Asynchronous Optimistic Rollback Recovery using Secure Distributed Time. Computer Science

stochastic resist model evolution -回复Stochastic Resistor Model Evolution: Understanding the Role of Randomness in Circuit AnalysisIntroduction:In the world of circuit analysis, mathematicians and engineers have long been working towards developing accurate models that accurately represent the behavior of electrical components. Over the years, various models have been proposed and refined, each building upon the shortcomings of its predecessors. One such model that has gained significant attention is the Stochastic Resistor Model. In this article, we will delve into the evolution of this model, discussing its history, purpose, and implications in the field of circuit analysis.1. The Origins:The Stochastic Resistor Model, also known as the Random Resistor Model, is rooted in the concept of randomness and probability. Its inception can be traced back to the mid-20th century when researchers began to acknowledge the limitations of deterministicmodels in capturing the true nature of electrical components. While deterministic models assume ideal and predictable behavior, the Stochastic Resistor Model recognizes that there are inherent uncertainties in real-world scenarios.2. Understanding Stochasticity:To comprehend the Stochastic Resistor Model, one must first understand the concept of stochasticity. It refers to the randomness and unpredictability present in natural phenomena. In the context of circuit analysis, stochasticity implies that the properties of electrical components, such as resistors, cannot be precisely determined due to various factors, including manufacturing tolerances, temperature fluctuations, and quantum effects.3. The Need for a Stochastic Model:Deterministic models have been the backbone of circuit analysis for decades, providing valuable insights and predictions. However, as technology advanced, engineers and scientists faced the challenge of accurately representing the behavior of increasingly complexcircuits. The limitations of deterministic models became evident when dealing with nanoscale components and high-frequency circuits. The need for a model that incorporates randomness was apparent.4. Introducing Stochastic Resistor Model:The Stochastic Resistor Model aims to fill the gap left by deterministic models. It acknowledges the existence of random factors influencing the behavior of resistors in circuits. By incorporating statistical techniques, this model provides a way to quantify and analyze uncertainties in circuit designs more accurately. The Stochastic Resistor Model's fundamental assumption is that the resistance value of a resistor follows a probability distribution, allowing for a stochastic analysis of circuits.5. Evolution of Stochastic Resistor Model:The Stochastic Resistor Model has evolved significantly since its inception. Initially, researchers focused on developing basic models that incorporated simple probability distributions. However, as computational power increased, researchers began exploring morecomplex distributions, such as Gaussian, log-normal, and Weibull. Additionally, the model evolved to incorporate the correlation between resistors, leading to the development of more accurate circuit simulations.6. Implications and Applications:The Stochastic Resistor Model has revolutionized circuit analysis by providing a more realistic representation of the behavior of electrical components. This model has found applications in various fields, including integrated circuit design, reliability analysis, and circuit optimization. By accurately accounting for uncertainties, engineers can design circuits that are more robust, reliable, and resistant to variations in the manufacturing process and environmental conditions.7. Future Directions:The evolution of the Stochastic Resistor Model is an ongoing process. Researchers continue to refine the model by exploring new probability distributions, improving correlation models, and incorporating other random variables, such as noise sources.Furthermore, the integration of the Stochastic Resistor Model with other stochastic component models, such as capacitors and inductors, is an area of active research.Conclusion:The Stochastic Resistor Model has emerged as a powerful tool in circuit analysis, allowing engineers and scientists to embrace the unpredictability and randomness present in real-world electrical components. Its evolution has led to improved accuracy and reliability in circuit design, paving the way for future advancements in technology. By understanding and harnessing stochasticity, we can anticipate a future where circuit analyses are more precise and resilient in the face of uncertainties.。

