computation of the Shapley value of TU-games. Received: September 1997/Final version: May 1999     

Fast local search methods for solving limited memory influence diagrams

Limited memory influence diagrams are graph-based models that describe decision problems with limited information such as planning with teams and/or agents with imperfect recall. Solving a (limited memory) influence diagram is an NP-hard problem, often approached through local search. In this work we give a closer look at k-neighborhood local search approaches. We show that finding a k-neighboring strategy that improves on the current solution is W[1]-hard and hence unlikely to be polynomial-time tractable. We also show that finding a strategy that resembles an optimal strategy (but may have low expected utility) is NP-hard. We then develop fast schema to perform approximate k-local search; experiments show that our methods improve on current local search algorithms both with respect to time and to accuracy.     

A comparative analysis of adaptive algorithms in the finite element method for solving the boundary value problem for a stationary reaction-diffusion equation

A new adaptive algorithm is proposed for constructing grids in the hp-version of the finite element method with piecewise polynomial basis functions. This algorithm allows us to find a solution (with local singularities) to the boundary value problem for a one-dimensional reaction-diffusion equation and smooth the grid solution via the adaptive elimination and addition of grid nodes. This algorithm is compared to one proposed earlier that adaptively refines the grid and deletes nodes with the help of an estimate for the local effect of trial addition of new basis functions and the removal of old ones. Results are presented from numerical experiments aimed at assessing the performance of the proposed algorithm on a singularly perturbed model problem with a smooth solution.     

A Program for Computing the Cohomology of Lie Superalgebras of Vector Fields

The cohomology of Lie (super)algebras has many important applications in mathematics and physics. At present, since the required algebraic computations are very tedious, the cohomology is explicitly computed only in a few cases for different classes of Lie (super)algebras. That is why application of computer algebra methods is important for this problem. We describe an algorithm (and its C implementation) for computing the cohomology of Lie algebras and superalgebras. In elaborating the algorithm, we focused mainly on the cohomology with coefficients in trivial, adjoint, and coadjoint modules for Lie (super)algebras of the formal vector fields. These algebras have many applications to modern supersymmetric models of theoretical and mathematical physics. As an example, we consider the cohomology of the Poisson algebra Po(2) with coefficients in the trivial module and present 3- and 5-cocycles found by a computer. Bibliography: 6 titles.     

Ascending bid auctions with behaviorally consistent bidders

Decision makers whose preferences do not satisfy the independence axiom of expected utility theory, when faced with sequential decisions will act in a dynamically inconsistent manner. In order to avoid this inconsistency and maintain nonexpected utility, we suggest the idea of behavioral consistency. We implement this notion by regarding the same decision maker at different decision nodes as different agents, and then taking the Bayesian — Nash equilibrium of this game. This idea is applied to a finite ascending bid auction game. We show the condition for the existence of an equilibrium of this game, and we also characterize the equilibrium in those cases when it exists. In particular, when the utility functionals are both quasi-concave and quasi-convex, then there is an equilibrium in dominant strategies where each bidder continues to bid if and only if the prevailing price is smaller than his value. In the case of quasi-concavity it is shown that, in equilibrium, each bidder has a value such that he continues with positive probability up to it, and withdraws after that.This research was supported by the NSF under Grant No. SES87-08360. We would like to thank Professor Irving H. LaValle for his helpful suggestions.     

Activity graphs: A language for flexible consultation systems

An intelligent decision system (IDS) uses artificial intelligence principles to deliver automated, interactive decision analysis (DA) consultations. Network methods adapted from operations research underlie two key IDS components: influence diagrams and activity graphs. Influence diagrams, which are familiar to DA researchers and practitioners, represent decision problems inevent space. Activity graphs, which are introduced in this paper, represent processes inaction space. While activity graphs can represent any process, we use them as a knowledge-engineering and programming language to represent the process knowledge of skilled decision analysts in the context of a specific class of decisions. This paper defines activity graphs as an extension of directed AND-OR graphs. Anactivity tree is a directed AND-OR tree consisting of nodes, which may contain activities (small computer programs) and connectors that establish logical relationships among nodes and define logical resolution agendas. Anactivity graph is a directed, multiply connected network of activity trees. Activity graphs may involve recursion. Development of the activity graph language is motivated by our desire to enable professional decision analysts — or other experts — with limited advanced programming experience to design and build consultation systems that combine the guidance offered by protocol systems with the flexibility and generality of transaction systems. This paper defines the activity graph language in detail. A simple example illustrates key concepts. The paper also discusses our experience using a computer system that implements activity graphs for developing commercial IDSs.     

An efficient label setting/correcting shortest path algorithm

We design a new label shortest path algorithm by applying the concept of a pseudo permanent label. This approach allows an algorithm to partition the set of nodes into two new sets: pseudo permanently labeled nodes and its complementary set. From this point of view, this new label method can be considered as a label setting method. Moreover, at least one node becomes permanently labeled when some nodes which belong to the set of pseudo permanently labeled nodes are scanned in each iteration of the algorithm. In the case of networks with non-negative length arcs it is easy to prove that this node has the minimum distance label among the non-pseudo permanently labeled nodes. On the other hand, it is not known during the computation which pseudo permanently labeled nodes are permanently labeled. Therefore, all distance labels are temporary and the algorithm becomes a label correcting method. Nevertheless, the proposed algorithm exhibits some nice features, such as: (1) the time bound for the running of the algorithm for a network with n nodes and m arcs is O(nm); (2) the number of node scan operations in the algorithm is less than the number of these operations in the previous label correcting algorithms as is observed in the computational experience; (3) the algorithm incorporates two new rules which allow easy detection of a negative cycle in the network; (4) the algorithm is quite simple and very easy to implement, and does not require sophisticated data structures; (5) the algorithm exhibits flexibility in the order in which the new pseudo permanently labeled nodes are scanned. The above features are possible through the application of the pseudo permanent label concept.     

基于Erdos1合著网络和论文引用网络的复杂网络节点特征研究

首先,研究了Erdos1合著网络的特征属性,一方面使用节点的度、介数、接近中心性来描述Erdos1合著网络节点重要性,另一方面使用特征向量中心性和本文提出的高阶度参数来描述Erdos1合著网络节点影响力;然后,分别用逼近理想解的排序算法（TOPSIS算法）和主成份分析（PCA）对节点重要性和影响力排序;最后,利用修改的网页排名算法（PageRank算法）讨论了网络科学原创性论文中最具影响力的论文。     

具有超图合作结构的赋权Position值

具有超图交流结构的可转移效用合作对策,也称为超图对策,它由一个三元组(N,v,H)所组成,其中(N,H)是一个可转移效用对策(简称TU-对策),而(N,H)是一个超图(超网络)。在超图对策中,除Myerson值(Myerson)外,Position值(Meessen)是另一个重要的分配规则。该模型要求把超图结构中每条超边Shapley的值平均分配给它所包含的点,而不考虑每个点的交流能力或合作水平。本文引入超图结构中点的度值来度量每条超边中每个点的交流能力或合作水平,并结合Haeringer提出用于推广Shapley值的权重系统,并由此定义了具有超图合作结构的赋权Position值。我们证明了具有超图合作结构的赋权Position值可以由“分支有效性”、“冗余超边性”、“超边可分解性”、“拟可加性”、“弱积极性”和“弱能转换”六个性质所唯一确定,并且发现参与者获得的支付随其度值的增加而增加,参与者分摊的成本随其度值的增加而降低。