Multi-player and multi-round auctions with severely bounded communication. ESA

采购管理理论综述作者：敬辉蓉， 李传昭， Gou Hui-rong， Li Chuan-zhao作者单位：敬辉蓉,Gou Hui-rong(重庆大学经济与工商管理学院,重庆400044;重庆师范大学旅游学院,重庆400047)， 李传昭,Li Chuan-zhao(重庆大学经济与工商管理学院,重庆,400044)刊名：工业工程英文刊名：INDUSTRIAL ENGINEERING JOURNAL年，卷(期)：2008,11(2)被引用次数：3次1.Harris F W Operations and Cost-factory management series 19152.Inman R A Quality certification of suppliers by JIT manufacturers 1990(02)3.Jahnukainen J;Lahti M Efficient purchasing in make-to-order supply chains[外文期刊] 1999(59)4.Fiorito Susan S;Giunipero Larry C;He Yan Retail buyers' perceptions of quick response systems 1998(06)5.Wu Min;Low SOi Pheng Economic order quantity (EOQ)versus just-in-time (JIT) purchasing:an ahemative analysis in the ready-mixed concrete industry[外文期刊] 2005(23)6.Niall Waters-Fuller Just-in-time purchasing and supply:a review of the literature 1995(09)7.Ganesh lyer;J Miguel Villas-Boas A bargaining Theory of distribution channels 2003(XI)8.Weng Z Kevin Channel coordination and quantity discounts 1995(09)9.thing-Hua;Chen-Ritzo;Terry P Harrison Better,faster,cheaper:an experimental analysis of a multiattribute reverse auction mechanism with restricted infor mation feedback[外文期刊] 2005(12)10.Leandro Arezamena;Estelle Cantillon Investment ineenfives in procurement auctions 2004(71)11.David C Parkes;Jayant Kalagnanam Model for iterative multi-attribute procurement auctions[外文期刊] 2005(03)12.Yossi Sheffi Combinatorial auctions in the procurement of transportation services[外文期刊] 2004(04)13.Rachel R Chen;Robin O Roundy;Rachel Q Zhang Efficient auction mechanisms for supply chain procurement[外文期刊] 2005(03)14.Hariga M;Haouari M An EOQ lot sizing model with random supplier capacity[外文期刊] 1999(01)15.Silver E A;Pyke D F;Peterson R Inventory management and production planning and scheduling 199816.Khouja M The single period (news-vendor) inventory problem:a literature review and suggestions for future research 1999(27)17.Willis T;Hnston C Vendor requirements and evualation in a JIT environment 1990(04)18.Naumann E;Reck R A buyer's bases of power 1982(04)19.Alan Smart;Alan Harrison Reverse auctions as a support mechanism in flexible supply chain2002(03)20.John G Riley;William F Samuelson Optimal auction 1981(71)21.Roger B Myerson Optimal auction design[外文期刊] 1981(06)22.Vickrey,William Counterspeculation,auctions,and competitive sealed tenders 1961(16)23.Yen Benjamin P-C;Ng Elsie O S The impact of electronic commerce on procurement[外文期刊] 2003(3-4)24.Monaban J P A quantity discount pricing model to increase vendor profits 1984(30)25.Susan A Shaw;Juliette Gibbs Procurement strategies of small retailers faced with uncertainty:an analysis of channel choice and behavior[外文期刊] 1999(01)26.Robert A Novack;Stephen W Simco The industrial procurement process:a supply chain perspective 1991(01)27.Dr David R Rink;Dr Harold W Fox Using the product life cycle concept to formulate actionable purchasing strategies 2003(02)28.Hammer Michael;Champy James Reengineering the corporation,a manifesto for business revolution 199329.Sridhar K Moorthy Managing channel profits:comment 1987(04)30.Jeuland A;Shugan S Managing channel profits 1983(02)31.James M Wilson A simulation analysis of ordering policies under inflationary conditions:a critique 1995(08)32.Wirth Andrew Technical paper:inventory control and inflation:a review 1989(01)33.Chang P L;Lin C T On the effect of centralization of the expected costs in a multi-location newsboy problem 1991(42)u A;Lau H The newsstand problem:a capaeitated multiproduct single period inventory problem 1996(94)35.Gullu R;Onol E;Erkip N Analysis of an inventory system under supply uncertainty 1999(59)36.Burton T T JIT/repetitive sourcing strategies:tying the knot with your suppiers 1988(04)1.周虹基于Web Services的物资采购管理系统的研究与应用[期刊论文]-中国科技信息 2010(21)2.王正武.黄巍物资信息的流转模式浅析[期刊论文]-兵工自动化 2009(2)3.周永宏绿色供应链条件下的企业采购管理[期刊论文]-科技资讯 2008(20)本文链接：/Periodical_gygc200802001.aspx。

欲善其事必先利其器拍卖行插件Auctioneer教程Autioneer Advanced 101Auctioneer完全教程——教你怎样用Auctioneer赚钱而不是去Farming目录第一章Auctioneer介绍第二章使用简单拍卖出售第三章使用估价师第四章高级估价师：批出售和价格匹配(即自动削价)设置第五章使用Search界面和Auctioneer工具第六章使用Search界面中的查询器和过滤器第七章使用BeanCounter来跟踪你的效率第八章关于Auctioneer工具深入过程的建议方案附录：反馈和进一步帮助第一章Auctioneer介绍Auctioneer是什么那？它是一个在Wow游戏中扩占拍卖厅功能的用户界面插件。

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这个插件帮助人们在游戏中赚取金币的主要方法是为你收集数据信息并分析这些数据。

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Auctioneer能够对比拍卖物品的热销价格和NPC的销售价甚至能够对比一个物品附魔分解以后的潜在价格，这样你要在拍卖行买东西这方面得到更多的金币，分解它或者卖给NPC。