Model 8500Angle Position Indicator• 0.01° Resolution• 0.03° Accuracy on each of the two independent channels • Auto-compensation of phase shift errors maximizes accuracy• Auto-ranging adjusts to any line-to-line voltage of 10-100V•Optional IEEE-488 interface for remote sensing and controlThe Model 8500, API is a Synchro/Resolver-to-digital converter that performs high quality analog-to-digital conversions of Synchro or Resolver data.The API transmits the converted digital representation of the analog data simultaneously to the following locations:• The front panel display which uses six seven-segment LED planar information displays. • The BCD outputs of the rear panel parallel I/O connector.• The optional IEEE-488 interface bus, which has full MATE compatibility.The Model 8500 is housed in a 9 1/2-inch wide rack panel and is packaged primarily for computer controlled or fixed installation applications.Front Panel Description: The front panel contains a group of controls and indicators including a power ON/OFF switch, a set of function switches and coordinated LED indicators, a set of input terminals, and a display for angular data, degree/minutes, and IEEE-488 interface status information. (See table on next page)Rear Panel Description: The rear panel provides an ac input receptacle (J2), a parallel I/O connector (J1), and an 8-position mode DIP-Switch with appropriate instruction label. An IEEE-488 ADDRESS switch and connector (J3) are optionally available. (See table on next page)Power Requirements: The API operates with either 115 V ac or 230 V ac, 47 to 440 Hz power source. Power may be applied from the line cord or parallel I/O rear connector J1. Refer to tables, J1 power connections and switch selection of voltage and power sourceInternal Power Connections: All API models are factory set for 115 V AC line cord operation (line cord is supplied), but can operate using 115 V AC or 230 V AC as selected by an internal switch. In addition, a rear power connector, which also accepts 115 V/230 V AC, can be used in place of the line cord by selecting the position of an internal switch (see pin designations).SpecificationsItem Specification Input SpecificationsInputchannels Signalinputs 2 (selectable)Automatic line-to-line tracking, Synchro orResolver.10 to 100 V L-L, 47-440 Hz (F2, option 2) or 360 to 1200 Hz (F2, option 4).Signal input impedance Reference levels 250 k ohms (minimum)1 to 115 V rms, all frequency ranges.(All Synchro or Resolver data must be derived from this reference.)Reference input impedance Power requirements 100 k ohms (minimum) 115/230 V rms_+ 10% 47 to 440 Hz, 20 VAData Freeze DF DFFreeze Track+5 V 0 V or open0 V +5 V or open(Display and output frozen; internalcircuitry continues to track signal.)Channel Remote Program 0 V or GND=CH1, +5 V or OPEN=CH2 Output SpecificationsDisplayReadoutresolution Digital output dataDigital output levelLogic1LogicConverterBusy 5 decimal digits, 0.56-inch high LED indicators for channel and remote0.01 degree or 1 minute (F2, option 2 or 4) 5 decades of BCD digits (1,2,4,8 code)+3.9 V minimum, 4 standard LS TTL loads 0.1 V maximumTTL compatible (pulses are present when converter is busy)Performance SpecificationsAngular Accuracy 0.03 degreesNorth Atlantic Industries, Inc. . 11-06-01SpecificationsItem SpecificationAngular Resolution 0.01 degreesAngular Range 0 to 359.99 degrees or 0 to 359 degrees 59minutes; or, -179.99 to +180 degrees or -179 degrees 59minutes to +180 degrees 00 minutes (F2=2 or 4) Auto phase correction Automatically corrects for signal phase shift up to_+ 80degrees.Trackingspeed Selectable:Lo speed = 180 degrees/sec with no tracking error, 47-440 Hz (F2, option 2)Hi speed = 1800 degrees/sec with no tracking error, 360-1200 Hz (F2, option 4)Settling time Dependent on tracking speed selected and frequency range:Less than 1.5 seconds for 180 degrees step change, Lotracking speed and 47-440 Hz (F2, option 2).Less than 1.0 seconds for 180 degrees step change, Hitracking speed and 360-1200 Hz (F2, option 4). Velocity output:HI Tracking rate 2.85 mV dc/degree/second (nominal)LO Tracking rate 28.5 mV dc/degree/second (nominal)Operating modeFaultindications Lamptest Track onlyNo reference present: all 8s displayedNo Synchro or Resolver connected or input line-to-line voltage is too low: display is blanked.Over velocity: "o" displayed to left of angle displayOptional IEEE-488 MATE relay closureDisconnect reference to display all 8s or apply logic "0" to J1-38.Mechanical Specifications Front Panel Color (See figure 2-1 Outline and Dimension Drawing, API Model 8500)Semi-gloss gray, 26440 per Fed-Std-595 window area black #27038 per Fed-Std-595Markings Semi-gloss black enamel 27038 per Fed-Std-595; PantoneWarm Red U (warnings and logo only); White #27875 perFed-Std-595Size 9.5" W x 1.75" H x 12" DWeight 4 lbs. (maximum)Operating Temperature 0-50o CNorth Atlantic Industries, Inc. . 11-06-01ORDERING INFORMATIONFeatures and Options:To identify the Model 8500 options, a three-digit number is assigned in accordance with table below.For example, the Standard Model 8500 with half rack mounting (1), 47 Hz to 440 Hz frequency selectable display (2), and parallel interface (1) would have an option number of F121.8500-F 1 2 1F1F2F3No. Feature OptionsF1 Configuration Options 1. Panel mount, half rack2. Bench use (includes front terminals and stand)3. Panel mount, full rackF2 Frequency and Display 2. 47-440 Hz, selectable display4. 360 Hz to 1200 Hz, selectable displayF3 Interface 1.Parallelonly2. Parallel and IEEEAccessories:The API can be ordered with mounting adapters for mounting either one or two units in a standard 19-inch equipment rack. The table below describes full rack and tandem full rack mounting accessories:Type of Mount Description NAI P/N Full Rack Mounting Mounts one unit in 19-inch rack 300697Tandem Full Rack Mounting Mounts two units side by side in 19-inchrack300698The parallel I/O 50-pin mating connector, J1 is supplied by North Atlantic Industries (NAI P/N 783718) but operator must make cable assembly. It consists of the following parts:Description AMP P/N QtyShellClamp Retainer Pins 205211-1205732-1205980-166569-311250North Atlantic Industries, Inc. . 11-06-01Front Panel Controls and IndicatorsControl/Indicator FunctionPWR push button Alternate Acting Switch. Turns power on and off (push button in: power on;push button out: power off).REM push button Alternate Acting Switch. Selects remote operation of the API (push buttonin: Remote; push button out: local).CHAN push button Alternate Acting Switch. Selects input channel (push button in: CH 1; pushbutton out: CH 2).HOLD push button Momentary Switch. Push in to freeze display and output data.REM LED When ON, indicates the API is in remote operation.CHAN LED When on, indicates Channel 1 is selected.When off, indicates Channel 2 is selected.CAUTIONTerminals S1, S2, S3, S4, and REF HI and LO are directlyconnected to the transformer inputs and must not be used ifJ1 inputs are used. Refer to table 2-1, J1 pin connections.S1,S2,S3,S4Accepts Synchro or Resolver input signals (bench units only).TerminalsAccepts reference input signal (bench units only).REF HI and LOTerminalsRear Panel DescriptionControl/Indicator FunctionInput Power Receptacle Power cable connector (J2) for 115 V ac or 230 V ac input.ADDRESS Switches ADDRESS DIP switches set unit address for IEEE bus.Parallel I/O Connector Provides API interconnection with external systems, powersources, etc. Refer to table 2-1.MODES Switch 8-position DIP switch which controls selectable modes. Referto paragraph 3-3.3.IEEE-488 Connector (optional) Connects IEEE-488 standard I/O bus to unit.North Atlantic Industries, Inc. . 11-06-01Mode Switch SW2Switch NumberSwitchSignal NameSwitch Position1 01 CH1 SYN Channel 1 in Resolver mode or forremote control of Channel 1.Channel 1 in Synchro mode.2 CH2 SYN Channel 2 in Resolver mode or forremote control of Channel 2.Channel 2 in Synchro mode.3 INT/EXTREF Selects auto phase correctedinternal reference.Selects external reference.4 Bandwidth(H/L) Selects low bandwidth (trackingspeed) or remote control of trackingspeed.Selects high bandwidth (trackingspeed).5 Deg/Min Selects 2 LSDs of display in minutesof arc. Selects 2 LSDs of display in hundredths of degrees.6 Display(U/B) Unipolar display.(0o to 359.99o) or remote control ofdisplay.Bipolar display_+ 180o.7 Sign Sign bit equals plus sign (+) in F2=2or 4 units. Sign bit equals minus sign (-) in F2=2 or 4 units.8 P/I Selects IEEE-488 interface forremote control Selects parallel interface for remote control.North Atlantic Industries, Inc. . 11-06-01J1 Pin DesignationsPin Function1 Power input Hi (internal switch enables pins 1 & 2; disables IEC power connector2 Power input loground3 Chassisground4 Digital(Channel 1)5 S16 S2 “ “7 S3 “ “8 S4 “ “9 R1 “ “10 R2 “ “Busy11 Converter12 .04° or 4’13 .01° or 1’14 .8° or not used15 .2° or 20’16 4°17 1°18 Channel 2 Synchro jumper (connect to pin 35 for channel 2 Synchro operation)19 NC20 Tracking HI/LO input21 S1 (Channel 2)22 S2 “ “23 S3 “ “24 S4 “ “25 R1 “ “26 R2 “ “27 Data freeze (DF)28 .02° or 2’29 .08° or 8’30 .1° or 10’31 .4° or 40’32 2°33 8°34 Channel 1 Synchro jumper (connect to pin 35 for channel 1 Synchro operation)35 Synchro jumper common36 Fault 1 (not used)37 Fault 2 (not used)38 Lamp Test39 Unipolar/Bipolar for F2=2 or 4(Built in Test Equipment)output40 BITE(analog)output41 VelocityFreeze(DF)42 Data(0=CH1, 1=CH2)Program43 Remotespare44 NC45 20°BCD Outputs46 40° “ “47 80° “ “48 10° “ “49 100° “ “50 200° or sign bit in bipolar “ “North Atlantic Industries, Inc. . 11-06-01。

