Switching defence for switched systems under malicious attacks: A Stackelberg game approach

The security issue of switched systems is researched from a noncooperative dynamic game-theoretic perspective in this paper. A Stackelberg game is developed for the switched autonomous system suffering malicious attacks, and the Stackelberg equilibrium switching and attack strategies are constructed respectively. Afterwards, the proposed game-theoretic approach is extended to switched control systems. A Stackelberg Nash game is consequently established to characterize the hierarchical decision making processes, where the controller and the attacker are the followers who simultaneously make their own decisions, and a sufficient condition is provided for the construction of the Stackelberg Nash equilibrium. Finally, a continuous stirred tank reactor is exploited to validate the effectiveness and applicability of the proposed results.     

Local decision making and decision fusion in hierarchical levels

Hierarchical problem solving is preferred when the problem is overwhelmingly complicated. In such a case, the problem should better be analyzed in hierarchical levels. At each level, some temporary solutions are obtained; then a suitable decision fusion technique is used to merge the temporary solutions for the next level. The hierarchical framework proposed in this study depends on reutilization or elimination of previous level local agents that together perform the decisions due to a decision-fusion technique: a performance criterion is set for local agents. The criterion checks the success of agents in their local regions. An agent satisfying this criterion is reutilized in the next level, whereas an agent not successful enough is removed from the agent pool in the next level. In place of a removed agent, a number of new local agents are developed. This framework is applied on a fault detection problem.      

Phenomena in Inverse Stackelberg Games,Part 2: Dynamic Problems

Games are considered in which the role of the players is a hierarchical one. Some players behave as leaders, others as followers. Such games are named after Stackelberg. In the current paper, a special type of these games is considered, known in the literature as inverse Stackelberg games. In such games, the leader (or: leaders) announces his strategy as a mapping from the follower (or: followers) decision space into his own decision space. Arguments for studying such problems are given. The routine way of analysis, leading to a study of composed functions, is not very fruitful. Other approaches are given, mainly by studying specific examples. Phenomena in problems with more than one leader and/or follower are studied within the context of the inverse Stackelberg concept. As a side issue, expressions like “two captains on a ship” and “divide and conquer” are given a mathematical foundation.     

A Model for Multi-timescaled Sequential Decision-making Processes with Adversary

Extending the multi-timescale model proposed by the author et al. in the context of Markov decision processes, this paper proposes a simple analytical model called M timescale two-person zero-sum Markov Games (MMGs) for hierarchically structured sequential decision-making processes in two players' competitive situations where one player (the minimizer) wishes to minimize their cost that will be paid to the adversary (the maximizer). In this hierarchical model, for each player, decisions in each level in the M-level hierarchy are made in M different discrete timescales and the state space and the control space of each level in the hierarchy are non-overlapping with those of the other levels, respectively, and the hierarchy is structured in a "pyramid" sense such that a decision made at level m (slower timescale) state and/or the state will affect the evolutionary decision making process of the lower-level m+1 (faster timescale) until a new decision is made at the higher level but the lower-level decisions themselves do not affect the transition dynamics of higher levels. The performance produced by the lower-level decisions will affect the higher level decisions for each player. A hierarchical objective function for the minimizer and the maximizer is defined, and from this we define "multi-level equilibrium value function" and derive a "multi-level equilibrium equation". We also discuss how to solve hierarchical games exactly.     

Phenomena in Inverse Stackelberg Games,Part 1: Static Problems

Games are considered in which the role of the players is a hierarchical one. Some players behave as leaders, others as followers. Such games are named after Stackelberg. In the current paper, a special type of these games is considered, known in the literature as inverse Stackelberg games. In such games, the leader (or: leaders) announces his strategy as a mapping from the follower (or: followers) decision space into his own decision space. Arguments for studying such problems are given. The routine way of analysis, leading to a study of composed functions, is not very fruitful. Other approaches are given, mainly by studying specific examples. Phenomena in problems with more than one leader and/or follower are studied within the context of the inverse Stackelberg concept. As a side issue, expressions like “two captains on a ship” and “divide and conquer” are given a mathematical foundation.     

