Competitive Coevolution through Evolutionary Complexi?cation
Kenneth O.Stanley and Risto Miikkulainen
Department of Computer Sciences
The University of Texas at Austin
Austin,TX78712USA
kstanley,risto@https://www.doczj.com/doc/4e1309782.html,
Technical Report UT-AI-TR-02-298
December,2002
Abstract
Two major goals in machine learning are the discovery of complex multidimensional solutions and con-
tinual improvement of existing solutions.In this paper,we argue that complexi?cation,i.e.the incremental
elaboration of solutions through adding new structure,achieves both these goals.We demonstrate the power
of complexi?cation through the NeuroEvolution of Augmenting Topologies(NEAT)method,which evolves
increasingly complex neural network architectures.NEAT is applied to an open-ended coevolutionary robot
duel domain where robot controllers compete head to head.Because the robot duel domain supports a wide
range of sophisticated strategies,and because coevolution bene?ts from an escalating arms race,it serves
as a suitable testbed for observing the effect of evolving increasingly complex controllers.The result is an
arms race of increasingly sophisticated strategies.When compared to the evolution of networks with?xed
structure,complexifying networks discover signi?cantly more sophisticated strategies.The results suggest
that in order to realize the full potential of evolution,and search in general,solutions must be allowed to
complexify as well as optimize.
1Introduction
Evolutionary algorithms are a class of algorithms that can be applied to open-ended learning problems in
Arti?cial Intelligence.Traditionally,such algorithms evolve?xed-length genomes under the assumption
that the space of the genome is suf?cient to encode the solution.A genome containing genes encodes
a single point in an-dimensional search space.In many cases,a solution is known to exist somewhere in that space.For example,the global maximum of a function of three arguments must exist in the three
dimensional space de?ned by those arguments.Thus,a genome of three genes can encode the location of
the maximum.
However,many common structures are de?ned by an arbitrary number of parameters.In particular,
those solution types that can contain a variable number of parts can be represented by any number of genes.
For example,the number of parts in neural networks,cellular automata,and electronic circuits can vary
(Miller et al.200a;Mitchell et al.1996;Stanley and Miikkulainen2002d).In fact,two neural networks with
different numbers of connections and nodes can approximate the same function(Cybenko1989).Thus,it is not clear what number of genes is appropriate for solving a particular problem.Because of this ambiguity, researchers evolving?xed-length genotypes must use heuristics,such as smaller neural networks general-izing better than larger ones,in order to estimate a priori the appropriate number of genes to encode such structures.
A major obstacle to using?xed-length encodings is that heuristically determining the appropriate num-ber of genes becomes impossible for very complex problems.For example,how many nodes and connec-tions are necessary for a neural network that controls a ping-pong playing robot?Or,how many bits are needed in the neighborhood function of a cellular automata that performs information compression?The answers to these questions can hardly be based on empirical experience or analytic methods,since little is known about the solutions.One possible approach is to simply make the genome extremely large,so that the space it encodes is extremely large and a solution is likely to lie somewhere within.Yet the larger the genome,the higher dimensional the space that evolution needs to search.Even if a ping-pong playing robot lies somewhere in the10,000dimensional space of a10,000gene genome,searching such a space may take prohibitively long.
Even more problematic are open-ended problems where phenotypes are meant to improve inde?nitely and there is no known?nal solution.For example,in competitive games,estimating the complexity of the “best”possible player is dif?cult because making such an estimate implicitly assumes that no better player can exist.How could we ever know that?Moreover,many arti?cial life domains are aimed at evolving increasingly complex arti?cial creatures for as long as possible(Maley1999).Fixing the size of the genome in such domains also?xes the maximum complexity of evolved creatures,defeating the purpose of the experiment.
In this paper,we argue that the right way to evolve arbitrarily structured phenotypes is to start evolution with a population of small,simple genomes and systematically complexify solutions over generations by adding new genes.That way,evolution begins searching in a small easily-optimized space,and adds new dimensions as necessary.This approach is more likely to discover highly complex phenotypes than an approach that begins searching directly in the intractably large space of complete solutions.In fact,natural evolution has itself utilized this strategy,occasionally adding new genes that lead to increased phenotypic complexity(Martin1999;Section2).In biology,this process is called complexi?cation,which is why we use this term to describe our approach as well.
When a good strategy is found in a?xed-length genome,the entire representational space of the genome is used to encode it.Thus,the only way to improve it is to alter the strategy,thereby sacri?cing some of the functionality learned over previous generations.In contrast,complexi?cation elaborates on the existing strategy by adding new structure without changing the existing representation.Thus the strategy does not only become different,but the number of possible responses to situations it may face increases(?gure1).
This idea is implemented in a method for evolving increasingly complex neural networks,called Neu-roEvolution of Augmenting Topologies(NEAT;Stanley and Miikkulainen2002b,c,d).NEAT begins by evolving networks without any hidden nodes.Over many generations,new hidden nodes and connections are added,resulting in the complexi?cation of the solution space.This way,more complex strategies elabo-rate on simpler strategies,focusing search on solutions that are likely to maintain existing capabilities.We use NEAT to demonstrate the power of complexi?cation.
NEAT was tested in a competitive robot control domain with and without complexi?cation.Coevolu-tion was used to evolve robot controllers against each other to?nd better strategies.We chose this domain because it is open-ended;there is no known optimal strategy but it is possible to come up with increasingly more sophisticated strategies inde?nitely.The main results were that(1)evolution did complexify when possible,(2)complexi?cation led to elaboration,and(3)signi?cantly more sophisticated and successful
Figure1:Alteration vs.elaboration example.A robot(depicted as a circle)evolves to avoid an obstacle.In the alteration scenario(top),the robot?rst evolves a strategy to go around the left side of the obstacle.However,the strategy fails in a future generation when the obstacle begins moving to the left.Thus,the robot alters its strategy by evolving the tendency to move right instead of left.However,when the obstacle later moves right,the new,altered, strategy fails because the robot did not retain its old ability to move left.In the elaboration scenario(bottom),the original strategy of moving left also fails.However,instead of altering the strategy,it is elaborated by adding a new ability to move right as well.Thus,when the obstacle later moves right,the robot still has the ability to avoid it by using its original strategy.Elaboration is necessary for a coevolutionary arms race to emerge and it can be achieved through complexi?cation.
strategies were evolved with complexi?cation than without.These results imply that complexi?cation al-lows coevolution to continually elaborate on successful strategies,resulting in an arms race that achieves a signi?cantly higher level of sophistication than is otherwise possible.
We begin by reviewing biological support for complexi?cation,as well as past work in coevolution, followed by a description of the NEAT method,and experimental results.
2Background
2.1Complexi?cation in Nature
Mutation in nature not only results in optimizing existing structures:New genes are occasionally added to the genome,allowing evolution to perform a complexifying function over and above optimization.In addi-tion,complexi?cation is protected in nature in that interspeci?c mating is prohibited.Such speciation creates important dynamics differing from standard GAs.In this section,we discuss these important characteristics of natural evolution as a basis for our approach to utilize them computationally in genetic algorithms.
Gene duplication is a special kind of mutation in which one or more parental genes are copied into an offspring’s genome more than once.The offspring then has redundant genes expressing the same proteins. Gene duplication has been responsible for key innovations in overall body morphology over the course of natural evolution(Amores et al.1998;Carroll1995;Force et al.1999;Martin1999).
A major gene duplication event occurred around the time that vertebrates separated from invertebrates. The evidence for this duplication centers around HOX genes,which determine the fate of cells along the anterior-posterior axis of embryos.HOX genes are crucial in shaping the overall pattern of developmental in embryos.In fact,differences in HOX gene regulation explain a great deal of arthropod and tetrapod diversity (Carroll1995).Amores et al.(1998)explain that since invertebrates have a single HOX cluster while vertebrates have four,cluster duplication must have signi?cantly contributed to elaborations in vertebrate body-plans.The additional HOX genes took on new roles in regulating how vertebrate anterior-posterior axis develops,considerably increasing body-plan complexity.Although Martin(1999)argues that the additional clusters can be explained by many single gene duplications accumulating over generations,as opposed to massive whole-genome duplications,researchers agree that gene duplication contributed signi?cantly to important body-plan elaboration.
A detailed account of how duplicate genes can take on novel roles was given by Force et al.(1999):Base pair mutations in the generations following duplication partition the initially redundant regulatory roles of genes into separate classes.Thus,the embryo develops in the same way,but the genes that determine overall body-plan are con?ned to more speci?c roles,since there are more of them.The partitioning phase completes when redundant clusters of genes are separated enough that they no longer produce identical proteins at the same time.After partitioning,mutations within the duplicated cluster of genes alter different steps in development than mutations within the original cluster.In other words,duplication creates more points at which mutations can occur.In this way,developmental processes complexify.
In order to implement this idea in arti?cial evolutionary systems we are faced with two major challenges. First,such systems evolve variable-length genomes,which can be dif?cult to cross over without losing in-formation.For example,depending on when duplications occurred in the ancestral histories of two different genomes,the same gene may exist at different positions.Conversely,different genes may exist at the same position.Thus,arti?cial crossover may lose essential genes through misalignment.Second,it may be dif-?cult for a variable-length genome GA to?nd innovative solutions;Optimizing many genes takes longer than optimizing only a few,meaning that more complex genotypes may be eliminated from the population before they have a suf?cient opportunity to be optimized.
How has nature solved these problems?First,nature has a mechanism for aligning genes with their proper counterparts during crossover,so that data is not lost nor obscured.This alignment process has been most clearly observed in E.coli(Radding1982;Sigal and Alberts1972).A special protein called RecA takes a single strand of DNA and aligns it with another strand by attaching the strands at genes that express the same traits,which are called homologous genes.The process by which RecA protein aligns homologous genes is called synapsis.In experiments in vitro,researchers have found that RecA protein does not complete the process of synapsis on fragments of DNA that are not homologous(Radding1982).
Second,innovations in nature are protected through https://www.doczj.com/doc/4e1309782.html,anisms with signi?cantly divergent genomes never mate because they are in different species.Thus,organisms with larger genomes compete for mates among their own species,instead of with the population at large.That way,organisms that may initially have lower?tness than the general population still have a chance to reproduce,giving novel concepts a chance to realize their potential without being prematurely eliminated.
It turns out complexi?cation is also possible in evolutionary computation if abstractions of synapsis and speciation are made part of the genetic algorithm.The NEAT method(section3)is an implementation of this idea:the genome is complexi?ed by adding new genes which in turn encode new structure in the phenotype, as in biological evolution.
Complexi?cation is especially powerful in open-ended domains where the goal is to continually generate more sophisticated https://www.doczj.com/doc/4e1309782.html,petitive coevolution is a particularly important such domain,as will be reviewed in the next section.
2.2Competitive Coevolution
In competitive coevolution,individual?tness is evaluated through direct competition with other individuals in the population,rather than through an objective?tness measure.In other words,?tness signi?es only the relative strengths of solutions;an increased?tness in one solution leads to a decreased?tness for another. Ideally,competing solutions will continually outdo one another,leading to an”arms race”of increasingly better solutions(Dawkins and Krebs1979;Rosin1997;Van Valin1973).