海员机工考试英语试题及答案一、选择题（每题2分，共20分）1. Which of the following is the correct way to spell the word "maintenance"?A. MaintananceB. MaintenenceC. MaintenenceD. Maintenance答案：D2. The term "deadweight" refers to:A. The maximum weight a ship can carryB. The weight of the ship without cargoC. The weight of the cargo onlyD. The total weight of the ship including cargo答案：A3. What does the abbreviation "SOLAS" stand for?A. Society of Load and Load AfloatB. Safety of Life at SeaC. Standard Operating Load and Load AfloatD. Ship Operations and Load Afloat答案：B4. Which of the following is not a type of marine fuel?A. Heavy fuel oilB. Marine gas oilC. Liquefied natural gasD. Unleaded gasoline答案：D5. The International Maritime Organization (IMO) is responsible for:A. Regulating the global shipping industryB. Providing maritime educationC. Conducting marine researchD. All of the above答案：A6. What is the primary function of a lifebuoy?A. To provide a source of lightB. To be used as a signaling deviceC. To assist in rescuing a person overboardD. To store emergency rations答案：C7. The term "GMDSS" stands for:A. Global Maritime Data SystemB. Global Maritime Distress and Safety SystemC. Global Maritime Development SystemD. Global Maritime Delivery System答案：B8. What is the purpose of a bilge pump on a ship?A. To pump water from the bilges to maintain the ship's stabilityB. To cool the ship's engineC. To clean the ship's deckD. To transfer fuel between tanks答案：A9. The term "LOA" when referring to a ship, stands for:A. Length of ArrivalB. Length of AgreementC. Length Over AllD. Length of Approval答案：C10. Which of the following is not a navigational aid?A. BuoyB. LighthouseC. RadarD. Compass答案：C二、填空题（每题2分，共20分）1. The _______ is the part of the ship's hull that is in contact with the water.答案：hull2. The term "draft" refers to the _______ of a ship's hull that is submerged in water.答案：depth3. A _______ is a device used to measure the depth of water. 答案：sounder4. The _______ is the highest point of a ship's structure above the waterline.答案：mast5. The _______ is a device used to measure the speed of a ship through the water.答案：log6. The _______ is the area on a ship where cargo is loaded and unloaded.答案：cargo hold7. A _______ is a rope used to secure a ship to a dock or another ship.答案：hawser8. The _______ is the part of the ship's deck where the crew works.答案：forecastle9. The _______ is a device used to communicate with other ships and shore stations.答案：radio10. The _______ is a type of knot used to secure a rope to a fixed object.答案：cleat hitch三、简答题（每题10分，共40分）1. What are the main functions of a ship's engine room?答案：The main functions of a ship's engine room include generating power for propulsion, providing mechanical energy for various onboard systems, and maintaining the ship's operational efficiency.2. Explain the importance of regular maintenance of a ship's hull.答案：Regular maintenance of a ship's hull is crucial for preventing corrosion, minimizing biofouling, ensuring the structural integrity of the vessel, and maintaining its hydrodynamic efficiency, which in turn affects fuel consumption and overall performance.3. What are the key components of a ship's navigation system? 答案：Key components of a ship's navigation system include the compass, radar, GPS, chart plotter, autopilot, and communication equipment such as VHF radios and satellite communication systems.4. Describe the role of a ship's life-saving equipment in emergency situations.答案：In emergency situations, a ship's life-saving equipment plays a critical role in ensuring the safety of the crew and passengers. This equipment includes lifeboats, life rafts, lifebuoys, lifejackets, and emergency signaling devices, which are designed to facilitate rescue operations and provide temporary survival support until help arrives.。