Two Game Models for Cooperation with Implicit Noncooperation

We propose two flexible game models to represent and analyze cases that cannot be modeled by current game models. One is called sharing creditability game (SCG) and the other is called bottomline game (BLG). The new models transform cooperative games into new games that incorporate auxiliary information (noncooperative in nature) usually neglected in previous theories. The new games will be solved only by traditional noncooperative game theory. When the new solutions are applied to the original games, the solutions can reflect the auxiliary information in addition to the original objectives of the decision makers or players. Generally, the new solutions are different from the cooperative and the noncooperative solutions of the original games. Existing transferable utility (TU) games and noncooperative games will coincide with special cases of the two new game models. Using SCG and BLG, the prisoner’s dilemma can be reformulated and a richer set of decisions can be considered for the players. The two new game models have potential applications in military and socioeconomic situations.This research was partly funded by the College Engineering, Ohio State University.     

Stackelberg solutions for fuzzy random two-level linear programming through probability maximization with possibility

This paper considers Stackelberg solutions for decision making problems in hierarchical organizations under fuzzy random environments. Taking into account vagueness of judgments of decision makers, fuzzy goals are introduced into the formulated fuzzy random two-level linear programming problems. On the basis of the possibility and necessity measures that each objective function fulfills the corresponding fuzzy goal, together with the introduction of probability maximization criterion in stochastic programming, we propose new two-level fuzzy random decision making models which maximize the probabilities that the degrees of possibility and necessity are greater than or equal to certain values. Through the proposed models, it is shown that the original two-level linear programming problems with fuzzy random variables can be transformed into deterministic two-level linear fractional programming problems. For the transformed problems, extended concepts of Stackelberg solutions are defined and computational methods are also presented. A numerical example is provided to illustrate the proposed methods.     

A survey of quantum-like approaches to decision making and cognition

There has always been a steady interest in how humans make decisions amongst researchers from various fields. Based on this interest, many approaches such as rational choice theory or expected utility hypothesis have been proposed. Although these approaches provide a suitable ground for modeling the decision making process of humans, they are unable to explain the corresponding irrationalities and existing paradoxes and fallacies. Recently, a new formulation of decision theory that can correctly describe these paradoxes and possibly provide a unified and general theory of decision making has been proposed. This new formulation is founded based on the application of the mathematical structure of quantum theory to the fields of human decision making and cognition. It is shown that by applying these quantum-like models, one can better describe the uncertainty, ambiguity, emotions and risks involved in the human decision making process. Even in computational environments, an agent that follows the correct patterns of human decision making will have a better functionality in performing its role as a proxy for a real user. In this paper, we present a comprehensive survey of the researches and the corresponding recent developments. Finally, the benefits of leveraging the quantum-like modeling approaches in computational domains and the existing challenges and limitations currently facing the field are discussed.     

Unifying temporal and organizational scales in multiscale decision-making

In enterprise systems, making decisions is a complex task for agents at all levels of the organizational hierarchy. To calculate an optimal course of action, an agent has to include uncertainties and the anticipated decisions of other agents, recognizing that they also engage in a stochastic, game-theoretic reasoning process. Furthermore, higher-level agents seek to align the interests of their subordinates by providing incentives. Incentive-giving and receiving agents need to include the effect of the incentive on their payoffs in the optimal strategy calculations. In this paper, we present a multiscale decision-making model that accounts for uncertainties and organizational interdependencies over time. Multiscale decision-making combines stochastic games with hierarchical Markov decision processes to model and solve multi-organizational-scale and multi-time-scale problems. This is the first model that unifies the organizational and temporal scales and can solve a 3-agent, 3-period problem. Solutions can be derived as analytic equations with low computational effort. We apply the model to a service enterprise challenge that illustrates the applicability and relevance of the model. This paper makes an important contribution to the foundation of multiscale decision theory and represents a key step towards solving the general X-agent, T-period problem.     