In practice,it is dif?cult to establish an arms race.Evolution tends to?nd the simplest solutions that can win,meaning that strategies can switch back and forth between different idiosyncratic yet uninteresting variations(Darwen1996;Floreano and Nol?1997;Rosin and Belew1997).Several methods have been developed to encourage the arms race(Angeline and Pollack1993;Ficici and Pollack2001;Noble and Watson2001;Rosin and Belew1997).For example,a”hall of fame”can be used to ensure that current strategies remain competitive against strategies from the past.Recently,Ficici and Pollack(2001)and Noble and Watson(2001)introduced a promising method called Pareto coevolution,which?nds the best learners and the best teachers in two populations by casting coevolution as a multiobjective optimization problem.
Although such techniques improve the performance of competitive coevolution,they do not directly en-courage continual coevolution,i.e.creating new solutions that maintain existing capabilities.For example, no matter how well selection is performed,or how well competitors are chosen,if no better solution exists in the?xed solution space,elaboration is impossible since the global optimum has already been reached. Moreover,it may occasionally be easier to escape a local optimum by adding a new dimension of freedom to the search space than by jumping back out into the same space that led to the optimum in the?rst place.
Complexi?cation is an appropriate technique for establishing a coevolutionary arms https://www.doczj.com/doc/4e1309782.html,plexi?-cation naturally elaborates strategies by adding new dimensions to the search space.Thus,progress can be made inde?nitely long:even if a global optimum is reached in the search space of solutions,new dimensions can be added,opening up a higher-dimensional space in which even higher optima may exist.
To test this idea experimentally,we chose a robot duel domain that combines predator/prey interaction and food foraging in a novel head-to-head competition(Section4).We use this domain to demonstrate how NEAT uses complexi?cation to continually elaborate on strategies.The next section describes the NEAT neuroevolution method,followed by a description of the robot duel domain and a discussion of the results. 3NeuroEvolution of Augmenting Topologies(NEAT)
The NEAT method of evolving arti?cial neural networks combines the usual search for appropriate network weights with complexi?cation of the network structure.This approach is highly effective:NEAT outper-forms other neuroevolution(NE)methods,e.g.on the benchmark double pole balancing task by a factor of ?ve(Stanley and Miikkulainen2002b,c,d).The NEAT method consists of solutions to three fundamental challenges in evolving neural network topology:(1)What kind of genetic representation would allow dis-parate topologies to crossover in a meaningful way?Our solution is to use historical markings to line up genes with the same origin.(2)How can topological innovation that needs a few generations to optimize be protected so that it does not disappear from the population prematurely?Our solution is to separate each innovation into a different species.(3)How can topologies be minimized throughout evolution so the most ef?cient solutions will be discovered?Our solution is to start from a minimal structure and grow only when necessary.In this section,we explain how NEAT addresses each challenge.
Figure2:A NEAT genotype to phenotype mapping example.A genotype is depicted that produces the shown phenotype.There are3input nodes,one hidden,and one output node,and seven connection de?nitions,one of which is recurrent.The second gene is disabled,so the connection that it speci?es(between nodes2and4)is not expressed in the phenotype.In order to allow complexi?cation,genome length is unbounded.
3.1Genetic Encoding
Evolving structure requires a?exible genetic encoding.In order to allow structures to complexify,their representations must be dynamic and expandable.Each genome in NEAT includes a list of connection genes,each of which refers to two node genes being connected.(?gure2).Each connection gene speci?es the in-node,the out-node,the weight of the connection,whether or not the connection gene is expressed(an enable bit),and an innovation number,which allows?nding corresponding genes during crossover.
Mutation in NEAT can change both connection weights and network structures.Connection weights mutate as in any NE system,with each connection either perturbed or not.Structural mutations,which form the basis of complexi?cation,occur in two ways(?gure3).Each mutation expands the size of the genome by adding gene(s).In the add connection mutation,a single new connection gene is added connecting two previously unconnected nodes.In the add node mutation an existing connection is split and the new node placed where the old connection used to be.The old connection is disabled and two new connections are added to the genome.The new nonlinearity in the connection changes the function slightly,but new nodes can be immediately integrated into the network,as opposed to adding extraneous structure that would have to be evolved into the network later.
Through mutation,the genomes in NEAT will gradually get larger.Genomes of varying sizes will result,sometimes with different connections at the same positions.A dif?cult problem for crossover,with numerous differing topologies and weight combinations,is an inevitable result of allowing genomes to grow unbounded.Yet such growth is necessary if complexi?cation is to occur.How can NE cross over differently sized genomes in a sensible way?The next section explains how NEAT addresses this problem.
Figure3:The two types of structural mutation in NEAT.Both types,adding a connection and adding a node, are illustrated with the genes above their phenotypes.The top number in each genome is the innovation number of that gene.The bottom two numbers denote the two nodes connected by that gene.The weight of the connection,also encoded in the gene,is not shown.The symbol DIS means that the gene is disabled,and therefore not expressed in the network.The?gure shows how connection genes are appended to the genome when a new connection is added to the network and when a new node is added.Assuming the depicted mutations occurred one after the other,the genes would be assigned increasing innovation numbers as the?gure illustrates,thereby allowing NEAT to keep an implicit history of the origin of every gene in the population.
3.2Tracking Genes through Historical Markings
It turns out that there is unexploited information in evolution that tells us exactly which genes match up
between any individuals in the population.That information is the historical origin of each gene in the
population.Two genes with the same historical origin represent the same structure(although possibly with
different weights),since they were both derived from the same ancestral gene from some point in the past.
Thus,all a system needs to do to know which genes line up with which is to keep track of the historical
origin of every gene in the system.
Tracking the historical origins requires very little computation.Whenever a new gene appears(through
structural mutation),a global innovation number is incremented and assigned to that gene.The innovation
numbers thus represent a chronology of every gene in the system.As an example,let us say the two
mutations in?gure3occurred one after another in the system.The new connection gene created in the
?rst mutation is assigned the number,and the two new connection genes added during the new node
mutation are assigned the numbers and.In the future,whenever these genomes crossover,the offspring will inherit the same innovation numbers on each gene;innovation numbers are never changed.Thus,the
historical origin of every gene in the system is known throughout evolution.
A possible problem is that the same structural innovation will receive different innovation numbers in
the same generation if it occurs by chance more than once.However,by keeping a list of the innovations
that occurred in the current generation,it is possible to ensure that when the same structure arises more than
once through independent mutations in the same generation,each identical mutation is assigned the same
innovation number.Thus,there is no resultant explosion of innovation numbers.
Through innovation numbers,the system now knows exactly which genes match up with which.(?gure
Parent1
Parent2Figure 4:Matching up genomes for different network topologies using innovation numbers.Although Parent 1and Parent 2look different,their innovation numbers (shown at the top of each gene)tell us which genes match up with which without the need for topological analysis.Even without any topological analysis,a new structure that combines the overlapping parts of the two parents as well as their different parts can be created.In this case,equal ?tnesses are assumed,so the disjoint and excess genes from both parents are inherited randomly.The disabled genes may become enabled again in future generations:there’s a preset chance that an inherited gene is disabled if it is disabled in either parent.
4).Genes that do not match are either disjoint or excess ,depending on whether they occur within or outside the range of the other parent’s innovation numbers.When crossing over,the genes in both genomes with the same innovation numbers are lined up.Genes that do not match are inherited from the more ?t parent,or if they are equally ?t,from both parents randomly.
Historical markings allow NEAT to perform crossover without the need for expensive topological anal-ysis.Genomes of different organizations and sizes stay compatible throughout evolution,and the problem of comparing different topologies (Radcliffe 1993)is essentially avoided.Such compatibility is essential in order to complexify structure.However,it turns out that a population of varying complexities cannot main-tain topological innovations on its own.Because smaller structures optimize faster than larger structures,and adding nodes and connections usually initially decreases the ?tness of the network,recently augmented structures have little hope of surviving more than one generation even though the innovations they represent might be crucial towards solving the task in the long run.The solution is to protect innovation by speciating the population,as explained in the next section.
3.3Protecting Innovation through Speciation
NEAT speciates the population so that individuals compete primarily within their own niches instead of with the population at large.This way,topological innovations are protected and have time to optimize their structure before they have to compete with other niches in the population.Protecting innovation through speciation follows the philosophy that new ideas must be given time to reach their potential before they are prematurely eliminated.
Historical markings make it possible for the system to divide the population into species based on topo-logical similarity.We can measure the distance between two network encodings as a simple linear com-bination of the number of excess()and disjoint()genes,as well as the average weight differences of matching genes(
(1)
The coef?cients,,and adjust the importance of the three factors,and the factor,the number of genes in the larger genome,normalizes for genome size(can be set to1if both genomes are small,i.e., consist of fewer than20genes).Genomes are tested one at a time;if a genome’s distance to a randomly chosen member of the species is less than,a compatibility threshold,it is placed into this species.Each genome is placed into the?rst species from the previous generation where this condition is satis?ed,so that no genome is in more than one species.If a genome is not compatible with any existing species,a new species is created.
As the reproduction mechanism for NEAT,we use explicit?tness sharing(Goldberg and Richardson 1987),where organisms in the same species must share the?tness of their niche.Thus,a species cannot afford to become too big even if many of its organisms perform well.Therefore,any one species is unlikely to take over the entire population,which is crucial for speciated evolution to work.The adjusted?tness for organism is calculated according to its distance from every other organism in the population:
Figure5:The Robot Duel Domain.The robots begin on opposite sides of the board facing away from each other as shown by the lines pointing away from their centers.The concentric circles around each robot represent the separate rings of opponent sensors and food sensors available to each robot.Each ring contains?ve sensors,which appear larger or smaller depending on their activations.The robots lose energy when they move around,and gain energy by consuming food(shown as small sandwiches).The food is placed in a horizontally symmetrical pattern around the middle of the board.The objective is to attain a higher level of energy than the opponent,and then collide with it.Because of the complex interaction between foraging,pursuit,and evasion behaviors,the domain allows for a broad range of strategies of varying sophistication.Animated demos of such evolved strategies are available at https://www.doczj.com/doc/4e1309782.html,/users/kstanley/demos.html.
introduced over evolution.Thus,because NEAT protects innovation using speciation,it can start this way, minimally,and grow new structure only as necessary.
New structure is introduced incrementally as structural mutations occur,and only those structures sur-vive that are found to be useful through?tness evaluations.This way,NEAT searches through a minimal number of weight dimensions,signi?cantly reducing the number of generations necessary to?nd a solu-tion,and ensuring that networks become no more complex than necessary.This gradual production of in-creasingly complex structures constitutes complexi?cation.In other words,NEAT searches for the optimal topology by complexifying when necessary.
4The Robot Duel Domain
Our hypothesis is that complexi?cation allows discovering more sophisticated strategies,i.e.strategies that are more effective,?exible,and general,and include more components and variations than do simple strate-gies.To demonstrate this effect,we need a domain where it is possible to develop increasingly sophisticated strategies,and where sophistication can be readily measured.A coevolution domain is particularly appro-priate because a sustained arms race should lead to increasing sophistication.
In choosing the domain,it is dif?cult to strike a balance between being able to evolve complex strategies and being able to analyze and understand them.Pursuit and evasion tasks have been utilized for this purpose in the past(Gomez and Miikkulainen1997;Jim and Giles2000;Miller and Cliff1994;Reggia et al.2001), and can serve as a benchmark domain for competitive coevolution as well.While past experiments evolved either a predator or a prey,an interesting coevolution task can be established if the agents are instead equal and engaged in a duel.To win,an agent must develop a strategy that outwits that of its opponent,utilizing structure in the environment.
