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Strong AI and the Chinese Room Argument, Four views

Joris de Ruiter

3AI, Vrije Universiteit Amsterdam

jdruiter@few.vu.nl

First paper for: FAAI 2006

Abstract

Strong AI is the view that the human mind is a computational device and computers are in principle capable of thought. In 1980, Searle published a paper which argued against this position by means of a thought experiment: the chinese room. In the years to come, many comments were made to this paper, of which we will discuss two. Central questions in this paper will be whether strong AI is true, and whether it is possible to create a true 'Artificial Intelligence'. All four views (strong AI, Searle, Harnad and Churchland) differ from one another, and all will be summarized and discussed.

In short, this paper discusses some important philosophical underpinnings of AI, by summarizing and discussing the views of three authors and strong AI itself.

Introduction

In 1980, Searle defined Strong AI, and argued against it by means of the Chinese Room Argument (CRA). Searle's argument was (and is) a direct challenge to proponents of Artificial Intelligence (AI), and the argument also has broad implications for functionalist and computational theories of meaning and of mind. As a result, there have been many critical replies to it.

In this paper we discuss two such replies (from Harnad and Churchland1), along with Searle's chinese room argument and of course strong AI itself. Central questions will be:

(1) is strong AI true?

(2) is strong AI possible, and how?

Note that this paper requires no prior knowledge of the philosophy of AI: all necessary terms will be explained. To begin with, we start with an explanation of Strong AI.

What is strong AI?

Strong AI is the view that the human mind is a computational device and computers are in principle capable of thought[1]. Supporters of strong AI believe that an appropriately programmed computer isn't simply a simulation or model of a mind, it actually would count as a mind. That is, it understands, has cognitive states, and can think.

The term was originally coined by John Searle, who writes:

"According to strong AI, the computer is not merely a tool in the study of the mind; rather, the appropriately programmed computer really is a mind"[2]

By contrast, 'weak AI' is the view that computers are merely useful in psychology, linguistics, and other areas, in part because they can simulate mental abilities. Weak AI makes no claim that computers actually understand or are intelligent.

Strong AI states that all there is to having a mind (having mental/cognitive states), is running a program (the right program of course). By program, we mean a sequence of steps, an algorithm. Programs executed on a computer are always purely symbolic (consisting only of symbols like letters or digits (0/1)). Because of that, computation is pure symbol manipulation (e.g. manipulation of the zero's and one's).

This leads us to computationalism, which states more or less the same as strong AI2. For reasons of simplicity, we use them interchangeably.

Computationalism is the theory that cognition is computation, that mental states are just computational states. According to Harnad [3], the following can be said of computationalism:

(1) Mental states are just implementations of (the right) computer program(s).

(Otherwise put: Mental states are just computational states).

(2) Computational states are implementation-independent. (Software is hardware-

independent).

1 When referring to Churchland, we actually refer to 'the churchlands', a a married couple, both professors of philosophy. Sometimes the singular form is more suitable in a sentence, sometimes the plural. In both cases, we refer to both of them, since they have written the paper[4] together.

2 For reasons of simplicity, we see strong AI, computationalism and functionalism as the same, and use them interchangeably.

The same holds for understanding, consciousness, intentionality, intelligence, and mentality (having a mind). When we say that a computer has intentionality, or that it can truly understand, we usually mean all these words.

If we combine (1) and (2) we get: Mental states are just implementation-

independent implementations of computer programs. This is not self-

contradictory. The computer program has to be physically implemented as a

dynamical system in order to become the corresponding computational state, but the physical details of the implementation are irrelevant to the computational

state that they implement -- except that there has to be some form of physical

implementation. Radically different physical systems can all be implementing one and the same computational system.

So basically, computationalism/strong AI states that running a program is enough for mentality, and that mentality is implementation-independent. This has some far-reaching implications:

- We now know how the mind works (including consciousness, etc), namely by just running a program

- Whether you run that program on a human or computer3, the result is the same (understanding, consciousness, etc).

- A computer can display any systematic pattern of responses to the environment whatsoever, and can have all mental states that humans have. It's just a matter of finding the right computer program (given enough time and storage space). Note that this is exactly what AI-researchers are doing: making intelligent programs.

- The programs that we make to simulate intelligence (e.g. Eliza, SAM, SHRDLU), are truly intelligent, and explain human intelligence

To make matters more concrete, let's consider such a program: SAM (Script Applier Mechanism), made by Roger Schank in 1977 [6]. Note that "nothing that follows depends upon the details of Schank's programs. The same arguments (coming after the story) would apply to Winograd's SHRDLU [7], Weizenbaum's ELIZA [8], and indeed any Turing machine simulation of human mental phenomena."[2]

Searle describes the program as follows[2]: (shortened)

"Very briefly, one can describe Schank's program as follows: the aim of the

program is to simulate the human ability to understand stories. It is characteristic of human beings' story-understanding capacity that they can answer questions

about the story even though the information that they give was never explicitly

stated in the story. Thus, for example, suppose you are given the following story:

A man went into a restaurant and ordered a hamburger. When the

hamburger arrived it was burned to a crisp, and the man stormed

out of the restaurant angrily, without paying for the hamburger

or leaving a tip.

Now, if you are asked 'Did the man eat the hamburger?', you will presumably

answer, 'No, he did not.' Schank's machines can similarly answer questions about restaurants in this fashion. "

According to Searle[2], partisans of strong AI claim that in this 'question and answer sequence', "the machine is not only simulating a human ability, but also that the machine can literally be said to understand the story and provide the answers to questions, and that what the machine and its program do explains the human ability to understand the story and answer questions about it."[2]

3 Note that a program can not only be run on a human or computer, but also on all sorts of crazy implementations like a roll of toilet paper, a pile of small stones, or a system of waterpipes and valves. To computationalism, all of these can have mentality, since they are all able to run a program. The question is just a matter of writing the right program, and enough time/spead and storage space.

Because of the huge size (10 billion neurons) and speed of the brain, these crazy implementations will not be suitable for reproducing the brain in real-time, but in principle they can.