下载和安装Auctioneer现在让我们开始下载并安装这个插件，Auctioneer这个插件主要有两种版本。

第一：Auctineer套件完全版，这个版本包含全部有趣且高级的Auctioneer组件，比如定价的估价师组件和能发现可以购买并以一定利润转售的物品的SearchUI界面组件。

第二：基本的Auctioneer插件精简版。

它仅包含基本定价和出售功能，这个简单版对那种仅仅出售自己拾取、收集、制造的物品人很有用。

Exploring auction mechanisms for role assignment inteams of autonomous robotsVanessa Frias-Martinez1,Elizabeth Sklar1,and Simon Parsons21Department of Computer ScienceColumbia University1214Amsterdam Avenue,New York,NY10027,USAvf2001,sklar@2Department of Computer and Information ScienceBrooklyn College,City University of New York2900Bedford Avenue,Brooklyn,NY11210,USAparsons@Abstract.We are exploring the use of auction mechanisms to assign roles withina team of agents operating in a dynamic environment.Depending on the degreeof collaboration between the agents and the specific auction policies employed,we can obtain varying combinations of role assignments that can affect both thespeed and the quality of task execution.In order to examine this extremely largeset of combinations,we have developed a theoretical framework and an envi-ronment in which to experiment and evaluate the various options in policies andlevels of collaboration.This paper describes our framework and experimentalenvironment.We present results from examining a set of representative policieswithin our test domain—a high-level simulation of the RoboCup four-leggedleague soccer environment.1IntroductionMulti agent research has recently made significant progress in constructing teams of agents that act autonomously in the pursuit of common goals[12,15].In a multi agent team,each agent can function independently or can communicate and collaborate with its teammates.When collaborating,the notion of role assignment is used as a means of distributing tasks amongst team members by associating certain tasks with particular roles.The assignment of roles can be determined a priori or can change dynamically during the course of team operation.Collaboration enables a team of agents to work together to address problems of greater complexity than those addressed by agents operating independently.In general, using multiple robots is often suggested to have several advantages over using a single robot[4,7].For example,[11]describes how a group of robots can perform a set of tasks better than a single robot.Furthermore,a team of robots can localize themselves better when they share information about their environment[7].But collaboration in a team of robots may also add undesirable delays through the communication of information between the agents.We are exploring—within dynamic,multi-robot environments—the use of auction mechanisms to assign roles to agents dynamically and the effect of different approachesto collaboration within the team.In order to evaluate this set,we have developed a theoretical framework and a simulation environment.The theoretical framework helps us to identify the space of possibilities,and the simulation environment helps us to evaluate the various degrees of collaboration.This paper begins by highlighting some background material on auctions and the use of auction mechanisms in multi agent systems.Then we describe our theoretical framework.Next we detail our experimental environment—a high-level simulation of the RoboCup Four-Legged Soccer League.We then present results of simulation experiments evaluating both collaborative and non-collaborative models of information sharing as well as various auction policies.Finally,we close with a brief discussion and directions for future work.2AuctionsFollowing Friedman[9],we can consider an auction to be a mechanism that regulates how commodities are exchanged by agents operating in a multi agent environment.An auction mechanism defines how the exchange takes place.It does this by laying down rules about what the traders can do—what messages they can exchange in an interac-tion—and rules for how the allocation of commodities is made given the actions of the traders.Auctions have been used in different environments for resource allocation, such as electronic institutions[6],distributed planning of routes[13]or assignment of roles to a set of robots to complete a common task[10].3Theoretical frameworkIn our auction,there are two types of agents:the auctioneer and the trader—a player in the RoboCup soccer game.The player makes an offer and the auctioneer’s job is to coordinate the offers from all the players and perform role assignment.There arefive main components to our model.First,we define R to be the set of possible roles:R={P