低碳供应链纵向减排合作下的政府补贴策略

为了研究低碳供应链减排合作的政府补贴问题,分别构建了在纳什博弈、斯塔克尔伯格博弈和供应链集中决策三种不同的博弈关系下,政府对制造商和零售商合作减排投入进行补贴的博弈模型。分析了企业的最优减排成本投入和政府的最优补贴率,并进一步讨论了政府补贴行为和企业选择减排合作行为之间的互动博弈。研究显示:政府提供的减排补贴和企业的收益随着供应链上下游合作的紧密程度不同而不同,在纳什博弈下最高,在集中决策下最低,斯塔克尔伯格博弈下介于两者之间。比较了力量不对等供应链中企业分别处于领导者和跟随者时获得的政府补贴的差异。同时在政府补贴行为和供应链企业合作行为的交互博弈中,政府的最优策略是不对任何一方实行减排补贴。     

Scheduling and control,performance measures and discrete event simulation

There are a growing number of finite capacity scheduling tools which are in use and available to industry. To be fully effective, a scheduling system needs to be incorporated within a control methodology which enables shop-floor performance to be analysed and, when necessary, corrective actions to be formulated. Manufacturing system performance, however, is not only dependent upon short-term planning decisions, but is also constrained by the capability for which it is designed. Hence, any control methodology should be based on a consistent set of performance measures and well defined procedures which help to integrate decision making at all levels in the order fulfilment process. The paper examines the distinctive roles of process optimisation and process control in the decision hierarchy; it classifies a set of measures of performance and other system variables according to the functions they serve in manufacturing control, and suggests a hierarchical process control methodology. The role of discrete event simulation techniques in linking decisions on manufacturing system design and production planning/control is also discussed.     

Complexity analysis of dynamic noncooperative game models for closed-loop supply chain with product recovery

In this paper, we consider a closed-loop supply chain (CLSC) with product recovery, which is composed of one manufacturer and one retailer. The retailer is in charge of recollecting and the manufacturer is responsible for product recovery. The system can be regarded as a coupling dynamics of the forward and reverse supply chain. Under different decision criteria, two noncooperative game models: Stackelberg game model and peer-to-peer game model are developed. The dynamic phenomena, such as the bifurcation, chaos and sensitivity to initial values are analyzed through bifurcation diagrams and the largest Lyapunov exponent (LLE). The influences of decision parameters on the complex nonlinear dynamics behaviors of the two models are further analyzed by comparing parameter basin plots, and the results show that with the improvement of retailer’s competitive position, the CLSC system will be more easier to enter into chaos.     

Confidence structures in decision making

Decision making is defined in terms of four elements: the set of decisions, the set of outcomes for each decision, a set-valued criterion function, and the decision maker's value judgment for each outcome. Various confidence structures are defined, which give the decision maker's confidence of a given decision leading to a particular outcome. The relation of certain confidence structures to Bayesian decision making and to membership functions in fuzzy set theory is established. A number of schemes are discussed for arriving atbest decisions, and some new types of domination structures are introduced.This research was partly supported by Project No. NR-047-021, ONR Contract No. N-00014-75-C-0569 with the Center for Cybernetic Studies, The University of Texas, Austin, Texas, and by ONR Contract No. N-00014-69-A-0200-1012 with the University of California, Berkeley, California.     

Equilibrium analysis of supply chain structures under power imbalance

This paper investigates the implications of channel power on supply chain stability in a setting where multiple suppliers sell substitutable products through a common retailer. Such supply chains have been traditionally analyzed as one- or two-stage Stackelberg non-cooperative games with all suppliers sharing balanced (equal) decision-making power. In this paper, we relax this assumption and formulate game-theoretic models to examine scenarios where one supplier can act as the Stackelberg leader. Consequently, we analyze new supply chain structures and introduce the notion of structure dominance, a novel approach to analyze the performance of supply chains that has practical implications. Thus, a decision maker can employ the concepts of structure dominance to determine whether there exist supply chain scenarios that are more stable than others, i.e., less prone to power reconfigurations, at both agent and group level. We find that power imbalance causes significant declines in supply chain profits, and the more balanced the agents are the higher their profits when demand is linear, regardless of product competition. It develops that neither the Manufacturer Stackelberg nor the Retailer Stackelberg supply chains are stable structures in our generalized setting, but that structures where power is equally split between agents provide for best stability and performance.     