These are exactly the claims that Searle likes to refute with his chinese room argument. But before going in to that, let's explore a bit more about computationalism.

According to Harnad[3], there's a third proposition about computationalism:

(3) There is no stronger empirical test for the presence of mental states than

Turing-Indistinguishability; hence the Turing Test is the decisive test for a

computationalist theory of mental states.

According to him, "this does not imply that passing the Turing Test (TT) is a guarantor of having a mind or that failing it is a guarantor of lacking one. It just means that we

cannot do any better than the TT, empirically speaking. Whatever cognition actually turns out to be -- whether just computation, or something more, or something else -- cognitive science can only ever be a form of 'reverse engineering' [10] and reverse-engineering has only two kinds of empirical data to go by: structure and function (the latter including all performance capacities). Because of tenet (2), computationalism has eschewed structure; that leaves only function. And the TT simply calls for functional equivalence (indeed, total functional indistinguishability) between the reverse-engineered candidate and the real thing."[3]

Now that we know what computationalism/strong AI is, we can ask two questions:

(1) is strong AI true? Are all claims made by strong AI true?

(2) is strong AI possible, and how? Can we really build an understanding computer, a

true 'Artificial Intelligence'? The holy grail of AI, a computer which can truly reason, solve problems, speaks fluently natural language, is intelligent and sapient (self-aware); is it possible? And if so, how?

Both questions will be addressed after the introduction, where we will discuss the views of computationalism and three writers (Searle, Harnad en Churchland).

About (1), we can already say that there's lots of discussion needed before we would have a final conclusion. We would have to discuss all objections to the CRA, and probably also understanding, consciousness, the symbol grounding problem, etc. It suffices to say that each of these topics has generated it's own pile of papers and books. In this paper we only discuss computationalism, and some of the arguments made by Searle, Harnad en Churchland.

About (2), we can already say that it can be answered in two ways, theoretically and practically. The theoretical approach will likely be (again) a long discussion, so let's take a look at the practical approach. Why don't we just skip the question, and start actually making the programs? And when we have a good enough program (e.g. which passes a test), we say, yes, this is strong AI!

Such an approach has indeed been taken. It's called the Turing test, and it tests the machine's capability to perform human-like conversation. If we cannot distinguish between a human and a machine in human-like conversation, the test concludes that the machine also has mentality.

This sounds like a great solution for believers of computationalism, because it ignores structure and only calls for functional equivalence (see tenet (3) above). Also, no introspectivism is needed (which suits cognitive scientists), and instead of long theoretical discussions, we can just start making programs (which suit pragmatists and

AI researchers).

Unfortunately, not everyone agrees. One of the objections phrased is that the machine only simulates mentality, while not actually understanding the conversation. This is one

of the things Searle intends to show with his chinese room argument: even if a program is indistinguishable from a human (it passes Turing test), it still understands nothing, and therefore, the Turing test is not a good enough test for understanding.

So alas, there's no simple way out for this one, we're condemned to dive into the theoretical discussion. As said, we will do this by summarizing and discussing the views of computationalism and three writers (Searle, Harnad en Churchland). Hopefully, some interesting conclusions will come out.

Views on Strong AI

We had 2 questions to answer:

(1) is strong AI true?

(2) is strong AI possible, and how?

Below we quickly summarize the viewpoints of computationalism, Searle, Harnad and Churchland, after which we will go more into depth into each of them.

To begin with, all disagree with one another on the first question. Computationalism of course agrees with itself. Searle does not, and lays out the chinese room argument to

'proof' computationalism is wrong. Harnad agrees with that proof, but points out the CRA is limited. The Churchlands disagree with Searle, and point to a fault in the CRA.

On the second question, all agree that strong AI is possible, but disagree on how to achieve it.

According to computationalism, having a mind is just a question of executing the right program, so we just have to write the right program (assuming it exists). Searle argues against this, by stating that even by executing the right program (a program which passes the Turing test), a computer will still understand nothing. According to him, a computer needs to have the same causal structures as a brain. Harnad and Churchland are also against computatationalism, but think hybrid or noncomputational systems (like artificial neural networks) will be the solution.

Computationalism:

Computationalism states that mentality is implementation-independent: whether you run a program on a human or computer, the result is the same (understanding, consciousness, etc). This is quite a big statement, and our intuition may think the opposite, so let's see what idea's lay behind it.

In his paper 'is the brain a digital computer'[11], Searle describes the 'primal story', which he describes as "a story about the relation of human intelligence to computation that goes back at least to Turing's classic paper[12]". He begins the primal story as follows: (slightly modified)

"We begin with two results in mathematical logic, the Church-Turing thesis and

Turing's theorem. For our purposes, the Church-Turing thesis states that for any

algorithm there is some Turing machine that can implement that algorithm (given enough time and storage space). Turing's theorem says that there is a Universal

Turing Machine which can simulate any Turing Machine. Now if we put these two

together, we have the result that a Universal Turing Machine (UTM) can

implement any algorithm whatever" [11].

Because the computer is a UTM, the computer can implement any algorithm. Now if computationalism is right, brains are Universal Turing Machines as well, and this would result in all far-reaching implications we described before.

Now, are there good reasons for supposing the brain might be a Universal Turing Machine?

"It is clear that at least some human mental abilities are algorithmic. For example,

I can consciously do long division by going through the steps of an algorithm for

solving long division problems. It is furthermore a consequence of the Church-

Turing thesis and Turing's theorem that anything a human can do algorithmically can be done on a Universal Turing Machine. I can implement, for example, the

very same algorithm that I use for long division on a digital computer. In such a

case, as described by Turing (l950), both I, the human computer, and the

mechanical computer are implementing the same algorithm, I am doing it

consciously, the mechanical computer nonconsciously.

Now it seems reasonable to suppose there might also be a whole lot of mental

processes going on in my brain nonconsciously which are also computational. And if so, we could find out how the brain works by simulating these very processes on

a digital computer. Just as we got a computer simulation of the processes for

doing long division, so we could get a computer simulation of the process for

understanding language, visual perception, categorization, etc." [11]

And so computatationalism concludes that all our brains processes are computational, and can therefore be simulated on a computer. Even more, this simulations are not just simulations, but the real thing; running the program is enough for mentality.