A,OS,DS},where P A is a primary attacker,OS is an offensive supporter,and DS is a defensive supporter. Note that the goalie is not considered a role to be assigned in this manner,since it cannot change during the course of the game.Next,we define P to be a set of player attributes:P={d ball,d goals,d mates,d opps} where d ball contains the distance from the player(who is making the offer)to the ball;d goals contains the distance from the player to each goal;d mates contains the distance from the player to each of its teammates;and d opps contains the distance from the player to each player on the opposing team.Third,we define F to be a set of functions which define the method for sharing per-ception information between agents.This information could be shared with teammates, the auctioneer,or both.Fourth,we define M to be a matching function,the method used by the auctioneer for clearing the auction,i.e.,matching the offers with roles.In other words,the matching function captures the coordination strategy.Finally,we define an auction,A,to be:A= P,R,M,f where P⊆P and P=∅;R⊆R and R=∅; M⊆M and M=∅;and f∈F.Our work is systematically exploring the space of all possible auctions P×R×M×F.B denotes the set of possible types of offers in a particular auction,A∈A: B={r,w}where:r⊆R is a set of roles for which the player bids;w is a set of real-valued weights,one weight corresponding to each of the roles in r(a weight of0 means that the player is not interested in making an offer for the corresponding role);and f(p),p⊆P,is the mechanism by which perceptual data is used to determine r and w.To date,we have defined two different types of auctions within this framework—a simple auction[5]and a combinatorial auction[3].We can define a simple auctionb t∈B as:b t={r,w},where the role r and w are singletons(unique offer).And a combinatorial auction,is defined as:b t={(r0,r1,r2),(w0,w1,w2)}where r i and w j are ing different combinations of weights allows the agent to bid for different combinations of roles,and this makes the auction combinatorial[1].4SimRob:our Simulated Approach to a RoboCup GameWe are using RePast[14]to implement our environment.RePast allows us to build a simulation as a state machine in which all the changes to the state machine occur through a schedule.In order to model a RoboCup soccer game in RePast,we need to define the agents,the environment and the state machine that RePast will execute at each scheduled tick,i.e.,simulated time step.4.1Agent parametersThe RoboCup Four-Legged League environment has four Sony AIBO robots per team and a bright orange ball.Each one of the robotic agents is associated with an array containing the values that define their perception and localization:(x,y,φ,d ball,d goals,d opps,d mates,b ball,b goals,b opps,b mates)(1) where(x,y)are the2D coordinates of the robot on thefield3;φis the orientation of the robot4;d ball is the distance from the robot to the ball,d goals is the distance from the robot to each goal,d opps is an array containing the distance from the robot to each opponent,and d mates is an array containing the distance from the robot to each team-mate.The boolean values in the second half of equation(1)indicate if the ball has been detected by the player(b ball),if each goal has been detected by the player(b goals),if each opponent has been detected nearby(b opps)and if each teammate has been detected nearby(b mates).4.2Simulation skeletonWe use RePast in order to simulate the development of a game with the agents.At the beginning of the simulation,we define four agents(per team)and a ball in thefield.Each of the agents is defined as explained above,by means of an array as in equation (1).The simulation run in RePast can be divided into the following steps:(1a)Generation of the agent parameters In thisfirst step,we obtain the parameters of each of the agents in thefield.The localization of the robot is expressed with the coordinates(x,y)in a2Dfield.We also obtain the distances to the ball d ball,to the goal d goals and to the opponents d opps.(1b)Amount of information shared by the agents The information shared by the agents is:mingoal,a boolean variable that is true when the agent is the one closest to the goal. This variable can be defined when the agents share the variable d goals among them. maxopp is a boolean variable that is true when the agent is farthest away from the opponents in thefield.This variable can be defined when the agents share d opps.And maxball is a boolean variable that is true when the agent is farthest away from the ball. This value can be defined when the variable d ball is shared among the agents.