支持向量回归核函数
支持向量回归是一种常用的机器学习方法，用于预测连续值的输出。

在一些复杂的数据集中，线性回归可能无法准确地拟合数据。

为了解决这个问题，我们可以使用核函数来进行支持向量回归。

核函数可以将低维特征空间映射到高维特征空间，从而使得数据集在高维空间中变得更加可分。

在支持向量回归中，我们通常使用径向基函数（RBF）核函数，因为它是最常用的核函数之一，并且可以处理非线性的数据集。

RBF核函数的形式为：
K(x, x') = exp(-gamma * ||x - x'||^2)
其中gamma是一个超参数，可以通过交叉验证来确定。

该核函数可以将数据映射到无限维空间中，从而使得支持向量回归可以处理非线性问题。

在支持向量回归中，我们还需要选择一个适当的惩罚参数C来平衡模型的复杂度和训练误差。

如果C值过小，模型会过拟合；如果C 值过大，模型会欠拟合。

因此，我们通常需要进行交叉验证来选择合适的C值。

总之，通过使用核函数，支持向量回归可以更好地处理非线性数据集。

在实际应用中，我们可以根据数据集的特点选择不同的核函数来进行支持向量回归。

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111 Genetic algorithm for planning cable telecommunication networks Original Research ArticleApplied Soft Computing, Volume 1, Issue 1, June 2001, Pages21-33Pablo Cortés, Juan Larrañeta, Luis Onieva, José M. García, María S. CaraballoClose preview | Purchase PDF (325 K) | Related articles | Related referencework articlesAbstract | Figures/Tables | ReferencesAbstractOptical fiber networks over synchronous digital hierarchy operating on ATM mode appear as one of the best alternatives for the new high-speed telecommunications networks. The network management requires planning studies so that the designed networks allow efficient operation in real time. A quantitative model evaluating the global topological design of an optical fiber network over synchronous digital hierarchy is described. The model includes the economic aspects involved in the project, the civil works, the capacity evaluation costs and the introduction of reliability conditions. A genetic approach based on the exploitation of the Kuhn–Tucker optimality conditions is proposed to solve the model. Article Outline1. Introduction2. Formulation2.1. Augmented network2.2. Notation and constraints2.3. Model3. Genetic approach3.1. Routing demand and capacity assignment procedure3.1.1. Initialization3.1.2. Iterations4. Results5. Conclusions AcknowledgementsAppendix A. PseudocodesA.1. Two-step rule-based heuristic A.2. Genetic algorithmAppendix B. Algorithm parameters References112 The forest vegetation simulator: A review of its structure, content, and applications Original Research ArticleComputers and Electronics in Agriculture, Volume 49, Issue 1, October 2005, Pages 60-80Nicholas L. Crookston, Gary E. DixonClose preview | Purchase PDF (155 K) | Related articles | Related reference work articlesAbstract | Figures/Tables | ReferencesAbstractThe Forest Vegetation Simulator (FVS) is a distance-independent, individual-tree forest growth model widely used in the United States to support management decisionmaking. Stands are the basic projection unit, but the spatial scope can be many thousands of stands. The temporal scope is several hundred years at a resolution of 5–10 years. Projections start with a summary of current conditions evident in the input inventory data. FVS contains a self-calibration feature that uses measured growth rates to modify predictions for local conditions. Component models predict the growth and mortality of individual trees, and extensions to the base model represent disturbance agents including insects, pathogens, and fire. The component models differ depending on the geographic region represented by regionally specific model variants. The differences are due to data availability and the applicability of existing models. The model supports specification of management rules in the input, such as thinning if density is too high. The rules can be extended to represent other factors. For example, the effect of climate change on standdevelopment by entering rules that specify how growth and mortality will change in response to changing climate.Applications range from development of silvicultural prescription for single stands to landscape and large regional assessments. Key issues addressed with FVS include forest development, wildlife habitat, pest outbreaks, and fuels management. The predictions are used to gain insights into how forested environments will respond to alternative management actions. Broad-scale forest management policies have been studied with FVS.For the 30 years since the model was initially introduced, the development team has anticipated and provided needed enhancements and maintained a commitment to working with and training users. The existence of an adequate user interface and the continued use of the original programming language are often overlooked factors for the success of this model.Future work will focus on improving FVS by adopting recent biometric techniques and including new information linking geomorphology to mortality and growth. Extending the model to more closely represent biophysical processes and adapting the model so that it is more relevant to management questions related to predicted climate change are also foci. Providing ways to dynamically link FVS to other models is our current strategy for providing major new capabilities.Article Outline1. Introduction2. Structure of FVS3. Component models and modeling techniques3.1. Large-tree growth – diameter increment driven3.2. Small-tree growth – height increment driven3.3. Mortality3.3.1. Prognosis-type mortality model3.3.2. SDI-based mortality model3.3.3. TWIGS-based mortality model3.4. Regeneration establishment3.5. Crown ratio change3.5.1. Prognosis-style crown model3.5.2. Weibull-based crown model3.5.3. Crown models from other modeling systems3.6. Self-calibration of component models, record tripling, and user control4. FVS applications4.1. Large-scale habitat assessments4.2. Forest health assessments4.3. Traditional forest planning4.4. Policy and resource supply analysis4.5. Linkage to other tools4.6. Climate change4.7. Beyond North America4.8. Forestry research and teaching5. Support organization and other key factors6. Limitations and future workReferences113 Application of multi-agent planning to the assignment problem Original Research ArticleComputers in Industry, Volume 54, Issue 3, August 2004, Pages 273-290S. Kornienko, O. Kornienko, J. PrieseClose preview | Purchase PDF (941 K) | Related articles | Related reference work articlesAbstract | Figures/Tables | ReferencesAbstractNowadays, a globalization of national markets requires developing the flexible and demand-driven production systems with new innovative concepts of management, information processing, production scheduling and planning. The presented work focuses on the low-level planning, where the multi-agent solution towards a ―job-machine‖ assignment is considered. The main point of the discussion is the flexibility of planning systems ensured by the concept of agent‘s ―roles‖ and ―emergencies‖. Depending on the state of ―emergency‖, the system receives stepwisely additional degrees of freedom to adapt the planning to the changing conditions of the manufacturing floor. The distributed constraint satisfaction and optimization approaches, underlying the suggested method, as well as activities of rescue agents, are described in the form of Petri networks providing both the conceptual notions and main details of implementation.Article Outline1. Introduction2. The problem of assigning jobs to machines3. Application of autonomous agents3.1. The primary algorithms3.2. The local emergency and rescue agents3.3. The global emergency4. Agent-based optimization5. DiscussionAcknowledgementsReferencesVitae114 Creativity and dysfunction in strategic processes: The case of scenario planning Original Research ArticleFutures, Volume 42, Issue 4, May 2010, Pages 271-281Brad MacKay, Peter McKiernanClose preview | Purchase PDF (309 K) | Related articles | Related reference work articlesAbstract | Figures/Tables | ReferencesAbstractThis paper attempts to open up a new line of enquiry into the dysfunctions of creativity within strategic processes. Generally, the impact and results of introducing creativity (and innovation) into organisational life are perceived to be wholesome and beneficial. But recent research in the area of organisational psychology has documented a ‗dark‘ side to its introduction, e.g., low employee morale, stress, theft, sabotage, destructive conflict. Learning from this work and shifting the domain to strategic management, this paper focuses on scenario planning—a strategy process widely regarded by participants and facilitators as creative and innovative in structure, content and output. First, the creative credentials of the process are established with reference to the literature and definitions from the creative and cultural industries. Second, the process is deconstructed into activities and each is examined for the extent of its embedded creativity. Third, informed by case evidence, four dysfunctions of the scenario planning process are conjectured: creativity layered on fantasy; heightened expectations and confusion; pride and passion; and creativity leading to excess. The paper concludes by suggesting four options for handling these potential dysfunctional effects and, in the light of the dialogue presented, re-interprets the definition of scenario planning presented earlier in the text.Article Outline1. Introduction2. Creativity and innovation2.1. Definitions of creativity and innovation2.2. Creative processes—psychology2.3. Creative processes—cultural industries3. The scenario planning process3.1. Historical development3.2. The architecture of a heuristic scenario planning process3.3. Scenario planning as a creative process3.4. Creativity and dysfunctions across and within individual stages 3.4.1. Creativity induced dysfunctions in the SPP3.4.1.1. Creativity upon fantasy3.4.1.2. Heightened expectations and confusion3.4.1.3. Pride and passion3.5. Creativity to excess4. Concluding commentsReferences115 On advance reservation of heterogeneous networkpaths Original Research ArticleFuture Generation Computer Systems, Volume 21, Issue 4, April 2005, Pages 525-538Chiara Curti, Tiziana Ferrari, Leon Gommans, S. van Oudenaarde, Elisabetta Ronchieri, Francesco Giacomini, Cristina VistoliShow preview | Purchase PDF (356 K) | Related articles | Related referencework articles116 Vulnerability of ground-nesting waterbirds to predation by invasive American mink in the Cape Horn Biosphere Reserve, Chile Original Research ArticleBiological Conservation, Volume 142, Issue 7, July 2009, Pages 1450-1460Elke Schüttler, Reinhard Klenke, Steven McGehee, Ricardo Rozzi, Kurt JaxShow preview | Purchase PDF (763 K) | Supplementary content | Relatedarticles | Related reference work articles117 Dépistage du cancer du sein en médecine libérale : une enquête auprès des généralistes et des gynécologues de Rhône-Alpes Original Research ArticleFeuillets de Radiologie , Volume 45, Issue 2, April 2005, Pages 141-147E. Olaya, J.-P. Ferley, N. Puech, G. Granet, E. Da Silva, J. Derrien,A. GarnierShow preview|Purchase PDF (183 K) | Related articles | Related referencework articles 118 Fuzzy logic-based real-time robot navigation in unknown environment with dead ends Original Research ArticleRobotics and Autonomous Systems , Volume 56, Issue 7, 31 July 2008, Pages 625-643Meng Wang, James N.K. LiuShow preview|Purchase PDF (3160 K) | Related articles | Related referencework articles 119 Modeling a rail transit alignment considering different objectives Original Research ArticleTransportation Research Part A: Policy and Practice , Volume 45, Issue 1, January 2011, Pages 31-45Sutapa Samanta, Manoj K. JhaShow preview|Purchase PDF (1564 K) | Related articles | Related referencework articles雅仕百货——企划经理 )|公司行业： 物业管理/商业中心 批发/零售公司性质： 民营公司公司规模： 500人以上 比比你的竞争力发布日期：2011-02-26 工作地点： 沈阳 招聘人数： 1 工作年限：三年以上 学 历： 博士职位描述有大型购物中心、商场企划工作3年以上。

浙江理工大学学报（自然科学版），第37卷，第6期，20：17年月Journal of Zhejiang Sci-Tech U niversity(Natural Sciences)V o l.37，N o. 6，N ov.2017D Q I：10. 3969/j.issn. 1673-3851. 201 7. 11. 015多模态函数聚类后再创种群的并行搜索佳点集萤火虫算法方贤1,铁治欣1,李敬明2,高雄1(1.浙江理工大学信息学院，杭州310018;.合肥工业大学管理学院，合肥230009)摘要：萤火虫算法在求解多模态函数时，随着峰值个数的增加，往往需要更大的种群规模才能得到较为理想 的结果，而且初始种群是否均匀分布对结果也有很大影响。