And so AI becomes a search for these programs, with results such as SAM, SHRDLU and ELIZA. And it is at this moment, that Searle comes in to say that those simulations are really just simulations, that we are not computers, and that syntax (symbol manipulation) is not enough for semantics.

Searle:

In 1980, Searle published a paper called 'Minds, brains, and programs' in which he defined strong AI (computationalism), and tries to prove it's wrong. He does this by laying out the chinese room argument, which shows that syntax is not enough for semantics, and therefore, that running a program cannot be sufficient for having a mind. According to him, for a computer to really understand natural language, it needs to have the same causal structures as the brain.

Below we will discuss these matters in more depth, beginning with a description of the chinese room argument.

According to Wikipedia, the Chinese room argument, goes as follows:

"Suppose that, many years from now, we have constructed a computer which

behaves as if it understands Chinese. In other words, the computer takes Chinese symbols as input, consults a large look-up table (as all computers can be

described as doing), and then produces other Chinese symbols as output.

Suppose that this computer performs this task so convincingly that it easily

passes the Turing test. In other words, it convinces a human Chinese speaker that it is a Chinese speaker. All the questions the human asks are responded to

appropriately, such that the Chinese speaker is convinced that he or she is talking to another Chinese speaker. The conclusion proponents of strong AI would like to draw is that the computer understands Chinese, just as the person does.

Now, Searle asks us to suppose that he is sitting inside the computer. In other

words, he is in a small room in which he receives Chinese symbols, looks them up on look-up table, and returns the Chinese symbols that are indicated by the table.

Searle notes, of course, that he doesn't understand a word of Chinese.

Furthermore, his lack of understanding goes to show, he argues, that computers

don't understand Chinese either, because they are in the same situation as he is.

They are mindless manipulators of symbols, just as he is - and they don't

understand what they're 'saying', just as he doesn't.

The conclusion of this argument is that running a program cannot create

understanding. The wider argument includes the claim that one cannot get

semantics (meaning) from syntax (formal symbol manipulation)." [13]

We might summarize this argument as a reductio ad absurdum against Strong AI:

"Let L be a natural language, and let us say that a 'program for L' is a program for conversing fluently in L. A computing system is any system, human or otherwise, that can run a program.

(1) If Strong AI is true, then there is a program for Chinese such that if any

computing system runs that program, that system thereby comes to understand

Chinese.

(2) I could run a program for Chinese without thereby coming to understand

Chinese.

(3) Therefore Strong AI is false.

The second premise is supported by the Chinese Room thought experiment."[13]

There've been lot's of objections raised against the chinese room argument. A summary of these can be found in the Stanford encyclopedia of philosophy [9]. For now it suffices to say that the fight is still going on:

"The many issues raised by the Chinese Room argument will not be settled until

there is a consensus about the nature of meaning, its relation to syntax, and

about the nature of consciousness. There continues to be significant disagreement about what processes create meaning, understanding, and consciousness, and

what can be proven a priori by thought experiments." [9]

So then Searle, if pure symbol manipulation cannot achieve strong AI, what can?

His view is that "only a machine could think, and indeed only very special kinds of machines, namely brains and machines that have the same causal powers as brains." Searle concludes to say, that "whatever else intentionality is, it is a biological phenomenon, and it is as likely to be as causally dependent on the specific biochemistry of its origins as lactation, photosynthesis, or any other biological phenomena." To keep things simple, one can take this as to mean that for a computer to actually think, it has to have the same biological structure as the brain. So basically, only biological brains can think, nothing else.

We will come back to this later, because Harnad and Churchland think there is actually a level between the two extremes (computationalism - only biological brains), namely hybrid or noncomputational systems.

Note that (apart from computationalism), the CRA also refutes the Turing test as a good enough test for understanding. Because according to Searle, even if a program passes it, it's still mindlessly manipulating symbols, and thereby understands nothing. The Turing test is a pure behavioural test (it only looks at the natural language behaviour of the system), while Searle is also interested in what's inside the system (brains or symbol

manipulation). The Turing test only looks at function, Searle also at structure (to be precise: the internal structure of the brain/computer).

If computationalism is true, we are allowed to only look at function (because the physical implementation doesn't matter), but because Searle 'proved' computationalism is false, we must also look at structure.

Harnad:

In his paper 'What's Wrong and Right About Searle's Chinese Room Argument?'[3], Harnad summarizes the chinese room argument, agrees with it (thereby refuting computationalism), and counters a few comments on the CRA. After that (with only one page left), he goes on to say that the CRA is limited to only refuting pure computationalism, and that "there are still plenty of degrees of freedom in both hybrid and noncomputational approaches".

He states that "for although the CRA shows that cognition cannot be all just computational, it certainly does not show that it cannot be computational at all".

Harnad concludes that Searle's contribution (the CRA) has not only been negative (destructing computationalism), but that "his critique has helped open up the vistas that are now called 'embodied cognition' and 'situated robotics'". Also, Harnad states that, thanks to Searle, he is now exploring neural nets.

The Churchlands:

According to the Churchlands, the CRA is false4, and so it's not proven that mentality cannot be achieved by pure symbol manipulation. However, due to performance failures of classical AI and specific characteristics of brains, they think that "classical AI is unlikely to yield conscious machines, but that "systems that mimic the brain might".

By classical AI, we refer to a movement at the beginning of the AI-research program, which has the goal of "identifying the undoubtedly complex function that governs the human pattern of response to the environment, and then write the program by which the Symbol Manipulation machine (SM-machine) will compute it."[4]. Note that by this description, classical AI is strongly dependant on claims made by

computationalism/strong AI.

By systems which mimic the brain, they refer to Artificial Neural Networks (ANN's).

Note that we have just answered the first major question (is strong AI true?), and are now tumbling into the second (is strong AI possible, and how?).