(2a)Defining a bidding policy for the agents For each simulation tick of the game play,the agent’s bid will be the role associated by the policy being tested to the set of perceptions gathered by the agent at that simulation tick.(2b)Defining an auction policy for the auctioneer The auction is responsible for dis-tributing the roles between the agents on thefield.The auctioneer will go through the different roles in the bid until one of the roles in the array is assigned to the agent, meaning that the bid is won.(3)Game Play Once the agent-roles are defined,we have to actually simulate the joint task to be developed by the agents.As stated before,our aim is that of simulating a soccer game.The game model is very simple.Each role has a state graph that will output a certain behavior depending on the perceptions gathered by the agent:–PA B EHAVIOR:If the agent sees the goal and the ball,then it kicks the ball,other-wise it turns to look for the ball without losing track of the goal.–OS B EHAVIOR:If the ball is seen,the agent kicks it.–DS B EHAVIOR:If the ball is seen,the agent follows it in order to prevent an agent from the opposing team scoring.Finally,if a goal is scored,the robots are sent back to their initial positions and the ball randomly changes location.Then,the three step(parameter generation,auction execution and game play)simulation is run again.5ExperimentsThis section describes our experimental work to date.We have started to explore the range of possible auctions and their effect on the coordination of a team,as measured by their performance in simulated games.We have experimented with four very sim-ple types of coordination and describe policies that we have used for experimentation, chosen somewhat ad hoc.In current work,we are learning policies[8].Table1.Example non-collaborative simple(S)and combinatorial(C)auctionsBall seen Mate seen Role(C)0DS01[OS,.7,DS,.2,PA,.1]1DS01[OS,.7,DS,.2,PA,.1]0DS11[OS,.7,DS,.2,PA,.1]1OS11[OS,.7,DS,.2,PA,.1]5.1Non-collaborative simple auctionThis approach defines a team of agents that don’t share any perception data.Hence, each one relies on the information that it gathers independently of the others.The offers made by the agents follow the policy in Table1column Role(S).This shows that we have defined the agent to offer to be OS when both ball and opponent are seen.In any other case,our agent will offer to be DS.We have chosen a simple matching policy that just associates afixed role to each of the possible sets of perceptions.5.2Non-collaborative combinatorial auctionIn this case there is still no sharing of perception,but the bid now contains a vector defining the agent’s role preferences For our experiments,we have defined two differ-ent bidding policies.The offensive policy,defined in Table1,column Role(C),repre-sents a team with an attacking approach,always looking for the goal and aiming to score.The other policy is more defensive.The offensive policy assigns the array of roles[DS,.7,OS,.2,PA,.1]to each of the agents.The matching is the same as before.5.3Collaborative simple auctionIn this case,the agents share all the perception data.Hence,when defining the bids,we can also share the three variables related to the minimum and maximum distances to the ball,opponents and goal.The table defining the bidding policy is huge.In Table2,col-umn Role(S),we show a few lines to give the sense of it,but it is deliberately similar to the policy for the non-collaborative auction to give a reasonable comparison.When no elements are seen by any of the agents,the agent bids for the role DS.When everything is seen and the distances are minimum,the agents bid to be OS.The matching policy is also the same as for the non-collaborative examples.5.4Collaborative combinatorial auctionHere the bidding Table2,column Role(C),is similar to the previous one,but contains a vector of bids and weights instead of only one role,and this vector is like that for theTable2.Collaborative simple(S)and combinatorial(C)auctions Ball seen Mate seen MaxOpp Role(S) 000[DS]000[DS]......111[OS]defensive offensivebid bidnoncollab simple16–4330collab simple40–3778ticks (time)g o a l s s c o r e d(a)unique matching policy (b)non-unique matching policyFig.1.Goals scored over the course of a gamesociated with the acceptance of the bids made by an agent.The higher the ratio,the more times its bid has been accepted.In