In the robot duel domain,two simulated robots try to overpower each other(?gure5).The two robots
begin on opposite sides of a rectangular room facing away from each other.As the robots move,they lose
was turned off in the remaining ten,in order to see how complexi?ed networks compared to those with
?xed topologies throughout evolution.The competitive coevolution setup is described?rst,followed by an
overview of the dominance tournament method for monitoring progress.
5.1Competitive Coevolution Setup
The evolution in the robot duel domain is open-ended in that it never reaches a?nal solution.Thus,the
question in such a domain is whether continual coevolution will take place,i.e.whether increasingly so-
phisticated strategies will appear throughout the run or eventually stop being https://www.doczj.com/doc/4e1309782.html,plexi?cation
should allow continual coevolution.The experiment has to be set up carefully for this process to emerge,
and to be able to identify it when it does.
In competitive coevolution,every network should play a suitable number of games to establish a good
measure of?tness.To encourage interesting and sophisticated strategies,networks should play a diverse
and high quality sample of possible opponents.One way to accomplish this goal is to evolve two separate
populations.In each generation,each population is evaluated against an intelligently chosen sample of
networks from the other population.The population currently being evaluated is called the host population,
and the population from which opponents are chosen is called the parasite population(Rosin and Belew
1997).The parasites are chosen for their quality and diversity,making host/parasite evolution more ef?cient
and more reliable than one based on random or round robin tournament.
A single?tness evaluation included two competitions,one for the east and one for the west starting
position.That way,networks needed to implement general strategies for winning,independent of their
starting positions.Host networks received a single?tness point for each win,and no points for losing.If a
competition lasted750time steps with no winner,the host received0points.
In selecting the parasites for?tness evaluation,good use can be made of the speciation and?tness shar-
ing that already occur in NEAT.Each host was evaluated against the champions of four species with the
highest?tness.They are good opponents because they are the best of the best species,and they are guaran-
teed to be diverse because their compatibility must be outside the threshold(section3.3).Another eight opponents were chosen randomly from a Hall of Fame(Rosin and Belew1997)that contained population
champions from all generations.Together,speciation,?tness sharing,and Hall of Fame comprise a compe-
tent competitive coevolution methodology.Appendix A contains the NEAT system parameter values used
in the experiments.
It should be noted that complexi?cation does not depend on the particular coevolution methodology.
For example Pareto coevolution(Ficici and Pollack2001;Noble and Watson2001)could have been used
as well,and the advantages of complexi?cation would be the same.However,Pareto coevolution requires
every member of one population to play every member of the other,and the running time in this domain
would have been prohibitively long.
In order to interpret experimental results,a method is needed for analyzing progress in competitive
coevolution.The next section describes such a method.
5.2Monitoring Progress in Competitive Coevolution
A competitive coevolution run returns a record of every generation champion from both populations.The
question is how can a sequence of increasingly sophisticated strategies be identi?ed in this data,if one
exists?This section describes the dominance tournament method for monitoring progress in competitive
coevolution(Stanley and Miikkulainen2002a)that allows us to do that.
123456
Figure7:Master tournament ambiguity.Champions of generations one through six are depicted.An arrow pointing from champion to champion denotes that is superior to in a direct288-way comparison.The master tournament reports that champion5defeats four other champions,while champion6only defeats three others.These results imply that5is superior to6.However,when5and6are compared directly against each other,6actually wins.This way,master tournament results can be misleading.A method that captures the order of dominance among different strategies avoids such a confusion.
First we need a method for performing individual comparisons,i.e.whether one strategy is better than another.Because the board con?gurations can vary during the game,champion networks were compared on144different food con?gurations from each side of the board,giving288total comparisons.The food con?gurations included the same9symmetrical food positions used during training,plus an additional2 food items,which were placed in one of12different positions on the east and west halves of the board. Some starting food positions give an initial advantage to one robot or another,depending on how close they are to the robots’starting positions.Thus,the one who wins the majority of the288total games has demonstrated its superiority in many different scenarios,including those beginning with a disadvantage. We say that network is superior to network if wins more comparisons than out of the288total comparisons.
Given this de?nition of superiority,progress can be tracked.The obvious way to do it is to compare each network to others throughout evolution,?nding out whether later strategies can beat more opponents than earlier strategies.For example,Floreano and Nol?(1997)used a measure called master tournament, in which the champion of each generation is compared to all other generation champions.Unfortunately, such methods are impractical in a time-intensive domain such as the robot duel competition.Moreover,the master tournament only counts how many strategies can be defeated by each generation champion,without identifying which ones.Thus,it can fail to detect cases where strategies that defeat fewer previous cham-pions are actually superior(?gure7).In order to track strategic innovation,we need to identify dominant strategies,i.e.those that defeat all previous dominant strategies.This way,we can make sure that evolution proceeds by developing a progression of strictly more powerful strategies,instead of e.g.switching between alternative ones.
The dominance tournament method of tracking progress in competitive coevolution meets this goal (Stanley and Miikkulainen2002a).Let a generation champion be the winner of a288game comparison between the two population champions of a single generation.Let be the th dominant strategy to appear over evolution.Then dominance is de?ned recursively starting from the?rst generation and progressing to the last:1
The?rst dominant strategy is the generation champion of the?rst generation;
dominant strategy,where,is a generation champion such that for all,is superior to (wins the288game comparison with).
This strict de?nition of dominance prohibits circularities.For example,must be superior to strate-gies through,superior to both and,and superior to.We call the th dominant strategy of the run.A network from a later generation that defeats but loses to has circular supe-riority,since it defeats newer strategies but loses to older ones.Since is circular,it would not be entered into the dominance hierarchy.The entire process of deriving a dominance hierarchy from a population is a dominance tournament,where competitors play all previous dominant strategies until they either lose a 288game comparison,or win every comparison to previous dominant strategies,thereby becoming a new dominant strategy.Dominance tournament allows us to identify a sequence of increasingly more sophisti-cated strategies.They also require signi?cantly fewer comparisons than the master tournament(Stanley and Miikkulainen2002a).
Armed with the appropriate coevolution methodology and a measure of success,we can now ask the question:Does the complexi?cation result in more successful competitive coevolution?
6Results
Each of the23evolution runs lasted500generations,and took between5and10days on a1GHz Pentium III processor,depending on the progress of evolution and sizes of the networks involved.The NEAT algorithm itself used less than1%of this computation:most of the time was spent in evaluating networks in the robot duel task.Evolution of fully-connected topologies took about90%longer than structure-growing NEAT because larger networks take longer to evaluate.
We de?ne complexity as the number of nodes and connections in a network:The more nodes and con-nections there are in the network,the more complex behavior it can potentially implement.The results were analyzed to answer three questions:(1)As evolution progresses does it also continually complexify?(2) How is complexi?cation utilized to create more sophisticated strategies?(3)Does complexi?cation allow better strategies to be discovered than does evolving?xed-topology networks?
6.1Evolution of Complexity
NEAT was run thirteen times,each time from a different seed,to verify that the results were consistent.The highest levels of dominance achieved were17,14,17,16,16,18,19,15,17,12,12,11,and13,averaging at15.15(sd=2.54).
At each generation where the dominance level increased in at least one of the thirteen runs,we averaged the number of connections and number of nodes in the current dominant strategy across all runs(?gure8). Thus,the graphs represent a total of197dominance transitions spread over500generations.The rise in complexity is dramatic,with the average number of connections tripling and the average number of hidden nodes rising from0to almost six.In a smooth trend over the?rst200generations,the number of connections in the dominant strategy grows by50%.During this early period,dominance transitions occur frequently (fewer prior strategies need to be beaten to achieve dominance).Over the next300generations,dominance transitions become more sparse,although they continue to occur.
Between the200th and500th generations a staircase pattern emerges,where complexity?rst rises dra-matically,then settles,then abruptly increases again.The reason for this pattern is speciation.While one
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050100150200250300350400450500A v e r a g e C o n n e c t i o n s i n H i g h e s t D o m i n a n t Generation Complexification of Connections 01234567050100150200250300350400450500
A v e r a g e N o d e s i n H i g h e s t D o m i n a n t Generation
Complexification of Nodes Figure 8:Complexi?cation of connections and nodes over generations.The graphs depict the average number of connections and the average number of hidden nodes in the highest dominant network in each generation.Averages are taken over 13complexifying runs.A hash mark appears every generation in which a new dominant strategy emerged in at least one of the 13runs.The graphs show that as dominance increases,so does complexity level on average.The differences in complexity between the average ?nal dominant and ?rst dominant strategies are statistically signi?cant for both connections and nodes ().
species is adding a large amount of structure,other species are optimizing the weights of less complex net-works.While it is initially faster to grow structure until something works,such ad hoc constructions are eventually supplanted by older species that have been steadily optimizing for a long period of time.Thus,spikes in complexity occur when structural elaboration leads to a better strategy,and complexity slowly settles when older structures optimize their weights and overtake more recent structural innovations.Even-tually,even more elaborate structures will emerge and again outperform the older structures.
Thus,there are two underlying forces of progress:the building of new structures,and the continual optimization of prior structures in the background.The product of these two trends is a gradual stepped progression towards increasing complexity.
Importantly,the dominant strategies did not have to complexify;Even though NEAT adds new structure to networks over generations,because of speciation many species remain simple even as some species become more complex.Thus,the results show more than just that the champions of each generation tend to become complex:The dominant strategies,i.e.the networks that have a strictly superior strategy to every previous dominant strategy,tend to be more complex the higher the dominance level .Thus,the results verify that continual progress in evolution is paired with increasing complexity.
6.2Sophistication through Complexi?cation
To see how complexi?cation contributes to evolution,let us observe how a sample dominant strategy de-velops over time.While many complex networks evolved in the experiments,we follow the species that
produced the winning network
in the third run because its progress is rather typical and easy to under-stand.Let us use for the best network in at generation ,and for the th hidden node to arise from a structural mutation over the course of evolution.We will track both strategic and structural innovations in order to see how they correlate.Let us begin with (?gure 9a),when had a mature zero-hidden-node strategy:
’s main strategy was to follow the opponent,putting it in a position where it might by chance
collide with its opponent when its energy is up.However,followed the opponent even when
Figure9:Complexi?cation of a Winning Species.The best networks in the same species are depicted at landmark generations.Nodes are depicted as squares beside their node numbers,and line thickness represents the strength of connections.Over time,the networks became more complex and gained skills.(a)The champion from generation10 had no hidden nodes.(b)The addition of and its respective connections gave new abilities.(c)The appearance of re?ned existing behaviors.
the opponent had more energy,leaving vulnerable to attack.did not clearly switch roles between foraging and chasing the enemy,causing it to miss opportunities to gather food.
.During the next100generations,evolved a resting strategy,which it used when it had signif-icantly lower energy than its opponent.In such a situation,the robot stopped moving,while the other robot wasted energy running around.By the time the opponent got close,its energy was often low enough to be attacked.The resting strategy is an example of improvement that can take place with-out complexi?cation:it involved increasing the inhibition from the energy difference sensor,thereby slightly modifying intensity of an existing behavior.
In(?gure9b),a new hidden node,,appeared.This node arrived through an interspecies mating,and had been optimized for several generations already.Node gave the robot the ability to change its behavior at once into a consistent all-out attack.Because of this new skill,no longer needed to follow the enemy closely at all times,allowing it to collect more food.By implementing this new strategy through a new node,it was possible not to interfere with the already existing resting strategy,so that now switched roles between resting when in danger to attacking when high on energy.This way,the new structure resulted in strategic elaboration.