针对萤火虫算法的这些不足，提出了一种多模态函数的 聚类后再创种群的并行搜索佳点集萤火虫算法。

该算法首先以数论佳点集的思想将萤火虫均匀分布于搜索空间 中，在粗糙搜索完成后，通过密度聚类算法进行捕峰操作，重新构造等同于峰值点数的各个平行空间；然后在各空间 中继续加入少量佳点集生成的萤火虫并行精细搜索，最终可获得各个平行空间的局部最优解以及整个空间的全局 最优解。

与其他算法在12个典型多模态函数中的测试结果进行对比，该算法总体上缩小了种群规模，加快了收效速 度，搜索精度更高，时间成本更低，稳定性能更好。

关键词：萤火虫算法；多模态函数；圭点集；密度聚类算法；并行搜索中图分类号：TP391.9文献标志码：A文章编号：1673-3851 (2017) 06-0843-080引言在工程技术、科学计算、经济管理等领域中，绝 大多数实际问题可以通过构建模型归结为函数优化问题。

其中，一些函数由于自身维数高、峰值点多、震荡严重等因素造成性态差。

采用传统的算法，如 D F P变尺度算法、P o w e ll方向加速法等，往往难以或无法找到全局最优解。

近年来随着智能计算学科的发展，一些仿生算法应运而生，如粒子群算法(particle swarm optimization,P S())、遗传算法(genetic algorithm,G A)、蚁群算法（ant clony optimization,AC O)、蜂群算法（artificial bee colony algorithm,A B C)、鱼群算法（artificial fish swarm algorithm,A F S A)等。

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Mutual fund performance evaluation with active peer benchmarks[J]. Journal of Financial economics, 2014, 112(1): 1-29.海外文献推荐-第七十四期参考文献：[1]Michael Stein and Svetlozar T. Rachev. Style Neutral Funds of Funds: Diversification or Deadweight? [J]. Journal of Asset Management, February 2011, V olume 11, Issue 6, pp 417–434海外文献推荐-第七十五期参考文献：[1] Elisabeth Kashner, 2019.01.31, Bogle led this investing Fee War, ;[2] Cinthia Murphy,2017,03.31, how to launch a successful ETF, ;[3] Drew V oros, 2019.01.23, how a small ETF Issuer Competes, ；[4] 2019.01.04, Invesco focusing on scale,海外文献推荐-第七十六期参考文献：[1] Shpak I , Human B , Nardon A . Idiosyncratic momentum in commodity futures[J]. Social Science Electronic Publishing, 2017.海外文献推荐-第七十六期参考文献：[1] Ehsani S , Linnainmaa J T . Factor Momentum and the Momentum Factor[J]. Social Science Electronic Publishing, 2017.海外文献推荐-第七十七期参考文献：[1] Iuliia Shpak*, Ben Human and Andrea Nardon. 2017.09.11, Idiosyncratic momentum in commodity futures. ResearchGate海外文献推荐-第七十八期参考文献：[1] Joel Hasbrouck. High-Frequency Quoting: Short-Term V olatility in Bids and Offers. JOURNAL OF FINANCIAL AND QUANTITATIVE ANALYSIS海外文献推荐-第七十九期参考文献：[1] Tarun Gupta and Bryan Kelly. Factor Momentum Everywhere. Institutional Investor Journals海外文献推荐-第八十期参考文献：[1] MICHAEL A. BABYAK , P H D. What You See May Not Be What You Get: A Brief, Nontechnical Introduction to Overfitting in Regression-Type Models. S T A T I S T I C A L C O R N E R海外文献推荐-第八十一期参考文献：[1] Eric Jondeau , Qunzi Zhang , Xiaoneng Zhu. Average Skewness Matters.海外文献推荐-第八十二期参考文献：[1] JOHN A. HASLEM. Morningstar Mutual Fund Measures and Selection Model. THE JOURNAL OF WEALTH MANAGEMENT海外文献推荐-第八十三期参考文献：[1] EUGENE F. FAMA and KENNETH R. FRENCH. Luck versus Skill in the Cross-Section of Mutual Fund Returns. THE JOURNAL OF FINANCE海外文献推荐-第八十四期参考文献：[1] How Transparent Are ETFs?[2] Lara Crigger. Nontransparent Active: Next ETF Revolution?.海外文献推荐-第八十五期参考文献：[1] Olivier Rousse and Benoît Sévi. 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变分自编码器损失函数-回复什么是变分自编码器(VAE)损失函数？变分自编码器(Variational Autoencoder, VAE)是一种生成模型，通过学习数据的潜在分布来实现数据的生成和重构。

VAE使用了一种特殊的损失函数，被称为“变分自编码器损失函数”。

这种损失函数结合了自编码器(Autoencoder)和变分推断(Variational Inference)的思想，使得VAE能够在数据生成和推断中达到更好的性能。