the not uniqueness experiments,we obtained very low ratios,meaning that the agents almost never won a bid,and so,the roles were distributed randomly.6Conclusions and Future workThis paper has described our preliminary work in exploring the use of auction mecha-nisms to coordinate players on a RoboCup team.While this work is only just beginning,we believe that the results demonstrate the potential of the approach to capture a wide range of types of coordination,and to be able to demonstrate their effectiveness through simulation.In addition,this approach makes it simple to explore more complex,and po-tentially more flexible,kinds of role allocation than have been previously used in the legged-league,for example [2,16].Our longterm work is to build on this foundation and explore a wide range of pos-sible auctions through simulation and on real (physical)robots.We are currently using learning techniques to automatically explore the space of auctions.We further intend to implement the most effective bidding and matching policies developed on our real Legged-League team.7AcknowlegementsThis work was made possible by funding from NSF #REC-02-19347and NSF #IIS 0329037.References1.C.Boutilier and H.H.Hoos.Bidding languages for combinatorial auctions.In Proceedingsof the17th International Joint Conference on Artificial Intelligence,pages1211–1217,San Francisco,CA,2001.Morgan Kaufmann.2.D.Cohen,Y.Hua,and P.Vernaza.The University of Pennsylvania Robocup2003LeggedSoccer Team.In Proceedings of the RoboCup Symposium,2003.3.S.de Vries and R.V binatorial auctions:A RMS Journal of Comput-ing,(to appear).4.G.Dudek,M.Jenkin,E.Emilios,and D.Wilkes.A taxonomy for multi-agent robotics.Autonomous Robots,3(4),1996.5.R.Engelbrecht-Wiggans.Auctions and bidding models:A survey.Management Science,26:119–142,1980.6.M.Esteva and J.Padget.Auctions without auctioneers:distributed auction protocols.InAgent-mediated Electronic Commerce II,LNAI1788,pages20–28.Springer-Verlag,2000.7.D.Fox,W.Burgard,H.Kruppa,and S.Thrun.Collaborative multi-robot localization.In Pro-ceedings of the23rd German Conference on Artificial Intelligence.Springer-Verlag,1999.8.V.Frias-Martinez and E.Sklar.A team-based co-evolutionary approach to multi agent learn-ing.In Proceedings of the2004AAMAS Workshop on Learning and Evolution in Agent Based Systems,2004.9.D.Friedman.The double auction institution:A survey.In D.Friedman and J.Rust,editors,The Double Auction Market:Institutions,Theories and Evidence,Santa Fe Institute Studies in the Sciences of Complexity,chapter1,pages3–25.Perseus Publishing,Cambridge,MA, 1993.10.B.Gerkey and M.Mataric.Sold!:Auction methods for multirobot coordination.IEEETransactions on Robotics and Automation,2000.11.D.Guzzoni,A.Cheyer,L.Juli,and K.Konolige.Many robots make short work.AI Maga-zine,18(1):55–64,1997.12.G.A.Kaminka,D.V.Pynadath,and M.Tambe.Monitoring deployed agent teams.In J¨o rg P.M¨u ller,Elisabeth Andre,Sandip Sen,and Claude Frasson,editors,Proceedings of the Fifth International Conference on Autonomous Agents,pages308–315.ACM Press,2001.13.T.L.Lenox,T.R.Payne,S.Hahn,M.Lewis,and K.Sycara.Agent-based aiding for indi-vidual and team planning tasks.In Proceedings of IEA2000/HFES2000Congress,2000.14.Repast..15.M.Tambe.Towardsflexible teamwork.Journal of Artificial Intelligence Research,7:83–124,1997.16.M.Veloso and S.Lenser.CMPAck-02:CMU’s Legged Robot Soccer Team.In Proceedingsof the RoboCup Symposium,2002.。

西泠印社拍卖规则英文Rules for Xiling Yinshe Auction1. Introduction:Xiling Yinshe Auction is a reputable auction organization based in China. The auction aims to provide a fair and transparent platform for buying and selling artworks, including calligraphy, paintings, and other collectibles.2. Participation:2.1 Anyone above the age of 18 and possessing legal capacity can participate in the auction.2.2 Participants must register and obtain a bidding number before the auction. Registration requires providing identification documents and completing necessary forms.3. Bidding Process:3.1 Bidding can be done in person, online, or through absentee bids.3.2 Bidders must clearly indicate the lot number, title, and their bid amount. Bids are accepted within the stated bidding intervals.3.3 The highest bidder at the conclusion of the bidding period will be the successful buyer.3.4 The auctioneer reserves the right to reject any bid deemed unreasonably low.4. Buyer's Premium:4.1 A buyer's premium is applicable to the hammer price for each lot. The premium is a percentage of the