In(?gure9c),another new hidden node,,split a link between an input sensor and.
Replacing a direct connection with a sigmoid function greatly improved’s ability to attack at appropriate times,leading to very accurate role switching between attacking and foraging.would try to follow the opponent from afar focusing on resting and foraging,and only zoom in for attack when victory was certain.This?nal structural addition shows how new structure can greatly improve the accuracy and timing of existing behaviors.
The analysis above shows that in some cases,weight optimization alone can produce improved strate-gies.However,when those strategies need to be extended,adding new structure allows the new behaviors to coexist with old strategies.Also,in some cases it is necessary to add complexity to make the timing or execution of the behavior more accurate.These results show how complexi?cation can be utilized to produce increasing sophistication.
In order to illustrate the level of sophistication displayed during competition,we conclude this section with a description of an observed competition between two sophisticated strategies,and,from another run of evolution.At the beginning of the competition,and collected most of the available food until their energy levels were about equal.Two pieces of food remained on the board in locations distant
Figure10:Sophisticated Endgame.Robot dashes for the last piece of food while is still collecting the second-to-last piece.Although it appeared that would lose because got the second-to-last piece,(gray arrow),it turns out that ends with a disadvantage.It has no chance to get to the last piece of food before, and has been saving energy while wasted energy traveling long distances.This way,sophisticated strategies evolve through complexi?cation,combining multiple objectives,and utilizing weaknesses in the opponent’s strategy. from both and(?gure10).Because of the danger of colliding with similar energy levels,the naive strategy would be to rush for the last two pieces of food.In fact,did exactly that.However, cleverly forfeit this race for the second-to-last piece,opting to sit still,even though its energy temporarily dropped below’s.After consumed the second-to-last piece,it headed towards the last piece,which was now far away.Even though had less energy,it was closer and got there?rst.It received a boost of energy while wasted its energy running the long distance from its current position.Thus,’s strategy ensured that it had more energy when they?nally met.Robot’s behavior was surprisingly deceptive,showing that a high level of strategic sophistication had evolved.
This analysis of individual evolved behaviors shows that complexi?cation indeed elaborates on exist-ing strategies,and allows highly sophisticated behaviors to develop that balance multiple goals and utilize weaknesses in the opponent.The last question is whether complexi?cation indeed is necessary to achieve these behaviors.
6.3Complexi?cation vs.Fixed-topology Evolution
To see whether complexifying coevolution is more powerful than standard coevolution in a?xed search space,we compared the two methods.Note that it is not possible to compare them using the standard crossvalidation methodology because no external performance measure exists in this domain.However, the evolved neural networks can be compared directly by playing a duel.Thus,a run of?xed-topology coevolution can be compared to a run of complexifying coevolution by playing the highest dominant strategy from the?xed-topology run against the entire dominance ranking of the complexifying run.The highest level strategy in the ranking that the?xed-topology strategy can defeat is a measure of its quality against complexifying coevolution.
By playing every?xed-topology champion against the dominance ranking from every complexifying run,we can measure the performance of non-complexifying evolution.In addition,we established a base-line for comparison by also playing the champions of the complexifying runs against the entire dominance ranking of every other complexifying run.The average levels reached by complexifying and?xed-topology coevolution can then be compared.
Accordingly,the performance of a particular run is the average highest dominance level of the strategies it can defeat among all complexifying runs,normalized by the total number of dominance levels for that
Figure11:The Best Complexifying Network.The highest performing network from complexifying coevolution is depicted.The network has11hidden units and202connections,plotted as in?gure9.This network makes signi?cant use of structure.While it still has basic direct connections,its strategy has been elaborated through the addition of new nodes and https://www.doczj.com/doc/4e1309782.html,teral and recurrent connections are used to make more re?ned decisions based on past experiences.While complexi?cation makes good use of this high-dimensional space,it is dif?cult for?xed-topology evolution to take advantage of similarly complex structures.
run.For example,if a strategy can defeat up to and including the13th dominant strategy out of15,then its performance against that run is
D o m . L e v e l
C o m p l e x i f y i ng
D o m . L e v e l G e n e r a t i o n s Figure 12:Comparing Typical Runs of Complexifying Coevolution and 10-hidden-unit Fixed-Topology Coevo-lution.Dominance levels are charted on the y-axis and generations on the x-axis.A line appears at every generation where a new dominant strategy arose in each run.The height of the line represents the level of dominance.The arrow shows that the highest dominant strategy found in 10-hidden-unit ?xed-topology evolution only performs as well as the 8th dominant strategy in the complexifying run,which was found in the 19th generation!(Average =24,sd =8.8)In other words,only a few generations of complexifying coevolution are as effective as several hundred of ?xed-topology evolution.