As said, the reasons that SM-machines may not lead to conscious intelligence, while

ANN's might, are twofold:

- performance failures of classical AI

- specific characteristics of brains (and thereby of ANN's as well)

By performance failures, they refer to the fact that SM-machines are not very good at object recognition. When compared to brains, computers were slower, and required vast knowledge bases, which of course created their own set of problems. The Churchlands conclude that "the functional architecture of classical SM machines is simply the wrong architecture for the very demanding jobs required". For these 'very demanding jobs', ANN's might be more suitable.

4 The arguments for this are a bit too long too state here. However, the Churchlands conclude that "even though Searle’s Chinese room may appear to be 'semantically dark', he is in no position to insist, on the strength of this appearance, that rule-governed symbol manipulation can never constitute semantic phenomena"

In general, we can make a distinction between brains and computers. Lot's of things can be said about their differences, the most important being:

- speed vs. parallelism: computers are roughly a million times faster than brains (both in signal propagation as in clock frequency), but brains have roughly 10^11 neurons (which can be seen as simple CPU's)

- symbol manipulation vs. vector manipulation. A computer does pure symbol manipulation (zero's and one's), while neural networks can be seen as vectors-transformers: input vectors are transformed by neurons and weighted links, after which they are outputted. According to the Churchlands, symbol manipulation appears to be just one of many cognitive skills that a network may or may not learn to display, but is certainly not it's basic mode of operation.

Note that by defining neural networks (and thereby ANN's) this way, this shields ANN's from Searle's chinese room argument. This is because Searle's argument is directed against rule-governed SM-machines, not against vector transformers.

But let us continue. Above, we made some distinctions between brains and computers. While these are interesting in itself, they result in the fact that brains and computers are useful for radically different types of computational problems.

"Parallel processing is not ideal for all types of computation. On tasks that require only a small input vector, but many millions of swiftly iterated recursive

computations, the brain performs very badly, whereas classical SM machines

excel. This class of computations is very large and important, so classical

machines will always be useful, indeed, vital.

There is, however, an equally large class of computations for which the brain’s

architecture is the superior technology. These are the computations that typically confront living creatures: recognizing a predator’s outline in a noisy environment;

recalling instantly how to avoid its gaze, flee its approach or fend off its attack;

distinguishing food from nonfood; and so on"[4]

With this knowledge in mind, we can answer the question: 'is strong AI possible, and how?'. To the Churchlands, the answer is that "pure SM-machines are unlikely to yield conscious intelligence, but that systems that mimic the brain might". They conclude to say that "only research can decide how closely an artificial system must mimic the biological one, to be capable of intelligence."

Conclusion

We've seen statements of strong AI/computationalism, and three views commenting on that. All tried to answer 2 questions:

(1) is strong AI true?

(2) is strong AI possible, and how?

Computationalism of course agrees that strong AI is true, and states that running a program is enough for creating a strong Artifical Intelligence. Searle argued against this (by stating the CRA), and concluded that computationalism is false. To him, strong AI can only be achieved by a computer having the same causal relations as the brain. Harnad agrees with that, but points out the CRA is limited to refuting only pure computationalism, which leaves "still plenty of degrees of freedom in both hybrid and noncomputational approaches". The Churchlands argue the chinese room argument is false, and so it's not proven that mentality cannot be achieved by pure symbol manipulation. However, due to performance failures of classical AI and specific characteristics of brains, they think that "classical AI is unlikely to yield conscious machines, but that systems that mimic the brain might".

While computationalism and Searle both have clear statements on what is needed and sufficient for achieving a strong AI, Harnad and Churchland leave the question open for empirical research to decide.

Finally, because there's disagreement over what is needed for mentality, there's also disagreement over whether the Turing test is a good enough test for mentality. Computationalism clearly states yes; Searle clearly states no; Harnad and the Churchlands are somewhere in between (for reasons unstated in this paper).

This paper was about strong AI/computationalism, the chinese room argument, the Turing test, understanding, consciousness, the relation between syntax and semantics, etc. For each of these terrains, there are loads of problems, loads of papers, and no consensus. This paper has only given an overview on some of these.

More research, especially in the domains of cognitive science and artificial intelligence, will hopefully shed some light one these issues.

References

Note: the main papers of this paper (the one's which are summarized and discussed), are: [2], [3], and [4]

[1] Definitions of some key terms

http://www.ucd.ie/philosop/documents/2.%20definitions%20of%20some%20key%20terms.htm [2] Searle, John. R. (1980) Minds, brains, and programs. Behavioral and Brain Sciences 3 (3): 417-457

https://www.wendangku.net/doc/ff1182449.html,/Preprints/OldArchive/bbs.searle2.html

[3] Harnad, S. (2001) What's Wrong and Right About Searle's Chinese Room Argument? In: M. Bishop & J. Preston (eds.) Essays on Searle's Chinese Room Argument. Oxford University Press.

https://www.wendangku.net/doc/ff1182449.html,/1622/

[4] Churchland, P.M. & Churchland, P.S. (1990) Could a Machine Think? Scientific American 262.1: 32-37.

https://www.wendangku.net/doc/ff1182449.html,/jbwagma/churchland.pdf

[5] Searle, John, 1990. "Is the Brain a Digital Computer?" Proceedings and Addresses of the American Philosophical Association 64: 21-37

https://www.wendangku.net/doc/ff1182449.html,/oucl/research/areas/ieg/e-

library/sources/searle_comp.pdf

[6] Schank, R. C. & Abelson, R. P. (1977) Scripts, plans, goals, and understanding Hillsdale, N.J.: Lawrence Erlbaum Press. [RCS, JRS]

[7] Winograd, T. (1973) A procedural model of language understanding. In: Computer models of thought and language, ed. R. Schank & K. Colby. San Francisco: W. H. Freeman. [JRS]

[8] Weizenbaum, J. (1965) Eliza - a computer program for the study of natural language communication between man and machine. Communication of the Association

for Computing Machinery 9:36 45. [JRS]

[9] Stanford Encyclopedia of Philosophy - Chinese Room Argument

https://www.wendangku.net/doc/ff1182449.html,/entries/chinese-room/

[10] 'Levels of Functional Equivalence in Reverse Bioengineering: The Darwinian Turing Test for Artificial Life', Artificial Life, vol.1, pp.293-301 (reprinted in: https://www.wendangku.net/doc/ff1182449.html,ngton (ed.), Artificial Life: An Overview, (Cambridge, MA: MIT Press, 1995).

https://www.wendangku.net/doc/ff1182449.html,/~harnad/Papers/Harnad/harnad94.artlife2.html

[11] Searle - Is the Brain a Digital Computer?

https://www.wendangku.net/doc/ff1182449.html,/~harnad/Papers/Py104/https://www.wendangku.net/doc/ff1182449.html,p.html

[12] Turing, Alan (1950). "Computing Machinery and Intelligence." Mind, 59, 433-460.