要理解VAE的损失函数，首先需要了解自编码器和变分推断的基本概念。

自编码器是一种无监督学习算法，其任务是将输入数据重新构建出来，同时学习数据的有效表示。

自编码器包括一个编码器和一个解码器，编码器将输入数据映射到低维潜在空间中，解码器将潜在空间的表示映射回原始数据空间。

变分推断是一种贝叶斯推断方法，用于从给定的观测数据中推断潜在变量的分布。

它通过引入一个潜变量的分布来近似真实的后验分布，从而解决了直接计算后验分布困难的问题。

VAE结合了自编码器和变分推断的思想，使用编码器将输入数据映射到潜在空间中的分布，并通过采样来生成潜在变量，解码器将潜在变量映射回原始数据空间。

为了保证学习到的分布能够更好地逼近真实的潜在分布，VAE引入了KL散度(Kullback-Leibler Divergence)作为正则化项。

VAE的损失函数可以表示为重构误差和KL散度的加权和。

具体来说，VAE 的损失函数可以分为两个部分，分别是重构损失和正则化项。

重构损失是衡量VAE重构能力的指标，它表示重构数据与原始数据之间的差异。

重构损失一般使用均方误差(Mean Squared Error, MSE)或交叉熵(Cross Entropy)来计算，其中交叉熵在处理离散数据时更为常用。

正则化项则是通过KL散度来约束潜在变量的分布接近单位高斯分布。

KL 散度用于衡量两个分布之间的差异，VAE使用KL散度作为正则化项的原因是为了使得学习到的分布更加平滑和连续。

International Conference on Principles and Practice of Constraint Programming1995,pp.88–102 Asynchronous Weak-commitment Search for Solving Distributed Constraint SatisfactionProblemsMakoto YokooNTT Communication Science Laboratories2-2Hikaridai,Seika-cho,Soraku-gun,Kyoto619-02,Japane-mail:yokoo@cslab.kecl.ntt.jpAbstract.A distributed constraint satisfaction problem(DistributedCSP)is a CSP in which variables and constraints are distributed amongmultiple automated agents,and various application problems in Dis-tributed Artificial Intelligence can be formalized as Distributed CSPs.We develop a new algorithm for solving Distributed CSPs called asyn-chronous weak-commitment search,which is inspired by the weak-commit-ment search algorithm for solving CSPs.This algorithm can revise a baddecision without an exhaustive search by changing the priority orderof agents dynamically.Furthermore,agents can act asynchronously andconcurrently based on their local knowledge without any global control,while guaranteeing the completeness of the algorithm.The experimental results on various example problems show that thisalgorithm is by far more efficient than the asynchronous backtrackingalgorithm for solving Distributed CSPs,in which the priority order isstatic.The priority order represents a hierarchy of agent authority,i.e.,the priority of decision making.Therefore,these results imply that aflexible agent organization,in which the hierarchical order is changeddynamically,actually performs better than an organization in which thehierarchical order is static and rigid.1IntroductionDistributed Artificial Intelligence(DAI)is a subfield of AI that is concerned with the interaction,especially the coordination among artificial automated agents. Since distributed computing environments are spreading very rapidly due to the advances in hardware and networking technologies,there are pressing needs for DAI techniques,thus DAI is becoming a very vital area in AI.In[13],a distributed constraint satisfaction problem(Distributed CSP)is formalized as a CSP in which variables and constraints are distributed among multiple automated agents.It is well known that surprisingly a wide variety of AI problems can be formalized as CSPs.Similarly,various application problems in DAI which are concerned withfinding a consistent combination of agent ac-tions(e.g.,distributed resource allocation problems[3],distributed scheduling problems[9],and multi-agent truth maintenance tasks[5])can be formalized as Distributed CSPs.Therefore,we can consider a Distributed CSP as a generalframework for DAI,and distributed algorithms for solving Distributed CSPs as an important infrastructure in DAI.It must be noted that although algorithms for solving Distributed CSPs seem to be similar to parallel/distributed processing methods for solving CSPs[2,14], research motivations are fundamentally different.The primary concern in par-allel/distributed processing is the efficiency,and we can choose any type of par-allel/distributed computer architecture for solving the given problem efficiently. In contrast,in a Distributed CSP,there already exists a situation where knowl-edge about the problem(i.e.,variables and constraints)is distributed among automated agents.Therefore,the main research issue is how to reach a solution from this given situation.If all knowledge about the problem can be gathered into one agent,this agent can solve the problem alone using normal centralized constraint satisfaction algorithms.However,collecting all information about a problem requires certain communication costs,which could be prohibitively high. Furthermore,in some application problems,gathering all information to one agent is not desirable or impossible for security/privacy reasons.In such cases, multiple agents have to solve the problem without centralizing all information. The author has developed a basic algorithm for solving Distributed CSPs called asynchronous backtracking[13].In this algorithm,agents act asynchronously and concurrently based on their local knowledge without any global control.In this paper,we develop a new algorithm called asynchronous weak-commit-ment search,which is inspired by the weak-commitment search algorithm for solving CSPs[12].The main characteristic of this algorithm is as follows.–Agents can revise a bad decision without an exhaustive search by changing the priority order of agents dynamically.In the asynchronous backtracking algorithm,the priority order of agents is de-termined,and an agent tries tofind a value satisfying the constraints with the variables of higher priority agents.When an agent sets a variable value,the agent commits to the selected value strongly,i.e.,the selected value will not be changed unless an exhaustive search is performed by lower priority agents.There-fore,in large-scale problems,a single mistake of value selection becomes fatal since doing such an exhaustive search is virtually impossible.This drawback is common to all backtracking algorithms.On the other hand,in the asynchronous weak-commitment search,when an agent can notfind a value consistent with the higher priority agents,the priority order is changed so that the agent has the highest priority.As a result,when an agent makes a mistake in value se-lection,the priority of another agent becomes higher;thus the agent that made the mistake will not commit to the bad decision,and the selected value will be changed.We will show that the asynchronous weak-commitment search algorithm can solve problems such as the distributed1000-queens problem,the distributed graph-coloring problem,and the network resource allocation problem[8]that the asynchronous backtracking algorithm fails to solve within a reasonable amount of time.We can assume that the priority order represents a hierarchy of agentauthority,i.e.,the priority order of decision making.Therefore,these results im-ply that aflexible agent organization,in which the hierarchical order is changed dynamically,actually performs better than an organization in which the hierar-chical order is static and rigid.In the following,we briefly describe the definition of a Distributed CSP and the asynchronous backtracking algorithm(Section2).Then,we show the basic ideas and details of the asynchronous weak-commitment search algorithm(Sec-tion3),and empirical results which show the efficiency of the algorithm(Section 4).Finally,we examine the complexity of the algorithm(Section5).2Distributed Constraint Satisfaction Problem and Asynchronous Backtracking2.1FormalizationA CSP consists of n variables x1,x2,...,x n,whose values are taken fromfi-nite,discrete domains D1,D2,...,D n respectively,and a set of constraints on their values.A constraint is defined by a predicate.That is,the constraint p k(x k1,...,x kj)is a predicate which is defined on the Cartesian product D k1×...×D kj.This predicate is true iffthe value assignment of these variables sat-isfies this constraint.Solving a CSP is equivalent tofinding an assignment of values to all variables such that all constraints are satisfied.A Distributed CSP is a CSP in which variables and constraints are distributed among automated agents.We assume the following communication model.–Agents communicate by sending messages.An agent can send messages to other agents iffthe agent knows the addresses of the agents1.–The delay in delivering a message isfinite,though random.For the trans-mission between any pair of agents,messages are received in the order in which they were sent.Each agent has some variables and tries to determine their values.However, there exist inter-agent constraints,and the value assignment must satisfy these inter-agent constraints.Formally,there exist m agents1,2,...,m.Each variable x j belongs to one agent i(this relation is represented as belongs(x j,i)).Con-straints are also distributed among agents.The fact that an agent k knows a constraint predicate p l is represented as known(p l,k).We say that a Distributed CSP is solved iffthe following conditions are satisfied.1This model does not necessarily mean that the physical communication network must be fully connected(i.e.,a complete graph).Unlike most parallel/distributed algorithm studies,in which the topology of the physical communication network plays an important role,we assume the existence of a reliable underlying commu-nication structure among agents and do not care about the implementation of the physical communication network.This is because our primary concern is the coop-eration of intelligent agents,rather than solving CSPs by certain multi-processor architectures.