6.3.210-Hidden-Unit Fixed-Topology Coevolution
Fixed-topology coevolution uses fully-connected networks with a single hidden layer,and the number of hidden units must be chosen by the experimenter.Thus,in order to make the comparison between ?xed-topology and complexifying coevolution fair,a reasonable size must be chosen for networks in the ?xed-topology runs.One sensible approach is to choose a complexity that approximates the complexity of the highest dominant strategy from the highest performing complexifying run (?gure 11).This strategy utilized 11hidden units and 202connections including both recurrent connections and direct connections from input to output.In order to approximate this complexity,we chose to use 10hidden unit fully recurrent networks with direct connections from inputs to outputs,with a total of 263connections.A network of this type should be able to approximate the functionality of the most effective complexifying strategy.
Three runs of ?xed-topology coevolution were performed with these networks,and their highest domi-nant strategies were compared to the entire dominance ranking of every complexifying run.Their average performances were ,,and ,for an overall average of .Compared to the
performance of complexifying coevolution,it is clear that ?xed-topology coevolution produced consistently much inferior solutions.As a matter of fact,no ?xed-topology run could defeat any of the 13highest
D o m . L e v e l C o m p l e x i f y i ng
D o m . L e v e l G e n e r a t i o n s Figure 13:Comparing Typical Runs of Complexifying Coevolution and 5-hidden-unit Fixed-Topology Coevo-lution.As in ?gure 12dominance levels are charted on the y-axis and generations on the x-axis,a line appears every generation where a new dominant strategy arose in each run,and the height of the line represents the level of dominance.The arrow shows that the highest dominant strategy found in 5-hidden-unit ?xed-topology evolution only performs as well as the 12th dominant in the complexifying run,which was found in the 140th generation.(Average 159,sd =72.0)Thus,even in the best con?guration,?xed-topology evolution takes about twice as long to achieve the same level of performance.
dominant strategies from complexifying coevolution.
This difference in performance can be illustrated by computing the average generation of complexifying coevolution with the same performance as ?xed-topology coevolution.This generation turns out to be 24(sd =8.8).In other words,500generations of ?xed-topology coevolution reach on average the same level of dominance as only 24generations of complexifying coevolution!In effect,the progress of the entire ?xed-topology coevolution run is compressed into the ?rst few generations of complexifying coevolution (?gure 12).
6.3.35-Hidden-Unit Fixed-Topology Coevolution
One of the arguments for using complexifying coevolution is that starting the search directly in the space of the ?nal solution may be intractable.This may explain why the attempt to evolve ?xed-topology solutions at a high level of complexity failed.Thus,in the next experiment we chose instead to evolve fully-connected,fully-recurrent networks with ?ve hidden nodes.After considerable experimentation,we found out that this level of complexity was the most productive for ?xed-topology evolution,allowing it to ?nd the best
口罩的种类、功能以及使用方法详细讲解 冬季由于天气寒冷、空气污染严重、预防传染病等原因，很多人会佩戴口罩，但是市面上售卖的口罩类型有很多种，佩戴哪种口罩能预防病毒传染？不同的口罩有哪些用途？特殊人群应该如何佩戴口罩？在这个特殊时期，小编就为大家带来了佩戴口罩预防口罩的种类选择和使用方法以及一些其他方面的注意事项，希望对大家有所帮助。 一、戴口罩能预防病毒感染吗？ 常见的口罩有以下几种：普通棉布口罩、一次性口罩（如：医用外科口罩）、N95型口罩。 1、一次性口罩（医用外科口罩） 能在一定程度上预防呼吸道感染，无法防霾。 医用外科口罩有三层，从外到内分别是防水层、过滤层、舒适层。舒适层是一层无纺布，佩戴时白色的无纺布朝内，蓝色的防水层朝外，有金属片的一边朝上，不要戴反，橡皮筋挂上双耳后捏紧金属片和鼻子贴合，抚平两颊，使口罩和面部之间尽量不留缝隙。 购买时要注意，应当选择外包装上明确注明“医用外科口罩”字样的口罩。 2、N95型口罩 能有效预防呼吸道感染，可以防霾。 N95型口罩是是NIOSH（美国国家职业安全卫生研究所）认证的9种防颗粒物口罩中的一种。“N”的意思是不适合油性的颗粒（炒菜产生的油烟就是油性颗粒物，而人说话或咳嗽产生的飞沫不是油性的）；“95”是指，在NIOSH 标准规定的检测条件下，过滤效率达到95%。 N95不是特定的产品名称。只要符合N95标准，并且通过NIOSH审查的产品就可以称为“N95型口罩”。
N95型口罩的最大特点就是可以预防由患者体液或血液飞溅引起的飞沫传染。飞沫的大小为直径1至5微米。美国职业安全与健康管理局（OSHA）针对医疗机构规定，暴露在结核病菌下的医务人员必须佩戴N95标准以上的口罩。 相比较医用外科口罩，N95型口罩密闭更好。在选用N95型口罩时，尽量选择不带呼吸阀的N95型口罩。 3、普通棉布口罩 它的材质可能为棉布、纱布、毛线、帆布及绒等，由于材质本身不够致密，无法起到预防感染目的。 二、如何正确佩戴口罩？ N95型口罩 医用外科口罩
英雄传说空之轨迹FC配置魔法
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导力魔法表 原表可查看游击士手册。要查阅各种魔法的配置要求，游戏中查看手册即可。此表和手册中的区别在于： 将魔法按照功能分类。 魔法的范围大小更精确。 增加了各攻击魔法的打击数（对于打击数概念的解释，请参考后文【战斗中获得的耀晶片数量与SEPITH UP奖励】部分） 增加了各魔法的驱动时间比。 注：魔法的范围 小圆、中圆、大圆、直线只是比较笼统的说法，大圆有大有小，直线有宽有窄。这里在范围后面补上了一个数字，以表示圆的半径或直线的宽度。例如“直线１”表示直线宽度为１格；“中圆３”表示圆的半径为３格，实际有效范围的直径为７格；“小圆２”表示圆的半径为２格，实际有效范围的直径为５格。 图示如下： 中圆３的有效范围小圆２的有效范围（黑色方块） □□■■■□□□□□□□□□ □■■■■■□□□■■■□□ ■■■■■■■□■■■■■□ ■■■■■■■□■■■■■□ ■■■■■■■□■■■■■□ □■■■■■□□□■■■□□ □□■■■□□□□□□□□□ 范围（非全体）魔法有两种指定范围的方式：目标指定和地点指定。 目标指定：最为常见。只能以某个敌人或队员/NPC为中心。在魔法驱动期间，如果目标移动，魔法的范围圈会跟着移动，如果目标失效（如战斗不能），范围圈会自动换到另一个有效目标上，因此不会发空。单体魔法也可以算是目标指定。 地点指定：圆范围可以指定在战场任何位置，直线可以朝向任何方向。同样范围大小，地点指定比目标制定有可能覆盖更多目标，但是敌人也有可能在驱动时逃出施法范围。所以最好赶在敌人行动前就发出，或者预估提前量，以免浪费。 此表中，范围的数字后面加了“◇”号的，表示此魔法为地点指定。否则为目标指定。
序章父亲、启程 在开篇剧情结束之后（如果不幸黑屏，请参阅置顶或者精品区本人FAQ，还是无法解决请在吧内求助吧友），小约和小艾需要前往洛连特市的游击士协会（PC版界面右上角可查看小地图），在游击士协会与爱娜对话后去二楼与雪拉对话，听过一些基础知识后艾约被带往导力工房，接下来请按照雪拉的要求合成回路并装到主角的导力器上（请至少合成HP1和行动力1，并在艾导力器上装HP1，约导力器上装行动力1）然后给主角中任意一位的导力器开封结晶孔。导力系统介绍完毕后艾约会前往地下水路，开始实地研修。
PS：在协会获得的游击士手册包含游戏大多数基础知识，且记录有游戏主线与支线任务的完成情况，请新手们务必仔细查阅（话说有些童鞋不知道手册能翻页？……）