final bid price.4.2 The buyer's premium is subject to change and will be announced prior to the auction.5. Payment:5.1 Payment must be made in full within a specified timeframe after the auction.5.2 Accepted payment methods include cash, bank transfer, or other approved payment platforms.5.3 The auction house reserves the right to refuse personal checks.6. Ownership and Delivery:6.1 Ownership of the purchased lot transfers to the buyer upon successful payment.6.2 The auction house provides shipment services for purchased lots. Additional charges may apply for packaging, insurance, and transportation.6.3 The buyer is responsible for any taxes, customs duties, or other charges related to the import or export of the purchased lot.6.4 Delivery timescales are estimates and may vary depending on various factors.7. Liability and Dispute Resolution:7.1 The auction house arranges all auctions with due care and professionalism. However, it is not responsible for any errors, omissions, or inaccuracies in catalog descriptions or related information.7.2 Any disputes arising from the auction will be handled according to the laws of the jurisdiction where the auction is conducted.7.3 Participants are encouraged to clarify any doubts or seek expert advice before participating in the auction.8. Miscellaneous:8.1 The auction house reserves the right to withdraw any lot from the auction for any reason.8.2 These rules may be subject to modification or amendment, which will be announced prior to the auction.Note: The above rules are a general outline and may be subject to specific terms and conditions applicable to each individual auction conducted by Xiling Yinshe Auction. Participants are advised to review the official auction catalog and consult the auction house for the most up-to-date rules and information.。

JEL分类号JEL分类号JEL分类系统，是美国经济学会《经济文献杂志》（Journal of Economic Literature)所创立的对经济学文献的主题分类系统，并被现代西方经济学界广泛采用。

该分类方法主要采用开头的一个英文字母与随后的两位阿拉伯数字一起对经济学各部类进行“辞书式”编码分类。

目录A-经济学总论和教学B-经济学思想流派和方法论C-数理和数量方法D-微观经济学E-宏观经济学和货币经济学F- 国际经济学G-金融经济学H-公共经济学I-卫生经济学，教育经济学和福利经济学J-劳动经济学和人口经济学K-法律和经济学L-产业组织M-企业管理和商务经济学;市场营销学;会计学N-经济史O- 经济发展，技术变迁和增长P-经济体制Q-农业经济学和自然资源经济学R-城市经济学，农村经济学和区域经济学Z-其他专题A-经济学总论和教学（ A- General Economics and Teaching ）A1A1 - General EconomicsA10 - GeneralA11 - Role of Economics; Role of Economists; Market for Economists A12 - Relation of Economics to Other Disciplines A13 - Relation of Economics to Social ValuesA14 - Sociology of EconomicsA19 - OtherA2A2 - Economics Education and Teaching of EconomicsA20 - GeneralA21 - Pre-collegeA22 - UndergraduateA23 - GraduateA29 - OtherA3A3 - Multisubject Collective WorksA30 - GeneralA31 - Multisubject Collected Writings of IndividualsA32 - Multisubject VolumesA39 - OtherB-经济学思想流派和方法论( B-Schools of Economic Thought and Methodology )B0B0 - GeneralB00 - GeneralB1B1 - History of Economic Thought through 1925B10 - GeneralB11 - PreclassicalB12 - ClassicalB13 - Neoclassical through 1925B14 - Socialist; MarxistB15 - Historical; 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（转载）英语介绍⽃地主及其规则该篇⽂章转载⾃：IntroductionFight the Landlord (Dou Di Zhu) is a climbing game primarily for three players, but also playable by four. In each hand one player, the "landlord", plays alone and the others form a team. The landlord's aim is to be the first to play out all his cards in valid combinations, and the team wins if any one of them manages to play all their cards before the landlord. The game is said to have originated in Hubei province but is now popular all over China, and is also extensively played on line.Players, Cards and DealThe three-player game will be described first. The differences in the four-player game are explained near the end of the page.This game uses a 54-card pack including two jokers, red and black. The cards rank from high to low:red joker, black joker, 2, A, K, Q, J, 10, 9, 8, 7, 6, 5, 4, 3.Suits are irrelevant.As in most Chinese games the cards are not really dealt, but taken from the deck by the players.One of the players shuffles the cards, gives them to the player to his left to cut, and stacks them face down in the middle of the playing surface. One card is turned face up and inserted somewhere near the middle of the stack - this will determine who starts the auction. The dealer then draws the top card from the deck, looking at it but not showing it to the other players. The player to his right does the same, then the third player, then the dealer and so on counter-clockwise around the table until each player has 17 