[13] Wikipedia - Chinese Room Argument

https://www.wendangku.net/doc/ff1182449.html,/wiki/Chinese_room

小红书福利社运营模式分享

小红书福利社运营模式分享今年上海的TechCrunch峰会上，小红书创始人毛文超应邀到场，和B轮领投方GGV纪源资本管理合伙人童士豪接受了动点科技高级编辑牛千的访问，现场分享了自己的创业经验，解析了小红书是如何成功的将小红书福利社社区和电商相结合，发展成为新一代的社区电商。以下是毛文超现场演讲的主要内容摘要：小红书能成功运营小红书福利社的经验是什么？牛千：我知道跨境电商是一个挺重要的领域，国内有巨头在做，也有不少创业公司出现，包括资本也很追捧。小红书也在这个领域做出了自己的特色，你先介绍下小红书吧。毛文超：在座女生可能对我们小红书比较熟悉，我们口号叫“找到国外的好东西”。简单来说，就是帮助中国85后、90后对自己生活有品质要求的用户找到他们想要的东西。我们小红书是一个新时代的社区电商，关注的如何提升用户的生活品质。牛千：其实小红书在2014年初刚上线，我们当时有过一次交流，发现小红书福利社社区用户活跃度还是蛮高的，小红书里面这些用户是些什么样的人？他们在社区里面可以怎么玩？或者小红书福利社社区里面的乐趣是什么？

毛文超：这是我经常被问到一个问题，到底社区该怎么玩，或者到底一个社区是怎么做起来的。现在为止，我们小红书福利社社区的用户分成几种人，有的是分享他们到国外买好东西的经验，有的去国外旅游之前会来逛逛然后做心愿单，有的是在国内也会分享海淘经验。当然，更多的人是在社区里面互动，包括点赞、评论，以及分享他们喜欢的内容到朋友圈。我觉得每个社区最后能够走到多远，或者它能创造什么价值，最重要的是两件事：第一个是它的种子用户是谁，这个奠定了社区的基因；第二件事是核心规则，作为一个社区管理者，我们小红书福利社鼓励什么样的行为，不鼓励什么样的行为，这个决定了社区能走多远。我们小红书福利社最早一批的核心用户，在我看来，是85后、90后这样一代对生活品质有要求的意见领袖。因为我们最早提供的是一个海外购物攻略。相信在座很多人都会出国旅游，而购物肯定是必不可少的环节，这个是整个中国的大环境。事实上，你们都是这方面的意见领袖。所以我们小红书很快积累了这样一批用户，并开始发酵。我们在玩法上比较鼓励去中心化的方式，每个人都能发挥自己的意见，这个事情对小红书福利社活跃度贡献很大，每个人都有机会把好内容带给大家，而不是说我是台下用户，等着台上几个人给我灌输观点。小红书这种纯UGC的形式非常符合口碑营销时代的需求，也是小红书福利社社区能一直保持高活跃度，高信任度，最终也代表了高转化率的重要原因。如何从社区升级为社区型电商？牛千：我上小红书就想买一个什么东西，我就想直接看到这个东西多少钱，在哪里可以买到，像我这样的用户，我不太喜欢在小红书福利社社区里面去逛，你们有没有这方面的计划，把社区用户分享的很好的内容把它更好的结构化，把它带给更多的普通用户？毛文超：女性用户分为两类，特别爱购物的，和比较爱购物的。对于后者，用户特点是既有发现的需求，却没有太多的时间闲逛，需要快速找到自己喜欢的东西，这其实占用户的主流。对于这类用户，我们就需要尽可能的去懂她，知道她是谁，可能会喜欢什么，也就是“个性化”。我们小红书从去年8月份开始，已经开始默默的通过标签结构化社区里的所有UGC内容，我们小红书福利社社区里面现在有40多万的标签，这是我们为沉淀下来的几百万条真实用户分享的海外商品口碑贴上的索引。同时我们也记录和储存了用户所有的使用行为，比如喜欢一个笔记，加入一个心愿单，点击了哪条笔记，关注了哪个用户和哪类标签等等。小红书接下来的战略重点就是通过这些数据让用户发现的效率更高。小红书的盈利模式是什么？

《英雄传说：零之轨迹》图文流程攻略(第四章~终章)

《英雄传说：零之轨迹》图文流程攻略（第四章~终章） ¤ 带着琪雅在市内逛一圈吧有很多有趣的情节东街出口处民居的婆婆会赠送“终极混合果汁” 西街杂货店有卖“黑市医生格伦第九卷和第十一卷” 另外杂货店都有卖“克洛斯贝尔时代周刊6”

前往市政厅市长办公室后会遇到议长前来惹事下楼被接待小姐叫住帮市长去买慰问品来到中央果汁店的位置发生剧情在车站前找到果汁店得到“特制苦西红柿奶昔”建议大家自己喝一口补充一下CP 把慰问品带回去得到奖励“还魂粉”

和警察局前台小姐对话时得知这个情报前往地下空间A第三终端的位置，入口右边的电梯可以直达魔兽只有一只两招钻石星辰就搞定了，它的绝招可以抽取我方的CP 另外要注意琪雅也会被攻击到逛完之后前往游击士协会寻求帮助城外开放去大圣堂，在教堂右边的主日学校找玛布尔修女会城后遇到隆他们去上课