PSP版的游击士手册、星杯手册为日文版，汉化版见此： https://www.doczj.com/doc/4e1309782.html,/mxf03/album/%BF%D5%D6%AE%B9%EC%BC%A3-%D3%CE%BB%F 7%CA%BF%CA%D6%B2%E1%2C%D0%C7%B1%AD%CA%D6%B2%E1 ◆主线任务⑴◆实地研修?回收宝物BP：1，500mira 此任务实质为战斗系统教学，请仔细看剧情并按照指示行动 研修结束后，前往里农杂货铺购买《利贝尔通讯》（在买书之前把身上钱全花完的话小约会帮忙付），可获得料理手册并学会枫糖曲奇的制作，并可以开始使用料理系统，料理一般需在酒店饭馆等处购买，也有宝箱开的和任务获得的。 ☆【料理手册】此时可获得的料理：枫糖曲奇、炸薯条(亚班特酒馆、格鲁纳门)、花色苏打（亚班特酒馆）、洋红之眼（亚班特酒馆）、一口气薏粉（亚班特酒馆，大盘料理） ☆收集齐全料理并没有什么特别奖励……没有收集癖的话无所谓，对于新手来说用料理不仅回复效果不错（非战斗状态建议去旅馆或者免费回复点回复，比较省钱），很多料理除了回 复HP外还附带特别效果，做料理也比买药便宜。
功能性绝缘的定义： 3.3.5 functional insulation insulation between conductive parts of different potential which is necessary only for the proper functioning of the appliance。 功能性绝缘的电气间隙的要求： 29.1.4 For functional insulation, the values of table 16 are applicable. However, clearances are not specified if the appliance complies with clause 19 with the functional insulation short-circuited. Lacquered conductors of windings are considered to be bare conductors. However, clearances at crossover points are not measured. 看蓝色字体部分，只要在短路时，能满足clause 19的要求，可不要求functional insulation 的电气间隙。 看红色字体部分，漆包线被视为“裸露导体”。也就是在本标准中，漆包膜不被视为功能性绝缘。这个前提很重要，这意味著绕组间不必进行“短路测试”。 功能性绝缘的爬电距离的要求： 29.2.4 Creepage distances of functional insulation shall be not less than those specified in table 18. However, creepage distances may be reduced if the appliance complies with clause 19 with the functional insulation short-circuited. 看蓝色字体部分，只要在短路时，能满足clause 19的要求，functional insulation 的爬电距离的要求可减小。标准并没有说可以减小到多少，这就意味著，只要functional insulation 能在短路时满足clause 19的要求即可满足爬电距离的要求。 结论：从标准的理解来看，functional insulation 仅为设备正常工作所需的绝缘，如果functional insulation 能在短路时满足Clause 19 的要求，可不要求其电气间隙和爬电距离。 实际上，我也是这么处理的。只要在检查、评估中发现functional insulation 的距离（cl & cr）很小，那么就用短路测试来检验，如果满足不了短路测试，那么我就要求其CL & CR。 我们在日常判断一个器具的功能绝缘其实无处不在，只要存在电势差的两点都可视为功能绝缘，但绝大部分情况下短路都是可以满足Clause 19的要求的，所以我们考核功能绝缘的地方就大大减少了。典型的功能绝缘的例子是L/N输入的地方，发热元件或马达元件的输入端子之间等等 功能绝缘的定义为保障产品功能的绝缘类型,它不像基本、附加、加强绝缘是用来防触电的,也就是说如果这类绝缘距离不够的话,可能会对最终的用户造成触电的危险,而功能绝缘的距离即使不够,用户也不会有触电危险,只不过会造成产品无法正常工作,它也可以用来减少起火的危险.例如电流保险丝之前火线和零线之间或是变压器内置温度保险丝两脚之间的距离
：FC时期导力器很好，5-2链，由于驱动2在FC时期的属性值还算是?不错的，双时限定影响不大，短链正好装攻击，长链则用于配魔法，是最早?能合出炽炎地狱的人，SC导力器降级为5-3链，由于高级回路的出现驱动2?的属性值沦为鸡肋，所以其导力器基本与4-3链无异，还好小约SC,3RD主要?是菜刀，影响也不大 小约可以完全按菜刀回路配，也可以给自己配个强音之力复
配出火山之咆哮和龙卷火焰也是不错的，菜刀的同时偶尔还能砸个魔法 艾斯蒂尔：4-4链无回路限制，看似不怎么样的导力器其实有很多种配法，?比如星之守护，全回复复，暗物质3RD小艾的太极轮是附加伤害仅次于樱花残月的S技，金刚击的伤害也不错?，还是建议不要弄成纯辅助的 小艾的下面这种配法有时改时减、强音复、鬼魅、沉默十字、大地之墙、火?山之咆哮、龙卷火焰、暗物质改等主打魔法，无论是用作辅助还是主攻都是不错的
小艾的纯奶妈回路，主打时改时减全回复，这里银耀是为了出死亡咆哮，银?耀换成封魔/石化之理可出大地之墙，换成红耀可兼顾攻击
满足一些星守控的配法，主打时改时减、强音复、星守、鬼魅、沉默十字、?暗物质改，把机功换成机功2还可出火山之咆哮 奥利维尔：一链，中心孔幻限定，虽然幻限定比较鸡肋，总体来说还是不错?的导力器，但是3RD男性法师由于女性魔装黑丝绸和ATS武器的问题很是悲?剧，于是裸ATS与公主相同，仅次于玲的王子殿下变成了辅助流。。。但是?王子那糟糕的战技论辅助实用度又不如神父，所以。。。真让人同情啊--=
满足一些灾难冲击波控的配法，主打时改时减、强音复、灾难冲击波、虚弱领域、导力停止、龙卷火焰、银色荆棘 传说中的根源屏障（--=无敌鸡肋魔法，普通难度完全无用），主打时改时?减、强音复、星守、导力停止、鬼魅、沉默十字、暗物质改、死亡咆哮，另?外附带范围2
序章父亲、启程 在开篇剧情结束之后，小约和小艾需要前往洛连特市的游击士协会（PC版界面右上角可查看小地图），在游击士协会与爱娜对话后去二楼与雪拉对话，听过一些基础知识后艾约被带往导力工房，接下来请按照雪拉的要求合成回路并装到主角的导力器上（请至少合成HP1和行动力1，并在艾导力器上装HP1，约导力器上装行动力1）然后给主角中任意一位的导力器开封结晶孔。导力系统介绍完毕后艾约会前往地下水路，开始实地研修。
完成情况，请新手们务必仔细查阅（话说有些童鞋不知道手册能翻页？……）
◆主线任务⑴◆实地研修?回收宝物BP：1，500mira 此任务实质为战斗系统教学，请仔细看剧情并按照指示行动。 研修结束后，前往里农杂货铺购买《利贝尔通讯》（在买书之前把身上钱全花完的话小约会帮忙付），可获得料理手册并学会枫糖曲奇的制作，并可以开始使用料理系统，料理一般需在酒店饭馆等处购买，也有宝箱开的和任务获得的。 ☆【料理手册】此时可获得的料理：枫糖曲奇、炸薯条(亚班特酒馆、格鲁纳门)、花色苏打（亚班特酒馆）、洋红之眼（亚班特酒馆）、一口气薏粉（亚班特酒馆，大盘料理） ☆收集齐全料理并没有什么特别奖励……没有收集癖的话无所谓，对于新手来说用料理不仅回复效果不错（非战斗状态建议去旅馆或者免费回复点回复，比较省钱），很多料理除了回复HP外还附带特别效果，做料理也比买药便宜。 ★【红耀石】在研修结束后请尽快与在亚班特酒店左边的民家里的雷特拉对话（此人所在房间内全是书架），可获得《红耀石》第１卷（此卷若错过之后还有次机会购得），若已触发解救孩子的剧情与雷特拉对话就无法获得此书了。 ★此书关系到最终武器的获得，漏掉任何一卷都将无法获得最终武器，若想获得最终武器请收集齐全。 ◆主线任务⑵◆保护孩子们BP：3+1（选择和约修亚一起出击+1），1000mira
强劲入门单反佳能650D功能全面解析（全文） 佳能（中国）有限公司正式发布了全新的EOS 系列3位编号机型EOS 650D。EOS 650D是广受好评的EOS 600D (参数图片文章)后继机型，大幅提升对焦及连拍性能，搭载触控技术的大型可旋转液晶监视器以及众多创意功能，为普及型数码单反相机定义新高度。新型CMOS图像感应器有效像素约1800万，与能高效处理图像并控制相机功能的DIGIC 5数字影像处理器组合，实现了高画质、高感光度低噪点。自动对焦系统搭载了中央八向双十字全9点十字型自动对焦感应器。最高约5张/秒的高速连拍可准确捕捉被摄体，不错过精彩瞬间。 （1/17） 转发到微博 佳能新一代入门级数码单反650D官方产品图
此外，佳能EOS 650D还搭载了实时显示拍摄和短片拍摄时提高自动对焦性能的Hybrid CMOS AF系统，位于图像感应器中央区域的部分像素用于相差检测自动对焦，快速检测合焦位置后，再进行反差检测自动对焦完成精确合焦。沿袭自EOS 600D的3.0″可旋转液晶监视器在EOS系列中首次采用触控面板，带来了直观的操作与便捷的拍摄。短片拍摄模式中搭载了“短片伺服自动对焦”，可以在短片拍摄期间对被摄体进行自动对焦追踪，还能用内置立体声麦克风录音，进行充满临场感、更出色的短片拍摄。另外，支持作品创作的新功能“手持夜景”模式和“HDR逆光控制”模式拓展了表现范围。EOS 650D预计于2012年6月下旬发售。 佳能EOS 650D单反 佳能650D有哪些功能超越了同类入门单反？ ·中央八向双十字全9点十字型自动对焦系统
·最高约5张/秒高速连拍 ·新研发CMOS图像感应器搭载Hybrid CMOS AF实现短片及实时显示拍摄高速对焦 ·支持短片拍摄中持续追踪被摄体的短片伺服自动对焦·搭载触控技术的大型可旋转液晶监视器以及众多创意功能EOS 650D是EOS数码单反相机系列中首款搭载了全十字型自动对焦感应器的3位编号机型。9个自动对焦点都配置了对应F5.6光束的十字型自动对焦感应器，可以不受被摄体形状和拍摄位置影响进行稳定对焦。使用频率高的中央对焦点斜上还配置了搭配大光圈镜头时可发挥效果的对应F2.8光束十字型自动对焦感应器。对应F2.8和F5.6光束的双十字型自动对焦提高了对焦精度。 取景器内的自动对焦点分布 中央八向双十字全9点十字型自动对焦感应器 自动对焦模式设置画面 自动对焦模式有3种，包括适合拍摄静止被摄体的单次自动对焦、动态被摄体追踪能力出色的人工智能伺服自动对焦和可以在单次自动对焦和人工智能伺服自动对焦之间自动切换的人工智能自动对焦。
支线详细图文攻略 作者：cokefish 第一章 ★来たれ食材ハンター[每样各4个以上BP+1] 魔獣の骨 魔獣の牙 魔獣の角 魔獣の鳥肉 魔獣の魚卵 魔獣の鳥卵 单纯的找怪麻烦...只要在海道以及通往学园的街景林道上就可以打全所需的食材。 注: 魔獣の鳥肉..魔獣の鳥卵打通往学园路上的大脚鸟..这两个掉落率似乎不太高..没有幸运回路的话就得多打几次了 此处有机会碰到熊猫..会掉落小艾目前最好的武器...熊猫的攻击力很高..大概2500左右的伤害........ ★ <<紺碧の塔>>の写真撮影[由小艾在正中拍摄BP+1,由朵洛希BP+2] 时间上的限定不是很严苛。可以等到幽灵事件逐渐明了，朵洛希加入队伍时完成。 来到塔顶，有３个点可供拍摄选择，左右两点拍出来的效果最好，不过．．．人家要的是研究照片．．．你的ＢＰ可就看不到附加咯。在拍摄时小艾会停下来问让她来拍是否恰当,选择第2项：由朵洛希来拍。这时候便会看到朵洛希的经典台词“好可爱啊～～～笑一个～～～”了。 当然了，你也可以鼓励小艾去拍，不过即使选择最好的取景点，拍出的也不过是“很不错”的而已，而朵洛希的则是．．．宽屏的．．．１６：９的。ＴＴ同样的相机，专业和业余就差这么多啊。 ★灯台の試運転 你有空的时候（比如做“メーヴェ海道の手配魔獣”任务时），可以顺便到灯塔去看望一下那个固执的老头。 当小艾亲切地和老头打过招呼后正欲出走时，发现他似乎面有难色，便又亲切地接下这个任务（对话选择第一项），原来是老人的腰痛了，无法去操作灯塔，便请小艾代劳了，没办法，不想被人背后说游击士缺少热情，勉为其难了。书架上有说明书，导力灯的具体操作