–∀i,∀x j where belongs(x j,i),the value of x j is assigned to d j,and∀k,∀p l where known(p l,k),p l is true under the assignment x j=d j.2.2Asynchronous BacktrackingA basic algorithm for solving Distributed CSPs called asynchronous backtrack-ing is developed in[13].In this algorithm,agents act asynchronously and con-currently,in contrast to traditional sequential backtracking techniques,while guaranteeing the completeness of the algorithm.Without loss of generality,we make the following assumptions while describ-ing our algorithms for simplicity.Relaxing these assumptions to general cases is relatively straightforward2.–Each agent has exactly one variable.–All constraints are binary.–There exists a constraint between any pair of agents.–Each agent knows all constraint predicates relevant to its variable.In the following,we use the same identifier x i to represent an agent and its variable.We assume that each agent(and its variable)has a unique identifier.In the asynchronous backtracking algorithm,each agent concurrently assigns a value to its variable,and sends the value to other agents.After that,agents wait for and respond to incoming messages.There are two kinds of messages: ok?messages to communicate the current value,and nogood messages to com-municate information about constraint violations.The procedures executed at agent x i by receiving an ok?message and a nogood message are described in Fig.1.An overview of these procedures is given as follows.–After receiving an ok?message,an agent records the values of other agents in its agent view.The agent view represents the state of the world recognized by this agent(Fig.1(i)).–The priority order of variables/agents is determined by the alphabetical or-der of the identifiers,i.e.,preceding variables/agents in the alphabetical or-der have higher priority.If the current value satisfies the constraints with higher priority agents in the agent view,we say that the current value is consistent with the agent view3.If the current value is not consistent with the agent view,the agent selects a new value which is consistent with the agent view(Fig.1(ii)).2In[11],an algorithm in which each agent has multiple variables is described.If there exists no explicit constraint between two agents,there is a chance that an implicit constraint exists between them.In such a case,a new relation between these agents must be generated dynamically.The procedure for adding a new relation is described in[13].The idea of making originally implicit constraints explicit can be found in the consistency algorithms for CSPs.For example,the adaptive consistency procedure[4]adds links to a constraint network while transforming the constraint network toa backtrack-free constraint network.3More precisely,the agent must satisfy not only initially given constraint predicates, but also the new constraints communicated by nogood messages.–If the agent can notfind a value consistent with the agent view,the agent sends nogood messages to higher priority agents(Fig.1(iii)).A nogood mes-sage contains a set of variable values that can not be a part of anyfinal solution.By using this algorithm,if a solution exists,agents will reach a stable state where all constraints are satisfied.If there exists no solution,an empty nogood will be found and the algorithm will terminate4.when received(ok?,(x j,d j))do—(i)add(x j,d j)to agent view;check agent view;end do;when received(nogood,x j,nogood)doadd nogood to nogood list;check agent view;end do;procedure check agent viewwhen agent view and current value are not consistent do—(ii)if no value in D i is consistent with agent view then backtrack;—(iii)else select d∈D i where agent view and d are consistent;current value←d;send(ok?,(x i,d))to other agents;end if;end do;procedure backtracknogoods←{V|V=inconsistent subset of agent view};when an empty set is an element of nogoods dobroadcast to other agents that there is no solution,terminate this algorithm;end do;for each V∈nogoods do;select(x j,d j)where x j has the lowest priority in V;send(nogood,x i,V)to x j;end do;Fig.1.Procedures for receiving messages(asynchronous backtracking)4A set of variable values that is a superset of a nogood can not be afinal solution.If an empty set becomes a nogood,it means that there is no solution,since any set is a superset of an empty set.3Asynchronous Weak-commitment SearchIn this section,we briefly describe the weak-commitment search algorithm for solving CSPs[12],and describe how the asynchronous backtracking algorithm can be modified into the asynchronous weak-commitment search algorithm. 3.1Weak-commitment Search AlgorithmIn the weak-commitment search algorithm(Fig.2),all variables have tentative initial values.We can execute this algorithm by calling weak-commitment ({(x1,d1),(x2,d2),...,(x n,d n)},{}),where d i is the tentative initial value of x i.In this algorithm,a consistent partial solution is constructed for a subset ofvariables,and this partial solution is extended by adding variables one by one un-til a complete solution is found.When a variable is added to the partial solution, its tentative initial value is revised so that the new value satisfies all constraints between the partial solution,and satisfies as many constraints between variables that are not included in the partial solution as possible.This value ordering heuristic is called the min-conflict heuristic[6].The essential difference between this algorithm and the min-conflict backtracking[6]is the underlined part in Fig.2.When there exists no value for one variable that satisfies all constraints between the partial solution,this algorithm abandons the whole partial solution, and starts constructing a new partial solution from scratch,using the current value assignment as new tentative initial values.This algorithm records the abandoned partial solutions as new constraints, and avoids creating the same partial solution that has been created and aban-doned before.Therefore,the completeness of the algorithm(alwaysfinds a so-lution if one exists,and terminates if no solution exists)is guaranteed.The experimental results on various example problems in[12]show that this algo-rithm is3to10times more efficient than the min-conflict backtracking[6]or the breakout algorithm[7].3.2Basic IdeasThe main characteristics of the weak-commitment search algorithm are as fol-lows.1.The algorithm uses the min-conflict heuristic as a value ordering heuristic.2.It abandons the partial solution and restarts the search process if there existsno consistent value with the partial solution.Introducing thefirst characteristic into the asynchronous backtracking algo-rithm is relatively straightforward.When selecting a variable value,if there exist multiple values consistent with the agent view(those that satisfy all constraints with variables of higher priority agents),the agent prefers the value that mini-mizes the number of constraint violations with variables of lower priority agents.In contrast,introducing the second characteristic into the asynchronous back-tracking is not straightforward,since agents act concurrently and asynchronously,procedure weak-commitment(left,partial-solution)when all variables in left satisfy all constraints doterminate the algorithm,current value assignment is a solution;end do (x i,d)←a variable and value pair in left that does not satisfy some constraint;values←the list of x i’s values that are consistent with partial-solution;if values is an empty list;if partial-solution is an empty listthen terminate the algorithm since there exists no solution;else record partial-solution as a new constraint(nogood);remove each element of partial-solution and add to left;call weak-commitment(left,partial-solution);end if;else value←the value within values that minimizesthe number of constraint violations with left;remove(x i,d)from left;add(x i,value)to partial-solution;call weak-commitment(left,partial-solution);end if;Fig.2.Weak-commitment search algorithmand no agent has exact information about the partial solution.Furthermore, multiple agents may try to restart the search process simultaneously.In the following,we show that the agents can commit to their decisions weakly by changing the priority order dynamically.We define the way of establishing the priority order by introducing priority values,and change the priority values by the following rules.–For each variable/agent,a non-negative integer value representing the pri-ority order of the variable/agent is defined.We call this value the priority value.–The order is defined such that any variable/agent with a larger priority value has higher priority.–If the priority values of multiple agents are the same,the order is determined by the alphabetical order of the identifiers.–For each variable/agent,the initial priority value is0.–If there exists no consistent value for x l,the priority value of x l is changed to k+1,where k is the largest priority value of other agents.Furthermore,in the asynchronous backtracking algorithm,agents try to avoid situations previously found to be nogoods.However,due to the delay of mes-sages,an agent view of an agent can occasionally be a superset of a previously found nogood.In order to avoid reacting to unstable situations,and perform-ing unnecessary changes of priority values,each agent performs the following procedure.