cards. The last three cards are left face down on the table until after the auction. It saves time if you look at your cards and sort them as you pick them up.AuctionThere is an auction to determine which player will be the landlord, and play alone against the other two. The possible bids are 1, 2 and 3. The player who drew the face up card in the "deal" is the first to bid. Each player in turn may either pass or bid higher than the highest bid so far. If everyone passes the hand is thrown in and there is a new deal. If there is a bid, the bidding continues counter-clockwise, each player passing or bidding higher than the previous bidder, until there are two consecutive players pass or someone bids 3, which ends the auction since it is the highest possible bid. The final and highest bidder is the landlord. This player now picks up the three face-down cards from the middle, for a total of 20 cards.PlayThe landlord plays first, and may play a single card or any legal combination. Each subsequent player in anticlockwise order must either pass (play no card) or beat the previous play by playing a higher combination of the same number of cards and same type. There are just two exceptions to this: a rocket can beat any combination, and a bomb can beat any combination except a higher bomb or rocket - see definitions below. The play continues around the table for as many circuits as necessary until two consecutive players pass. The played cards are then turned face down and put aside, and the person who played the last card(s) begins again, leading any card or legal combination.In this game, there are thirteen types of combination that can be played:Single card - ranking from three (low) up to red joker (high) as explained abovePair - two cards of the same rank, from three (low) up to two (high)Triplet - three cards of the same rankTriplet with an attached card - a triplet with any single card added, for example 6-6-6-8. These rank according to the rank of the triplet -so for example 9-9-9-3 beats 8-8-8-A.Triplet with an attached pair - a triplet with a pair added, like a full house in poker, the ranking being determined by the rank of the triplet - for example Q-Q-Q-6-6 beats 10-10-10-K-K.Sequence - at least five cards of consecutive rank, from 3 up to ace - for example 8-9-10-J-Q. Twos and jokers cannot be used.Sequence of pairs - at least three pairs of consecutive ranks, from 3 up to ace. Twos and jokers cannot be used. For example 10-10-J-J-Q-Q-K-K.Sequence of triplets - at least two triplets of consecutive ranks from three up to ace. For example 4-4-4-5-5-5.Sequence of triplets with attached cards - an extra card is added to each triplet. For example 7-7-7-8-8-8-3-6. The attached cards must be different from all the triplets and from each other. Although triplets of twos cannot be included, a two or a joker or one of each can be attached, but not both jokers.Sequence of triplets with attached pairs - an extra pair is attached to each triplet. Only the triplets have to be in sequence - forexample 8-8-8-9-9-9-4-4-J-J. The pairs must be different in rank from each other and from all the triplets. Although triplets of twos cannot be included, twos can be attached. Note that attached single cards and attached pairs cannot be mixed - for example 3-3-3-4-4-4-6-7-7 is not valid.Bomb - four cards of the same rank. A bomb can beat everything except a rocket, and a higher ranked bomb can beat a lower ranked one.Rocket - a pair of jokers. It is the highest combination and beats everything else, including bombs.Quadplex set - there are two types: a quad with two single cards of different ranks attached, such as 6-6-6-6-8-9, or a quad with two pairs of different ranks attached, such as J-J-J-J-9-9-Q-Q. Twos and jokers can be attached, but you cannot use both jokers in one quadplex set. Quadplex sets are ranked according to the rank of the quad. Note that a quadplex set can only beat a lower quadplex set of the same type, and cannot beat any other type of combination. Also a quadplex set can be beaten by a bomb made of lower ranked cards.Note that passing does not prevent you from playing on a future turn.Example Player A (the landlord) leads 3-3-3-9 to get rid of some low cards, player B passes, player C plays 5-5-5-7, player A plays K-K-K-J and player B plays A-A-A-3. C and A pass, so B can start again with anything. He leads a single 4.Note B could have played his aces on his the first turn, but preferred to pass to give his partner a chance to get rid of