基于多Agent的车间任务招投标粗模糊集建模研究

首先通过相识集、招标集、投标集和任务集的概念 ,描述制造执行系统中的调度 Agent与资源 A-gent间任务招投标过程模型 ;基于任务的属性和资源 Agent完成任务的成本、质量、负荷和时间等属性 ,定义论域上的模糊集 ,将模糊集中的隶属度函数作为粗集的属性 ,在模糊集上作截集 ,从而获得系统的分类知识 ;收集样本数据 ,构造并分析决策表 ,进而获得调度 Agent调度决策知识 ;应用调度知识进行推理 ,从争取获得招标任务的若干个资源 Agent中 ,选出最适合招标任务的中标者 .     

Multicriteria sorting using a valued indifference relation under a preference disaggregation paradigm

Most multicriteria decision methods need the definition of a significant amount of preferential information from a decision agent. The preference disaggregation analysis paradigm infers the model’s parameter values from holistic judgments provided by a decision agent. Here, a new method for inferring the parameters of a fuzzy outranking model for multicriteria sorting is proposed. This approach allows us to use most of the preferential information contained in a reference set. The central idea is to characterize the quality of the model by measuring discrepancies and concordances amongst (i) the preference relations derived from the outranking model, and (ii) the preferential information contained in the reference set. The model’s parameters are inferred from a multiobjective optimization problem, according to some additional preferential information from a decision agent. Once the model has been fitted, sorting decisions about new objects are performed by using a fuzzy indifference relation. This proposal performs very well in some examples.     

Neural nets in a group decision process

In recent years there has been some interest in applying Artificial Adaptive Agents (AAA) to the study of complex adaptive systems, especially economic systems. Neural networks are frequently employed as AAA. Artificial neural nets mimic certain aspects of the physical structure and information processing of the human brain and their most attractive characteristic is their ability to learn a pattern from a given set of examples. In this study, we investigated the ability of neural nets to model human behavior in a group decision process. The context was a market entry game with a linear payoff function and binary decisions. The players had to decide, for each trial, whether or not to enter a market whose capacity is public knowledge. Human behavior in this situation has been modeled and empirically validated by the Nash equilibrium for noncooperative n-person games. A simulation of the game was performed with neural nets instead of human subjects. The nets were trained using the results of the games in which they participated. The simulation with groups of neural nets exhibits phenomena very similar to those observed in groups of human players. Received February 2000     

Heterogeneous trading agents

In this article, we present a multiagent system (MAS) simulation of a financial market and investigate the requirements to obtain realistic data. The model consists of autonomous, interactive agents that buy stock on a financial market. Transaction decisions are based on a number of individual and collective elements, the former being risk aversion and a set of decision rules reflecting their anticipation of the future evolution of prices and dividends and the latter the information arriving on the market influencing the decision making process of each trader. We specifically look at this process and the following observations hold: The market behavior is determined by the information arriving at the market and agent heterogeneity is required in order to obtain the right statistical properties of the price and return time series. The observed results are not sensitive to changes in the parameter values. © 2003 Wiley Periodicals, Inc.     

Supply chain coordination with downstream operating costs: Coordination and investment to improve downstream operating efficiency

There are many conceptualizations and formalizations of decision making. In this paper we compare classical decision theory with qualitative decision theory, knowledge-based systems and belief–desire–intention models developed in artificial intelligence and agent theory. They all contain representations of information and motivation. Examples of informational attitudes are probability distributions, qualitative abstractions of probabilities, knowledge, and beliefs. Examples of motivational attitudes are utility functions, qualitative abstractions of utilities, goals, and desires. Each of them encodes a set of alternatives to be chosen from. This ranges from a small predetermined set, a set of decision variables, through logical formulas, to branches of a tree representing events through time. Moreover, they have a way of formulating how a decision is made. Classical and qualitative decision theory focus on the optimal decisions represented by a decision rule. Knowledge-based systems and belief–desire–intention models focus on an alternative conceptualization to formalize decision making, inspired by cognitive notions like belief, desire, goal and intention. Relations among these concepts express an agent type, which constrains the deliberation process. We also consider the relation between decision processes and intentions, and the relation between game theory and norms and commitments.