–Each agent records the nogoods that it has sent.When the agent view is a superset of a nogood that it has already sent,the agent will not change the priority value and waits for the next message.3.3Details of AlgorithmIn the asynchronous weak-commitment search,each agent concurrently assigns avalue to its variable,and sends the value to other agents.After that,agents wait for and respond to incoming messages5.In Fig.3,the procedures executed atagent x i by receiving an ok?message and a nogood message are described6.Thedifferences between these procedures and the procedures for the asynchronous backtracking algorithm are as follows.–The priority value,as well as the current value,is communicated throughthe ok?message(Fig.3(i)).–The priority order is determined by the communicated priority values.Ifthe current value is not consistent with the agent view,i.e.,some constraintwith variables of higher priority agents is not satisfied,the agent changes its value so that the value is consistent with the agent view,and also thevalue minimizes the number of constraint violations with variables of lower priority agents(Fig.3(ii)).–When x i can notfind a consistent value with its agent view,x i sends nogoodmessages to other agents,and increments its priority value.If x i has already sent an identical nogood,x i will not change the priority value and will waitfor the next message(Fig.3(iii)).3.4Example of Algorithm ExecutionWe illustrate the execution of the algorithm using a distributed version of the well-known n-queens problem(where n=4).There exist four agents,each ofwhich corresponds to a queen of each row.The goal of the agents is tofind positions on a4×4chess board so that they do not threaten one another.The initial values are shown in Fig.4(a).Agents communicate these valuesto one another.The values within parentheses represent the priority values.The initial priority values are0.Since the priority values are equal,the priority orderis determined by the alphabetical order of identifiers.Therefore,only the value ofx4is not consistent with its agent view,i.e.,only x4is violating constraints with higher priority agents.Since there is no consistent value,agent x4sends nogoodmessages and increments its priority value.In this case,the value minimizing the number of constraint violations is3,since it conflicts only with x3.Therefore, x4selects3and sends ok?messages to other agents(Fig.4(b)).Then,x3tries to change its value.Since there is no consistent value,agent x3sends nogood messages,and increments its priority value.In this case,the value that minimizes 5Although the following algorithm is described in a way that an agent reacts to messages sequentially,an agent can handle multiple messages concurrently,i.e.,the agentfirst revises agent view and nogood list according to the messages,and performs check agent view only once.6It must be mentioned that the way to determine that agents as a whole have reached a stable state is not contained in this algorithm.To detect the stable state,agents must use distributed termination detection algorithms such as[1].when received(ok?,(x j,d j,priority))do—(i)add(x j,d j,priority)to agent view;check agent view;end do;when received(nogood,x j,nogood)doadd nogood to nogood list;check agent view;end do;procedure check agent viewwhen agent view and current value are not consistent doif no value in D i is consistent with agent view then backtrack;else select d∈D i where agent view and d are consistentand d minimizes the number of constraint violations;—(ii)current value←d;send(ok?,(x i,d,current priority))to other agents;end if;end do; procedure backtrack—(iii)nogoods←{V|V=inconsistent subset of agent view};when an empty set is an element of nogoods dobroadcast to other agents that there is no solution,terminate this algorithm;end do;when no element of nogoods is included in nogood sent dofor each V∈nogoods do;add V to nogood sentfor each(x j,d j)in V do;send(nogood,x i,V)to x j;end do;end do;p max←max(x j,d j,p j)∈agent view(p j);current priority←1+p max;select d∈D i where d minimizes the number of constraint violations;current value←d;send(ok?,(x i,d,current priority))to other agents;end do;Fig.3.Procedures for receiving messages(asynchronous weak-commitment search)the number of constraint violations is1or2.In this example,x3selects1and sends ok?messages to other agents(Fig.4(c)).After that,x1changes its value to2,and a solution is obtained(Fig.4(d)).In the distributed4-queens problem,there exists no solution when x1’s value is1.We can see that a bad decision can be revised without an exhaustive search in the asynchronous weak-commitment search.x 1x 2x 3x 4(a)(b)(c)(d)(0)(0)(0)(1)(0)(0)(2)(1)(0)(0)(2)(1)Fig.4.Example of algorithm execution3.5Algorithm CompletenessThe priority values are changed if and only if a new nogood is found 7.Since the number of possible nogoods is finite,the priority values can not be changed infinitely.Therefore,after a certain time point,the priority values will be stable.Then,we show that the situations described below will not occur when the priority values are stable.(i)There exist agents that do not satisfy some constraints,and all agents are waiting for incoming messages.(ii)Messages are repeatedly sent/received,and the algorithm will not reach a stable state (infinite processing loop).If situation (i)occurs,there exist at least two agents that do not satisfy the constraint between them.Let us assume that the agent ranking k-th in the priority order does not satisfy the constraint between the agent ranking j-th (where j <k ),and all agents ranking higher than k-th satisfy all constraints within them.The only case that the k-th agent waits for incoming messages even though the agent does not satisfy the constraint between the j-th agent is that the k-th agent has sent nogood messages to higher priority agents.This fact contradicts the assumption that higher priority agents satisfy constraints within them.Therefore,situation (i)will not occur.Also,if the priority values are stable,the asynchronous weak-commitment search algorithm is basically identical to the asynchronous backtracking algo-rithm.Since the asynchronous backtracking is guaranteed not to fall into an infinite processing loop [13],situation (ii)will not occur.From the fact that situation (i)or (ii)will not occur,we can guarantee that the asynchronous weak-commitment search algorithm will always find a solution,or find the fact that there exists no solution.4EvaluationsIn this section,we evaluate the efficiency of algorithms by discrete event sim-ulation,where each agent maintains its own simulated clock.An agent’s time 7To be exact,different agents may find an identical nogood simultaneously.is incremented by one simulated time unit whenever it performs one cycle of computation.One cycle consists of reading all incoming messages,performing local computation,and sending messages.We assume that a message issued at time t is available to the recipient at time t+1.We analyze performance in terms of the amount of cycles required to solve the problem.Given this model, we compare the following three kinds of algorithms:(a)asynchronous backtrack-ing,in which a variable value is selected randomly from consistent values,and the priority order is determined by alphabetical order,(b)min-conflict only,in which the min-conflict heuristic is introduced into the asynchronous backtrack-ing,but the priority order is statically determined by alphabetical order,and(c) asynchronous weak-commitment search8.Wefirst applied these three algorithms to the distributed n-queens problem described in the previous section,varying n from10to1000.The results are summarized in Table1.For each n,we generated100problems,each of which had different randomly generated initial values,and averaged the results for these problems.For each problem,in order to conduct the experiments in a reasonable amount of time,we set the bound for the number of cycles to1000, and terminated the algorithm if this limit was exceeded;we counted the result as1000.The ratio of problems completed successfully to the total number of problems is also described in Table1.Table1.Required cycles for distributed n-queens problemasynchronous min-conflict only asynchronousbacktracking weak-commitmentn ratio cycles ratio cycles ratio cycles10100%105.4100%102.6100%41.55050%662.756%623.0100%59.110014%931.430%851.3100%50.810000%—16%891.8100%29.6 The second example problem is the distributed graph-coloring problem.The distributed graph-coloring problem is a graph-coloring problem,in which each node corresponds to an agent.The graph-coloring problem involves painting nodes in a graph by k different colors so that any two nodes connected by an arc do not have the same color.We randomly generate a problem with n nodes/agents and m arcs by the method described in[6],so that the graph is connected and the problem has a solution.We evaluate the problem n=60,90,8The amounts of local computation performed in each cycle for(b)and(c)are equiv-alent.The amounts of local computation for(a)can be smaller since it does not use the min-conflict heuristic,but for the lowest priority agent,the amounts of local computation of these algorithms are equivalent.。