孩子们上课后和玛布尔修女对话开始上课依次选择正确的答案 1.军队，2搜查一科，3记录调查情况，4第5代，5维持治安与遵守当地法律 6.从警察学校毕业，7白狼的帮助，8地下空间A区域，9是否有对政府权力的干涉力10.保护大家（这道题好像没有正确答案）完美的上完课得到奖励“神圣挂坠” ¤ 下面去乌拉尔医院在正门前遇到姐姐餐厅里聊一会去联系钓鱼医生上到天台进入科研楼

琪雅离开，前往河边找到琪雅和小滴众人回家鲁巴彻的死胖子已经快要气死了··········

¤ 早上有罗伊德做饭琪雅帮忙结果琪雅不知道怎么就做出了完美的食物当然做什么是你来选，不知道选择的不同会带来什么结果我还是喜欢熏肉鸡蛋乳酪面

员工福利

《员工福利管理》一、辨析题练习题 1．二战以来，福利国家危机的出现以及国家在社会保障方面支出的减少是员工福利飞速发展的原因。 2．按照员工享受福利的条件有无和种类，员工福利计划模式可以划分为统一型和差异型两种模式。 3．在我国，法定福利包括社会保险、法定假期和企业补充保险。 4．在企业年金的投资中，收益性原则是最重要的投资原则。 5．在我国，住房公积金由企业单方缴费，员工个人不需要缴费。 6．从企业的角度考虑，企业的薪酬策略也会影响员工福利设置，企业的薪酬策略就是企业的薪酬水平策略。简答题参考要点： 1. 二战以来，福利国家危机的出现以及国家在社会保障方面支出的减少是员工福利飞速发展的原因。解答: 错。二战以来，员工福利的高速发展是多种因素共同作用的结果： 1．社会政治经济环境为员工福利提供了发展的空间； 2．雇主对员工福利作用的深刻认识，引导企业从被动接受福利要求到主动提供福利计划； 3．政府介入员工福利领域，通过立法规范、政策支持、税收优惠等手段，促进了员工福利； 4．雇员对福利的认可接受程度也一定程度利于福利发展； 5．工会对员工福利的推动作用； 6．信息技术及管理技术的进步促进了员工福利的发展。 2. 按照员工享受福利的条件有无和种类，员工福利计划模式可以划分为统一型和差异型两种模式。解答: 错。按照员工享受福利的条件有无和种类，员工福利计划模式可以划分为免费型、绩效型和资历型三种。（一）免费型模式。免费型模式是指不论员工的工作绩效如何或员工现在是否在岗（或者退休），只要是企业的正式员工，都可以无条件获得企业设置的福利计划。（二）绩效型模式。绩效型模式是指员工要想获得一定的福利水平或者福利项目，必须达到一定的工作绩效标准。绩效型模式将福利的有无、福利的晋升与员工的工作绩效评价结果有机的结合起来，大大地增加了福利的激励作用。（三）资历型模式。资历型模式是以员工在企业里的工作时间为福利享受或晋升标准。主要适用于那些强调终身雇佣的企业，也适用于企业想挽留的核心员工。按照企业给员工提供的福利项目对象的一致性不同，员工福利计划可以划分为统一型和差异型两种模式。（一）统一型模式。统一型模式也叫全员型模式，指企业向员工提供的福利在内容上是一致的，即所有的员工享受到的福利项目都是一样的。（二）差异型模式。差异型模式是指企业向员工提供不同内容的福利，以满足员工不同的需求。在实践中，差异型模式的实施可以有不同的方式，弹性福利计划就是其中最有代表性的一种。 3. 在我国，法定福利包括社会保险、法定假期和企业补充保险。