镜头最全面解析 镜头的全面分析解剖，全面的分析监控镜头，没有比我们更全的描述了，经过几年的搜集整理，今天真功夫安防慢慢分析，摄像机镜头的作用是把被观察目标的光像呈现在摄像机的靶面上，也称光学成像。将各种不同形状、不同介质（塑料、玻璃或晶体）的光学零件（反射镜、透射镜、棱镜）按一定方式组合起来，使得光线经过这些光学零件的透射或反射以后，按照人们的需要改变光线的传输方向而被接收器件接收，即完成了物体的光学成像过程。光学镜头应满足成像清晰、透光率强、像面照度分布均匀、图像畸变小、光圈可调等要求。一般来说每个镜头都由多组不同曲面曲率的透镜按不同间距组合而成。间距和镜片曲率、透光系数等指标的选择决定了该镜头的焦距。 一、镜头分类 摄像机镜头按其功能和操作方法分为常用镜头和特殊镜头两大类。常用镜头又分为定焦镜头（自动和手动光圈）和变焦镜头（自动和手动光圈）。特殊镜头是根据特殊工作环境而专门设计的，一般有广角镜头、针孔镜头等。 二、镜头参数 a、相对孔径 光圈的主要作用是通过控制镜头光量的大小满足成像所需的合适照度。光圈越大，靶面成像照度越大，摄像机输出信号强度越大，信噪比越高。若光圈的实际孔径为ψ，由于光线通过透镜后的折射使镜头的有效孔径D 比实际孔径大，光圈的相对孔径等于有效孔径与镜头焦距之比，即：A=D/f，f 为镜头的焦距。 b、光圈系数 通常将表征镜头光圈大小的参数定义成光圈系数，用F 表示。光圈系数为镜头光圈相对孔径的倒数。F值的规律是后一个值正好是前一个数值的√2 倍，这是由于成像面中心亮度与（1/F）2成正比。F值越小相应灵敏度越大。常用值为1.4、2、2.8、4、5.6、8、11、 16、22等几个等级。 c、视场角 我们常用视场角来表征观察景物的范围。所谓视场角是指在视场角内的景物可全部落入成像尺寸内，而视场角以外的景物将不被摄取。因此，镜头的视场角与摄像机的靶面及镜头的焦距有关。 根据几何原理可以得到视场角的计算公式如下： ωH＝2tg-1（h/2f）ωV＝2tg-1（v/2f） 式中ωH为水平视场角，ωV为垂直视场角，f 为镜头的焦距，h为摄像机靶面的水平宽度，v为摄像机靶面的垂直高度。具体数值可参阅摄像机一节。 当成像尺寸确定后，焦距越短，视场角越大。因此可将镜头分为长角镜头（视场角小于45°）、标准镜头（视场角为45°～50°）、广角镜头（视场角大于50°）、超广角镜头（视场角接近180°）、鱼眼镜头（视场角大于180°）等。 另外，我们还可以得到如下公式以计算其中任一未知项数据。 f/v＝D/V f/h＝D/H 其中：f───镜头焦距；D───镜头至景物距离； h───靶面宽度；H───靶面高度。 三、镜头接口 镜头的安装方式有C型安装和CS型安装两种。C型安装接口指从镜头安装基准面到焦点的距离为17.526mm，而CS型接口的镜头安装基准面到焦点距离为12.5mm。因此将C型镜头安装到CS接口摄像机时需要加装一个5mm厚的接圈。
PC版游戏的第0页有自动存档，如果不幸卡关或者黑屏死机什么的，请读第0页自动存档 地图原尺寸普遍较大，由于度娘会自动缩图，如果觉得看不清楚请点开原图查看 序章父亲、启程 在开篇剧情结束之后（如果不幸黑屏，请参阅置顶或者精品区本人FAQ，还是无法解决请在吧内求助吧友），小约和小艾需要前往洛连特市的游击士协会（PC版界面右上角可查看小地图），在游击士协会与爱娜对话后去二楼与雪拉对话，听过一些基础知识后艾约被带往导力工房，接下来请按照雪拉的要求合成回路并装到主角的导力器上（请至少合成HP1和行动力1，并在艾导力器上装HP1，约导力器上装行动力1）然后给主角中任意一位的导力器开封结晶孔。导力系统介绍完毕后艾约会前往地下水路，开始实地研修。
PS：在协会获得的游击士手册包含游戏大多数基础知识，且记录有游戏主线与支线任务的完成情况，请新手们务必仔细查阅（话说有些童鞋不知道手册能翻页？……）
PSP版的游击士手册、星杯手册为日文版，汉化版见此： https://www.doczj.com/doc/4e1309782.html,/mxf03/album/%BF%D5%D6%AE%B9%EC%BC%A3-%D3%CE%BB%F 7%CA%BF%CA%D6%B2%E1%2C%D0%C7%B1%AD%CA%D6%B2%E1 ◆主线任务⑴◆实地研修?回收宝物BP：1，500mira 此任务实质为战斗系统教学，请仔细看剧情并按照指示行动 研修结束后，前往里农杂货铺购买《利贝尔通讯》（在买书之前把身上钱全花完的话小约会帮忙付），可获得料理手册并学会枫糖曲奇的制作，并可以开始使用料理系统，料理一般需在酒店饭馆等处购买，也有宝箱开的和任务获得的。 ☆【料理手册】此时可获得的料理：枫糖曲奇、炸薯条(亚班特酒馆、格鲁纳门)、花色苏打（亚班特酒馆）、洋红之眼（亚班特酒馆）、一口气薏粉（亚班特酒馆，大盘料理） ☆收集齐全料理并没有什么特别奖励……没有收集癖的话无所谓，对于新手来说用料理不仅回复效果不错（非战斗状态建议去旅馆或者免费回复点回复，比较省钱），很多料理除了回 复HP外还附带特别效果，做料理也比买药便宜。
红耀石 第1卷 研修结束后，与洛连特民家里的雷特拉对话可得到(我也想不起是那个房子里了,不过好在地方不大)。另柏斯（柏斯超市）有售。 第2卷 一章开始时，与守卫威尔特桥的士兵哈罗德对话可得到(一共就两个兵不是这个就是那个了，好像应该是看门的那个)。另柏斯（柏斯超市）有售。 第3卷 在得知摩尔根将军回来后，与哈肯大门酒吧中的马尔科对话可得到。 第4卷 准备前往瓦雷利亚湖时，去柏斯超市与里布罗对话可得到（就是在超市卖书的那个地方一个新的NPC，好像是在那打工的）。 第5卷 学园祭结束后，去玛诺利亚村前，与卢安市的玛奇尔达对话可得到(一进城坐在桥上的一个女孩)。 第6卷 第三章开始时，去杰尼丝王立学院社团大楼资料室找帕布对话可得到。 第7卷 中央工房事件后，去沃尔费堡垒，与布鲁诺对话可得到。 第8卷 进入终章，返回艾尔?雷登关所，与一楼接待士兵奥塔对话可得到。 第9卷 终章一开始，尽快去格鲁纳门找士兵塞维安对话可得到。 第10卷 剧情到夜晚去大圣堂时，先去空港与在那的拉尔夫对话可得到。 第11卷 武术大会第三天，与王都东区的安敦对话，让他看到中意的女性NPC即可得到。注意场景切换后要重新来过。 集齐全部11卷红耀石后，在进行到集合游击士解放离宫作战的时候，可以去『巴拉尔咖啡厅』，找老板对话。接着就能选择艾丝蒂尔用的「太极棍」或是约修亚用的「黑千鸟?白千鸟」。 序章父亲～起程 (总任务数：7 + 9 = 16，55 BP) （主线）实地研修·回收宝物BP：1 （主线）保护孩子们BP：3 + 1（和约修亚一起出击+1） （主线）剿灭帕赛尔农场的魔兽BP：1 + 2（没有捕捉失败+2/失败一次+1） （主线）克劳斯市长的委托BP：4 （主线）记者们的向导BP：4 （主线）市长官邸的强盗事件BP：6 + 5
PS：PC版游戏的第0页有自动存档，如果不幸卡关或者黑屏死机什么的，请读第0页自动存档 在开篇剧情结束之后，小约和小艾需要前往洛连特市的游击士协会，在游击士协会与爱娜对话后去二楼与雪拉对话，听过一些基础知识后艾约被带往导力工房，接下来请按照雪拉的要求合成回路并装到主角的导力器上（至少合成HP1和行动力1，并在艾导力器上装HP1，约导力器上装行动力1）然后给主角中任意一位的导力器开封结晶孔。导力系统介绍完毕后，艾约会前往地下水路，开始实地研修。 任務主线任务：实地研修 期限- 攻略此任务实质为战斗系统教学，请仔细看剧情并按照指示行动 报酬基本 BP：1 奖励：- 奖金：500mira 【料理手册】此时可获得的料理： 里农杂货铺：枫糖曲奇HP80 亚班特酒馆： 1）炸薯条 HP100 MOV+1 （在以后去杰尼斯王立学院之学园祭的时候也能买到,格鲁纳门卖沙拉三明治的小姑娘也卖这个） 2）花色苏打 HP250 中毒解除（蔡斯市也能买到） 3）洋红之眼 HP100 黑暗解除（蔡斯市也能买到） 4）一口气薏粉（大盘料理） HP180，大盘料理不可以战斗中使用，是平时补充角色用的，比如以后打特务部队长等难度较高的boss前需要以最佳状态出战就需要用到大盘料理了。 收集齐全料理并没有什么特别奖励。没有收集癖的话无所谓，对于新手来说用料理不仅回复效果不错（非战斗状态建议去旅馆或者免费回复点回复，比较省钱），很多料理除了回复HP外还附带特别效果，做料理也比买药便宜。 【红耀石】在研修结束后请尽快与在亚班特酒店左边的民家里的雷特拉对话（最内的屋子），可获得《红耀石》第１卷（此卷若错过之后还有次机会购得），若已触发解救孩子的剧情与雷特拉对话就无法获得此书了。 此书关系到最终武器的获得，漏掉任何一卷都将无法获得最终武器，若想获得最终武器请收集齐全。 【装备回路】武器店能买到： 1）武器：双头棍（艾），苦无（约） 2）衣服：皮革背心，金属背心 3）靴子：皮靴，防滑靴 4）饰品：银耳环，光明背带，黑色手镯 在武器店能买到苦无的话会使接下来的战斗轻松一些，小约双连击可以一回合搞定怪物，如果还有行动加1的回路就更好了。由于游戏初期遇到的魔兽怕火，搞一个攻击１的火系回路也不错。 研修结束后，前往里农杂货铺购买《利贝尔通讯》（在买书之前把身上钱全花完的话小约会帮忙付），可获得料理手册并学会枫糖曲奇的制作，并可以开始使用料理系统，料理一
【图文新手教程】空之轨迹SC流程攻略（附街道/城市/迷宫全地图） 前言 请先玩空之轨迹FC再来游戏，本攻略涉及少量只有玩过FC才会知道的名词，默认所有读者都玩过空之轨迹FC。本攻略主要对应空之轨迹SC的PC版，PSP版的用户也可以参考，两个版本不同的地方我尽量说明。游戏本体的下载、版本问题、OP黑屏问题、存档继承等等问题请到我的空间看一下。 https://www.doczj.com/doc/4e1309782.html,/gaocheche/blog/item/e81467ee947c68f12f2e21bc.html 本攻略会一直更新，查看最新版攻略请到这里看看（PDF版、EPUB版等其他版本也请到这里下载） https://www.doczj.com/doc/4e1309782.html,/gaocheche/blog/item/2b938253292e510442a75bbd.html 序章：少女的决意 艾丝蒂尔从晨曦中醒来，新的旅程开始了！人物可控之后直接去天台触发剧情即可，之后是漫长的剧情，再次可控之后从飞艇坪回布莱特家，这段路上会路过洛连特，有爱的同学可以跟各种NPC对话一下，会有很多收获的。到家之后又是剧情，之后序章正式开始。 舞台转移到列曼自治州卢·洛克训练场，人物可控后去二楼房间的床上拿装备，如果有继承SC完美存档的话此时还会得到一块塞姆里亚石，终章可以用来制作最终武器。下楼之后调查亚尼拉丝旁边的椅子坐好，之后会获得新型战术导力器、各属性耀晶片和FC通关奖励(机械手套、圣灵药、土人偶、幸运{回路，提升魔兽的掉宝率30%，刷东西非常有用})，接着跟菲莉斯管理员对话获得料理手册，买点食材做几个“大自然恩惠之水”，这个之后可以用来救命+回复HP，还可以购买《利贝尔通讯第1号》（这个收集了也没用，就是看的，这时不买可以在第八章买到）和料理“香草三明治”。之后去跟维修师罗伯特对话，会获得关于导力器的基础知识，接着合成一些基本回路，建议给小艾一个HP1，亚尼拉丝一个攻击1。准备好了之后离开宿舍，到南边出口跟克鲁茨对话，接着前往SC的第一个迷宫：巴斯塔尔水道。
中式装修风格 中式装修风格一般是指明清以来逐步形成的中国传统风格装修，这 种风格最能体现出中式的家具风与传统文化的审美，造型讲究对称，色彩讲究对比。中式材料以木材为主，图案以龙、凤、龟、狮等为搭配。精雕细琢、瑰丽奇巧。中式装修的墙、地面与普通装修没有什么区别，墙面用白色乳胶漆，也可以用浅色壁纸。地面用木地板、石材、地砖、地毯也都可以。中式装饰材料以木质为主，讲究雕刻彩绘，造型典雅，多采用酸枝木或大叶檀等高等硬木。经过工艺大师的精雕细刻，每件作品都有一段精彩的故事。而每件作品都能令人对过去产生怀念，对未来产生一种美好的向往。 色彩以深色沉稳为主，因中式家居色彩一般比较深，这样整个居室色彩才能协调，再配以红色或黄色的靠垫、坐垫就可烘托出居室的氛围，这样也可以更好地表现古典家具的涵。空间上讲究层次，多用隔窗、屏风来分割，用实木做出结实的框架，以固定支架，中间用棂子雕花，做成古朴的图案。门窗对确定中式风格很重要，因中式门窗一般均是用棂子做成方格或其他中式的传统图案，用实木雕刻成各式题材造型，打磨光滑富有立体感。天花以木条相交成方格形。上附木板，也可以做成简单的环形灯池吊顶，用实木做块，层次清晰，漆成花梨木色。家具设讲究对称，重视文化意蕴，配饰善用字画、古玩、卷轴、盆景、精致的工艺品加以点缀。更显主人的品位与尊贵。木雕画以壁画为主， 更具有文化韵味和独特风格。体现中国传统家居文化 的独特魅力
现代简约装修风格 现代人面临着城市喧嚣和污染，激烈的竞争压力，还有忙碌的工作和紧的生活。因而，更加向往清新自然、随意轻松的居室环境。越来越多的都市人开始摒弃繁缛豪华的装修，力求拥有一种自然简约的居室空间。 风格总述，现代简约装修风格以宁缺毋滥为精髓，合理的简化居室空间。一切从功能出发，讲究造型比例适度，空间结构明确美观，强调外观的明快、简洁。从简单舒适中体现出生活的精致，在材料上首选铁质构件、铝塑板或者合金材料。在设计上，会把机构组织曝露在外，注重室外整个房间的沟通与搭配。在颜色上，多用白色、灰色作为主基调色，并搭配其他颜色的家具，表现个性及章理。风格特点, 墙面、地面、顶棚一集家具设，乃至灯具器皿等均以简洁的造型、纯洁的质地、精细的工艺为其特征。家具突出强调功能性设计，设计线条简约流畅。家居色彩对比强烈，这是现代风格家具的特点。一些线条简单，设计独特极富创意和个性的饰品，都可以称为现代简约风格家装中的一员。 欧式装修风格 提到欧式风格，总让人联想到欧洲宫廷的雅典与精致。它在注重艺术感的同时，又体现了极强的实用性和空间感。欧式风格代表的是 一种生活态度：精致、舒适、豪华、经得起推敲。又带一点点不经意，它更具历史的沧桑感，也许可以流传于几代人的生活，就像那雕刻的时光，高尚、醇厚而又久远。因而，历久弥香。