some cards. C will now play if possible, so as not to give the landlord (A) a free chance to lead again. Having beaten A's second play, B leads a low card to give C the choice of playing another unwanted card or putting the landlord under pressure by playing a high card.ScoringIf the landlord runs out of cards first he has won, and each opponent pays him the amount of the bid - 1, 2 or 3 units - provided that no bomb or rocket was played. If one of the other two players runs out before the landlord, the landlord loses and must pay the amount of the bid to each opponent. For each occasion when any player played a bomb or rocket, the payment for the hand is doubled. So for example in a hand in which two bombs and a rocket were played, a player who bid 3 will win 24 points from each opponent for going out first, or pay 24 to each opponent if another player goes out first.Note that since the opponents of the landlord stand to win or lose equally, they form a temporary partnership. When playing against the landlord it is just as profitable to help your partner to run out of cards first as to win yourself. Because of this the partners will usually not beat each other’s cards, and the weaker partner will play to help the stronger partner.Four-Player GameThe four-player form of Fight the Landlord is played mainly in Zhejiang and Jiangsu provinces, including Shanghai. It uses a double deck, including two red and two black jokers - 108 cards altogether. Each player takes 25 cards and 8 cards are left over for the landlord, who plays alone from a hand of 33 cards against the other three players in partnership.The combinations that can be played differ from those in the three-player game, listed above, as follows:Single card attachments are not permitted - i.e. combination types 4 and 9 are exluded.There are no quadplex sets - combination type 13 is excluded.A bomb (type 11) can consist of four or more cards of equal rank, and a bomb with more cards beats a bomb with fewer cardsirrespective of the ranks of the cards.For a rocket (type 12) you need all four jokers.You cannot play a red and a black joker together as a pair, but you can use a pair of red jokers or a pair of black jokers either as a pair by itself or to attach to a triplet.The payment for a hand is doubled for each bomb of 6 or more cards and for each rocket, but bombs of 4 or 5 cards do not affect the payment.。

网络拍卖英文流程Ah, the digital bazaar of dreams and nightmares! Welcome to the world of online auctions, where you can bid on anything from a vintage typewriter to a haunted house (just kidding, we hope). Here’s a step-by-step guide to the wild, wacky, and sometimes worrisome world of online auctions, all in the good ol' American English.Step 1: The Lurking PhaseFirst things first, you gotta do some recon. Browse the listings like a detective, looking for that one-of-a-kind item that’ll make your heart go "ka-ching!" Just remember, what you're looking at is a digital mirage until you see it in person (or at least in a high-res photo).Step 2: The Registration ShuffleBefore you can start bidding, you gotta sign up for the auction site. It's like joining a club, but instead of a secret handshake, you need a credit card and a good credit score. Fill in the blanks, hit submit, and voilà! You're ready to rumble.Step 3: The Bidding BattleNow for the main event! Place your bid with the confidence of a poker player and the stealth of a ninja. Butwatch out, because the auction isn’t just you against the item—it’s you against a bunch of other bidders who are just as desperate to win.Step 4: The Nerve-Wracking WaitAfter you place your bid, it’s time for the waiting game. Keep an eye on the clock, and if you’re feeling brave, maybe even place a higher bid to scare off the competition. Just don’t blink, or you might miss the action!Step 5: The Winning WahooCongratulations, yo u’ve won the auction! Now it’s timeto pay up and wait for your prize to arrive. It’s like Christmas morning, but you already know what you’re getting.Step 6: The AftermathOnce your item arrives, inspect it like a pro. If it’snot what you bargained for, you might have to go through the hassle of returns or refunds. But if it’s everything you dreamed of and more, you can sit back and bask in the gloryof your successful online auction conquest.So there you have it, folks. The thrill of the chase, the agony of defeat, and the ecstasy of victory—all in thecomfort of your own home. Happy bidding, and may the odds be ever in your favor!。