《零之轨迹》最全攻略

第一章神狼一族的午后本章全DP50点料理4个——【多汁牛排】，【新鲜果汁】，【田园蛋包饭】，【特制炖牛肉】。书籍4个——【克洛斯贝尔时代周刊②】，【暗医生古连 1卷】-矿山镇购买，【暗医生古连 2卷】-可以购买也可以在医科大学获得，【暗医生古连 3卷】-第二章也可以购买。家具3个——【高级花瓶】-艾莉-时代百货商店，【飞镖盘】-兰迪-赌场，【导力车模型】-罗伊德-村子的杂货店，获得的家具可以回到支援科二楼和三楼进各个角色的房间放置，暂时还不知道除了观赏还有什么用。但是每个人房间的床都可以用来休息，全员完全回复呢，所以在市内的话不需要花钱住旅馆了。钓鱼——本章在阿尔摩里卡村获得钓竿之后就能钓鱼了，东大街的钓公师团开启【段位认定测试】功能，钓到不同种类的鱼去认定一下，可以获得奖励和提高钓鱼称号。而且东大街的集市上可以用鱼换耀晶片，所以钓鱼还是比较有用滴，别直接卖商店，还不如换了耀晶片再去卖。本章可以钓到9种鱼，参见下面小猫喂食中得鱼种。只能用蚯蚓和熬炼丸子，在港湾区、阿尔摩里卡村和医科大学的钓鱼点就能钓全。小猫——这一章之后，要得到钓鱼手册之后，用钓的鱼喂养小猫了。每章只能喂一次，用不同的鱼喂会获得不同的回路奖励哦，如下：阿尔摩里卡鲫鱼-回避1，橙河鱼-攻击1，岩穴鱼-防御1，天青鱼-EP1，灌木雪蟹-精神1，鲤鱼-魔防1，暴食鲈鱼-移动1，珍珠草-命中1，鳗鲡-行动力1。赌场——玩扑克比较好赚筹码，不是21点哦。押5个筹码就可以开始，每次都要点一下RAISE提高赌注，而且可以跨过第2、4轮最后到第7轮，赢得第7轮的话可以得到1000个筹码。这个扑克的规则就类似梭哈，但是只比牌型，不比牌的大小了，比如和对方都是一对，就平局了，不看是对几。有实际多玩几轮，换一下家具和装备，后面隐藏任务还需要200筹码换人偶。回路-【耀脉】——搜查官升到11级时获得的奖励，装备给罗伊德，在耀石奖励时放S技能，打那种7中晶石都暴的怪物，怪物能爆不求多，4~5就不错，第一张结束时的矿山中。放一次能爆各种晶石80+，第一张BOSS第一场，我爆了各类晶石170。共1190 个晶石，相当于25000米拉。——感谢512195574提供练级点——第一章玛因茨矿山隧道左下角可以去打怪23级，打时估计等级在17左右，攒完s后去打一次可以得300多经验（后面经验会减少但也够多了）可以直接在那里刷到22级左右，那里的可以捡的装备也不错，建议打困难或以上难度的在那里刷级= = 稀有魔兽图鉴——往三岔路往阿尔摩里卡村的路走第二张图(也就是私人场地的那张图下一张)的第一只葵花怪(也就是死亡葵花冠拉,我喜欢这么叫它)可以有几率遇到罕见的熊猫怪(疾风竹叶猫,尤其是第一章几乎2碰次1遇只)。其实,如果走强化线路的朋友最好在第一章时候在这里多刷刷 U物 (第一章出现的爆U物的怪几率很高) 新开的场景： 1、娱乐街的【赌场】开放啦，在里面可以玩21点、扑克之类的小游戏，筹码可以换东西，家具-【飞镖盘】和装备。 2、港湾区再往上走，【银行】开放了，而且有支线任务，推荐未出城前先完成，留好耀晶片各30个哦。完成支线任务即可在此用耀晶片兑换米拉，比商店兑换价格高一点点。 3、旧街区的【奈因瓦利】开放了，里面是物物交换，种类不多但都是钱买不到的。与老板多对话几次，可以获得不少情报哦。 4、旧街区的【乔姆工坊】开放了，需要U物质等打魔兽掉落的物品和低级的武器可以升级武器，先去刷怪吧~ 5、导力商店的前台开放【更换外壳】的业务，可以给导力器换漂亮的外壳，1000米拉一次，导力商店也出了新的回路。吉隆多武器商会也有了新的武器。 5、东大街的钓公师团，在可以钓鱼之后开放【段位认定】，可以提高钓鱼称号。 6、东大街的市集最右侧【鱼贩】马鲁狄，在可以钓鱼后开放【兑换耀晶片】，钓到的鱼后来兑换吧，每种鱼留一只喂小猫哦，也能得到不错的东西呢。第一章刚开始在克洛斯贝尔市可以获得： 1、西大街的铃铛之家公寓，最里面房间和婶婶对话可以获得料理-【多汁牛排】。 2、行政区的市政府门口广场上，果汁摊可以获得料理-【新鲜果汁】。 3、时代百货商店一楼杂货店可以购买书籍-【克洛斯贝尔时代周刊②】。 4、时代百货商店一楼杂货店可以购买家具-【高级花瓶】-艾莉。 5、赌场可以用筹码兑换家具-【飞镖盘】-兰迪。 6、警察局总部一楼接待员处对话，可以向她【展示战斗手册】，获得【U物质】、500米拉。以后每章开始时和中间都可以去一次。在第四章最后打最终BOSS之前展示战斗手册，可以获得【梦幻布料】。二周目以后收集到全魔兽图鉴再来展示战斗手册，可以获得10500米拉+【U物】+【梦幻布料】。 7、东大街最右侧民宅，地藏菩萨旁边的房屋，可以从老人处得到【柠檬汁】，在去往阿尔摩里卡村路上休息亭的时候剧情用到。 8、在西大街格瑞姆伍德律师事务内可以和律师对话获得不少关于狼型魔兽的情报。在主线任务进行到阿尔摩里卡村时可以获得： 9、村子右上角小房子，村长家右侧内获得【宝箱】-【省EP1】。 10、通往村口的桥上与钓鱼人对话，获得【钓鱼手册】等，可以开始钓鱼了。 11、在村内酒馆白腊亭内的柜台右侧墙上，获得料理-【田园蛋包饭】。 12、与所有人对话后准备离开村子之前，杂货店内与老人对话获得书籍-【暗医生古连 2卷】。 13、村内杂货店里卖家具-【导力车模型】-罗伊德。在主线任务进行到圣乌尔丝拉医科大学时可以获得： 14、在医院的旅馆与前台对话可获得料理-【特制炖牛肉】。在主线任务进行到矿山镇玛因茨时可以获得： 15、矿山镇的佩卡莱商店里有出售书籍-【暗医生古连 1卷和2卷】，序章没有获得1卷、阿尔摩里卡村没有获得2卷的朋友赶紧买哦，后面没机会了。 16、在装备等候至深夜之前，去矿山镇最下方的民宅找躺在床上的麦克斯，可以获得书籍-【暗医生古连 3卷】。也可以在第二章时在西大街的塔利兹商店购买。 17、在最后的战斗之前，即对桌子选择等待至深夜之前，可以去矿山镇左上角的【玛因茨矿山】，触发剧情，可以进入。里面有两层，第二层有四个【宝箱】分别获得【中回复药】、【复苏药】、回路-【HP2】、衣服【武士服】。主线任务一：魔兽危害的调查－阿尔摩里卡村部分

空之轨迹FC最详图文攻略

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.wendangku.net/doc/ff1182449.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。

《英雄传说：零之轨迹》图文流程攻略(序章~第一章)

《英雄传说：零之轨迹》图文流程攻略（序章~第一章）开场走到迷宫深处自动触发剧情醒来回到现实

¤ 下车去警察局报道和接待处的MM对话接到“特别任务支援科”的第一个任务得到导力器和4个结晶回路第一个洞已经打开，主角的第一个洞没有属性限制但是其他人有所以还是只能装土属性

进入地下空间图示的装置可以进行回复地图说明：蓝点宝箱位置，绿点回复，红点强制战斗地下空间A-1 宝箱：1回复药，2烟毋弹X5，3地·水·火·风耀晶片X20，4复苏药

地下空间A-2 宝箱：5地·水·火·风·时·空·幻耀晶片X20，6回复药，7打火机，8战斗探测器X5 ¤ 第一层进入深处可以看到通往地面的梯子调查一下坐升降梯到下层