市场部功能全面解析 企业的销售部门与市场部门是企业营销的两大基本职能部门。市场部门的任务是解决市场对企业产品的需求问题，销售部门的任务是解决市场能不能买到产品的问题，这两个问题同时作用于市场，就是我们今天所做的市场营销工作。市场是企业的龙头，是企业实现产品变成资金、变成利润的主要职能部门；企业生产产品数量、生产产品品种、生产产品规格、产品包装、产品外观等等必须以市场为导向。这些观念已被我们的国内企业接受。有的国内企业已将这些观念在市场实战运用自如，倍尝甜头。 但是，到了20世纪末，一切都在快速发展，于是股东、企业、企业家、职业经理人又觉得这样销售太慢，在寻找诸如“肥猪粉”、“丰乳剂”之类的快速成长添加剂加速销售额、销售量的增长，实现利润的最大化。企业纷纷设立市场部或企划部，进行了第二次营销行动。将广告、策划、宣传、创意、品牌、形象、市场定位等视为神明，认为“广告一响，黄金万两”。有的企业运用市场及广告到了惊天动地的地步，一年之内投入广告一个亿、五个亿已经司空见惯。 纵观百年国际著名消费品公司，都是一步一个台阶、一步一个脚印地发展市场，稳步地增长销售。这些公司有一个共同的营销特征，就是销售与市场二者之间协调的很好，既重视销售，又重视市场，将“拉”与“推”二种力量有机地结合了起来。 下面，本人就根据自己多年市场实战经验，针对市场或企划的基本功能和职责问题加以阐述，并就几个市场部的日常策略设计思路拿出来，供读者参考，也便于心急的企业、心急的人们能清醒地认识市场或企划的真正含义
和作用，以免将市场或企划导入歧途，招人谩骂。 第一部分市场部及各岗位职责 一、市场部的职责 市场部的主要职责有十五大方面。 01、制定年度营销目标计划。 02、建立和完善营销信息收集、处理、交流及保密系统。 03、对消费者购买心理和行为的调查。 04、对竞争品牌产品的性能、价格、促销手段等小的收集、整理和分析。 05、对竞争品牌广告策略、竞争手段的分析。 06、做出销售预测，提出未来市场的分析、发展方向和规划。 07、制定产品企划策略。 08、制定产品价格。 09、新产品上市规划。 10、制定通路计划及个阶段实施目标。 11、促销活动的策划及组织。 12、合理进行广告媒体和代理上的挑选及管理。 13、制定及实施市场广告推广活动和公关活动。 14、实施品牌规划和品牌的形象建设。 15、负责产销的协调工作。 市场部在产品不同阶段侧重点各有不同。 1．在产品导入期，市场部的职责重点有：对消费者购买心理行为的调查；制定产品上市规划；制定通路计划及个阶段实施目标；制定产品价格；制定
英雄传说6全任务BP导览 ================================== 序章父亲～起程 (总任务数：7 + 9 = 16，55 BP) （主线）实地研修·回收宝物BP：1 （主线）保护孩子们BP：3 + 1（和约修亚一起出击+1） （主线）剿灭帕赛尔农场的魔兽BP：1 + 2（没有捕捉失败+2/失败一次+1） （主线）克劳斯市长的委托BP：4 （主线）记者们的向导BP：4 （主线）市长官邸的强盗事件BP：6 + 5（推理正确+4[参考28楼]，唔……只好忍一下+1） （主线）市长官邸的强盗事件②BP：－ （支线）寻找发光的石头BP：2 （支线）牛奶小街的通缉魔兽BP：3 （支线）更换路灯BP：3 + 1 （开锁密码输入成功或交给约修亚来更换+1）（支线）训练士兵BP：3 + 2 （战斗胜利+2） （支线）采蘑菇BP：3 （支线）采集药材BP：3 （支线）寻找小猫BP：2 （支线）艾利兹街道通缉魔兽BP：4 （支线）送亲笔信BP：2 ================================== 第一章消失的定期船 (总任务数：5 + 11 = 16，64 BP) （主线）定期船失踪事件BP：5 + 3 （主线）定期船失踪事件②BP：－ （主线）南街区的强盗事件BP：10 （主线）南街区的强盗事件②BP：－ （主线）南街区的强盗事件③BP：－ （支线）收集食物材料BP：3 （支线）东柏斯街道的通缉魔兽BP：4 （支线）搜寻山道的魔兽BP：4 （支线）迷雾峡谷的通缉魔兽BP：5 （支线）调查熊刺草BP：4 （支线）安塞尔新街的通缉魔兽BP：5 （支线）护卫委托BP：4 + 1 （支线）西柏斯街道的通缉魔兽BP：4 （支线）被盗的戒指BP：3 （隐藏）琥珀之塔的可疑人物BP：4 （隐藏）黑色笔记本BP：5
简介 主人公为成龙.内容是邪恶的大妖术师,无常童子绑架的成龙的恋人明铃,成龙为了救出恋人而斩妖除魔的故事. 游戏画面清新,带有浓厚的中国风味,人物刻画形象生动,人设Q化非常讨人喜爱.表情丰富夸张,动作功夫一招一式也有板有眼,体现出了中国功夫的神彩.比较有创意的就是奖励关了,为游戏添光加彩,难度不是很大,但要想看到礼花也不是很容易的. 优点: 简单明快的横版动作 轻松欢快的BGM 必杀技的描绘 缺点: 不取得ITEM无法发出必杀 Continue画面的意思无法理解 主人公是否以成龙为原形,无从判别出来. 系统介绍 操作 A键跳跃 B键直拳攻击 按住B键闪光时放开飞镖攻击(共5颗)
蹲下时B键扫荡腿 跳跃时B键空中飞脚 必杀技上+B键 根据所加球状物生成不同的必杀技.共有四种: 旋风腿:范围较大的必杀,左右两个方向的敌人都可以攻击到.最大值是7个. 横踢:单个方向的攻击,相对攻击面较小些.但实用性非常强.最大值是9个. 倒勾:只能向上攻击方向攻击,无敌时间几乎没有,但打出来的几率也并不高.最大值是7个. 飞滚:向所朝方向翻滚冲击,拥有最长的无敌时间.最大值7个. p.s. 像空中飞脚或必杀横踢快速左右转向也可以做出两面的攻击. ITEM 游戏中时常遇到青蛙,打中它,就会掉血碗或球状物,不同的球状物有不同的必杀技. Special Stage 游戏里某些地方隐藏着铃铛,(靠近时才能出现,个人认为在左侧较易将其引出),接触后就能进入Special Stage special stage 其中的内容
累积积分,给予加血,人,飞镖(按顺序)的奖励
如果全部踩中(或打中),还会放出礼花. p.s. 在每次Continue之后.仍然会出现各处的铃铛. 特别在Stage4Area4中,每掉落从头开始,都可以进入铃铛中隐藏铃铛的地点: Stage1 Area1 内容:跳云彩 Stage1 Area3 内容:消灭木头人 Stage2 Area1 内容:保护方块
空之轨迹FC——隐藏任务攻略 空之轨迹F C是英雄传说轨迹系列第?部，这部作品可以所?最经典的R P G之?，特别是游戏中的?乐?常有感觉，?且很多玩家也是冲着这款游戏??的?乐来的。 隐藏任务攻略 第?章消失的定期船 1琥珀之塔的可疑?物 委托期限：和将军会?~从王国军那?被释放 报酬：B P42000?拉 经安塞尔新街前往琥珀之塔——打到5层——救下被魔兽攻击的亚鲁?教授 2??笔记本 委托期限：?(发现??笔记本时)~第?章通过古罗尼的?顶关所 报酬：B P52000?拉 在空贼基地B2北侧仓库——调查导?吸尘器发现笔记本——交给哈肯?门地牢的?兵 第?章?花恋诗 1扫荡灯塔的魔兽 委托期限：第?章开始~开始校园?活 报酬：B P41500?拉 到巴伦诺灯塔与弗科特??对话——逐层打上去消灭所有魔兽 2救?任务 在途径梅威海道由刚认识的公主带着去卢安时在沙滩上有?个象“?线天”的地?(就是在?出沙滩的陆地中间有?个可以过去的?缝，在沙滩上时尽量贴?出沙滩的陆地的边?应该能发现)，靠近救的是后来帮他找航海图的吉?，如果救了他在之后的帮他找航海图的任务完成后会有额外的B P 和?拉奖励 3剿灭旧校舍的魔兽 委托期限：在学校遇到乔?和汉斯~学院祭开始 报酬：B P31000?拉 从学校东边的出?到旧校舍——与??处的遇到学??克对话——消灭魔兽(魔兽带毒性攻击，最好装备银?环)
4装饰校园 委托期限：在学校遇到乔?和汉斯~学院祭开始 报酬：B P1500?拉 与学院主楼前的勤务员巴克拉对话——调查以下三个地?——主楼右侧通往社团?楼的?廊——北边男?宿舍前没有挂到旗?处——礼堂?门的右边 5收集资料 委托期限：在学校遇到乔?和汉斯~学院祭开始 报酬：B P1500?拉 与社团?楼2楼的资料室中的?对话——找到放置在以下3个地?的书——社团?楼2楼的男更?室椅?上——教职员室的桌?上——男?宿舍阿吉尔的房间书桌上——交给资料室中的? (以上三个任务是在最后?天排练之后?约他们说要?起吃饭，让?艾和公主去找乔?时完成的，先不要去找乔?，完成以上3个任务以后再去，否则剿灭旧校舍的魔兽会变成?质战提?了难度) 第三章??导?器 1修理运输车 委托期限：完成寻找运输车的?线任务后~提妲开始修理?泵 报酬：B P51500?拉 完成寻找运输车的?线任务后——与中央?房3层设计室的普罗梅笛对话——到5层演算室——通过导?演算机查询运输车的相关情报——到卡鲁迪亚隧道和中央?房的连通处——找??鲁迪拿“驱动?的导?器”——返回运输车事故现场对话 2爱之使者 委托期限：去亚尔摩村修理?泵~提妲开始修理?泵 报酬：B P22000?拉 前往沃尔费堡垒——同守卫?兵布拉姆对话——将情书带给中央?房B1?场的菲。如果在送情书的同时送去礼物会有额外的B P奖励，如果 送“绒?编制帽”奖励B P+4，?王诞?时在望都空港看见的情侣是菲和布拉姆。如果送“果馅饼”或“?作?套 的奖励是B P+2，?王诞?时在望都空港看见的情侣是菲和鲁迪 (以上两个任务都要在进?红叶亭要钥匙之前完成) 如果完成所有?线任务和以上的隐藏任务并在有的任务中会出现的对话选择中选择B P和?拉奖励最?的选项，在离开蔡斯之前就可以成为准游击?1级
PSP空之轨迹FC完整攻略 这游戏画面非常不错，虽说这是老游戏，但是个人认为非常经典，我在以前在电脑上玩了一遍，PSP出了后我又回味一遍感觉还是不错啊！献给喜欢这个游戏的您。 - 1 -
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故事开篇，女主角艾丝蒂尔(エステル)在家中等待父亲的归来，此时父亲却怀抱一名受伤的黑发男孩(土产?!)出现在家中，并告知艾丝蒂尔自己将要收养这名男孩。 一转眼5年时间过去，艾丝蒂尔和当年的男孩约修亚(ヨシュア)都已长大成人。这天父亲要求2人前往附近的ロレント城进行准游击士的测试，顺便买一本当前热门的杂志利贝尔通信(リベール通信)最新号回来。两人来到北方城镇内的游击士协会(地图上红色的房子就是)，在2楼见到一位游击士的前辈雪拉莎德(シェラザード)，(因为很熟所以主角们都称她为雪拉姐)。闲聊过后，雪拉告知主角2人参加实地研修，首先来到楼下与柜台姐姐对话，会得到一本手帐，里面详细记录了各种技能的获得方法以及任务的报告书，此后使用V键就可叫出。协会里竖立的扳子上可以看到目前的各种任务，上面写有期限的限制以及高中低难度的划分。 之后在雪拉的带领下来到结晶工房(店名是以老板的名字命名的，懒得打了|||)，从雪拉手中得到各色晶石各一颗，同右上的年轻技师谈话，选择第2项改造·换金，再选第2项进行合成，这里似乎要将所有技能都合成一个，和雪拉对话后将“水”属性的“HP1”技能装备给女主角(选择菜单上的Orbment进行装备)，男主角则装备“时”之属性的“行动力1”。再次和雪拉对话，再对话。然后再同右上技师谈话，这次选择改造·换金后选第1项，对一名人物的技能装备槽进行开封。最后一项实习的内容是前往下水道，在雪拉的带领下(强行带走^ ^)来到入口，进入后就开始打怪吧~。这里的敌人都不强，注意按照提示进行各种战斗方法的实习，最后来到左下角，会发现一个被魔兽守护着的宝箱!打倒敌人后得到小箱x2。将小箱交给雪拉后回到游击士协会，与柜台姐姐对话，选择第2项“报告”，此后每次完成任务后进行报告，就可以获得报酬以及游击士等级的鉴定。再次来到协会2楼，打开刚刚获得的小箱，发现其中所装的是准游击士的纹章，此时2人就真正的成为了准游击士了! 准备回家吧，走出游击士协会的大门后，会看到2个小孩正在嬉闹着说要去秘密基地，对话后往下方城门迈进，经过杂货店的时候别忘记去买父亲要的杂志哦~~。和杂货店的老板对话还可获得料理手帐，按C键即可叫出。出城的时候被游击士协会的姐姐叫住，原来刚才的2个小孩竟然跑去了北方的翡翠之塔!准游击士的第一项任务:保护2个小孩子平安归来。从北面出口进入山道，在第2张地图选择左面岔路一直前进(这里大地图有落差@_@，站在塔下时显示我在矿山~)，来到塔内2层，打倒敌人后救得2个小孩(战斗中要小心保护小孩子，千万不能让他们的HP变0 啊~~)。艾丝蒂尔气愤的教训小孩的时候一只魔兽从后方出现，千钧一发之时父亲出现，一击打倒敌人~。 好了~~这次真的可以回家了。将杂志和在协会得到的信件交给父亲，那封信竟然是帝国方面的联络!晚餐时父亲透露了将要出任务的消息，第2天清早2人为父亲送行……序章正式开始，先到游击士协会，和柜台姐姐对话后接到西方农场治退魔兽的委托。 此时协会看板上的任务有“光る石の搜索”(低)以及“ミルヒ街道の手配魔兽”(低)。 光る石の搜索:与结晶工房左侧门外的人对话，得知他将光る石掉落在杂货店门口，要求主角2人进行搜索，在杂货店门外的排水口发现闪光的东西后，前往礼拜堂后的下水道入口，在左下部分会发现光る石。打退石头引来的魔兽后就去还给石头的主人吧。 ミルヒ街道の手配魔兽:由西侧城门出去后第3张地图的西北方向，可以看到一只比较不同的- 3 -