在充满管道的房间听到孩子的声音从管道口进入找到孩子，注意管道通往上层可以得到宝箱找到孩子后得知还有一个孩子选择第二项继续深入看到回复点后保存一下进入房间发生战斗先选择第二项吸引魔物的注意，这样另一个小孩就不会被大量魔物围攻了保护的对象死亡的话游戏会失败

战斗中不让孩子受到伤害可以增加报酬注意尽量做到达成方法是首先之前的选择不要选错然后一上来就使用S技干掉靠近孩子的魔物救出了小孩却不小心陷入了另一个危机还要被游击士所救，这也使得警察的风评再度下降了... 回到局里先是被副所长狂喷一顿又被搜查二科的同事刺激 ¤ 接到通信前往宿舍了解到成立“特支科”的初衷和目前的窘境去和同事们聊聊吧蓝迪在202室，艾莉在301室，302室是绨欧的房间但是敲门没人应，之后可以进入里面没人来到屋顶有一段小剧情

空之轨迹FC超完美详细图文攻略

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

那些年我们享受过的美国福利

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福利政策第一章社会福利公司根据现行的法律和当地政府的规定，当员工入职并清晰地填写好相应的社保所需信息表后，公司将为员工提供一系列的社会保险，包括养老保险、失业保险、生育保险、工伤保险、医疗保险和住房公积金。所缴纳的保险种类、比例和缴费的基数将按政府有关规定之条款和政策予以调整。社会保险依照个人和公司共同承担的原则，按比例缴纳。【养老保险】公司为员工提供养老保险以感谢员工现在对公司的贡献和保障员工晚年生活稳定安乐。养老保险的缴纳比例和基数按照政府有关规定之条款和政策予以调整。该费用由公司和个人按比例各自承担。【失业保险】公司根据劳动法律法规的规定，按月向劳动部门缴纳失业保险金。失业保险的缴纳比例和基数按照政府有关规定之条款和政策予以调整。该费用由公司和个人按比例各自承担。【生育保险】公司根据劳动法律法规的规定缴纳生育保险。该费用由公司承担，符合保险条例规定的女性员工均可享受。【工伤保险】公司根据劳动法律法规的规定缴纳工伤保险。该费用由公司承担。【医疗保险】公司根据劳动法律法规的规定缴纳医疗保险。该费用由公司和个人按比例各自承担。【住房公积金】公司根据政府的规定，公司与个人各按照相应百分比计算缴纳金额。进入我公司之前就已经参保五险一金的员工，必须确认的自己的五险一金账户是完全可以转移至我公司相应的社保户内的，否则，未能成功传入我公司社保户内并正常缴费的，责任由该员工自负，公司将不予补交。

第二章公司福利休假管理第一节休假日第一条国家法定假日、双休日（星期六、星期日）、其他经公司决定的休假日为公司休假日。第二节带薪年假年假享受对象：公司员工司龄满一年后，可以享受带薪年假。第一条员工在年假期间享受与正常工作期间相同的薪资待遇。第二条年假休假天数：（司龄计算包含集团系统内的任一公司司龄）第三条年假休假天数计算：新入职员工司龄满1年后，当年的休假天数按比例进行折算，其中入职日期于15日（含15日）之前，按这个月计算，入职日期于15日之后，本月不计算；员工的休假天数取整数天，按照四舍五入的方法来计算。例1：某员工2014年3月13日入职，请问从什么时候可以开始休年假，可以休几天年假？答：从2015年3月13日之后，可以享受带薪年假； 2014年出勤月数为10个月，可享受的天数为5天*10/12=4.17天=4天例2：某员工2014年1月18日入职，请问从什么时候可以开始休年假，可以休几天的年假？答：从2015年1月18日之后，可以享受带薪年假； 2014年的出勤月数为11个月，可享受的天数为5天*11/12=4.58天=5天例3：某员工2009年3月18日入职，请问可以休几天的年假？答：可享受的天数为5+1天*9/12=5.75=6天

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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

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公司福利制度导读：本文是关于公司福利制度的文章，如果觉得很不错，欢迎点评和分享！【篇一：公司福利管理制度】第一章总则第一条目的为完善公司的福利体系，增强员工对公司的忠诚度，提升企业凝聚力，表达公司对员工的一种关心和爱护，满足员工在不同方面的需求，营造良好的工作和生活氛围，提高员工的归属感，使员工能够愉快地工作和生活，保障员工利益，减少员工的后顾之忧，特制定本管理制度。第二条原则本管理制度执行岗位匹配、绩效挂钩与灵活变更原则。第二章福利体系构成第三条福利类别公司福利分为经济性福利和非经济性福利两部分。第四条公司为员工提供以下经济性福利待遇一、社会保险福利基本养老保险、基本医疗保险、失业保险、生育保险、工伤保险、大病补充医疗保险、职工医疗互助、安康险及企业年金等。二、住房福利

公司每月按一定比例为职工缴纳住房公积金。三、带薪休假福利根据国家有关政策及公司相关规定，公司职工可享受以下带薪休假福利：国家法定节假日、婚丧假、产假与计划生育假、探亲假、工伤假、工龄休假等。四、其他经济性福利公司在工作期间为员工发放节日慰问费、冬季取暖费、保健津贴、交通补贴、医疗卫生费、回族补贴、独子费、妇女卫生费和节日慰问物品，并提供生日福利、体检福利、劳动安全卫生保护福利等。第五条公司为员工提供以下非经济性福利 1、为员工提供教育培训的机会； 2、为员工提供良好的办公场所； 3、创造良好的企业文化氛围； 4、授予员工各种荣誉称号等。第三章福利待遇及发放形式第六条社会保险及企业年金福利为进一步提高公司职工的基本福利和保障水平，建立多层次的养老保险体系，逐步提高职工退休后的养老保障待遇。根据劳动法和国家劳动保障部门、云锡集团的有关规定，结合公司实际，公司每月为员工按一定的基数和比例交纳养老、医疗、失业、工伤生育等“五大”社会保险费用及企业年金，使员工老有所养，病有所医，失业有所保，解除员工的后顾之忧。各项保险的缴费基数每年调整一次，缴费比例

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