Mark,
Actually I am sympathetic with this idea. I do think good can be
defined. And, I think it can be a simple definition. However, it
doesn't seem right to me to preprogram an AGI with a set ethical
theory; the theory could be wrong, no matter how good it sounds. So,
better to put such ideas in only as probabilistic correlations (or
"virtual evidence"), and let the system change its beliefs based on
accumulated evidence. I do not think this is overly risky, because
whatever the system comes to believe, its high-level goal will tend to
create normalizing subgoals that will regularize its behavior.
I'll stick to my point about defining "make humans happy" being hard,
though. Especially with the restriction "without modifying them" that
you used.
On Thu, Aug 28, 2008 at 12:38 PM, Mark Waser <[EMAIL PROTECTED]> wrote:
>> Also, I should mention that the whole construction becomes irrelevant
>> if we can logically describe the goal ahead of time. With the "make
>> humans happy" example, something like my construction would be useful
>> if we need to AI to *learn* what a human is and what happy is. (We
>> then set up the pleasure in a way that would help the AI attach
>> "goodness" to the right things.) If we are able to write out the
>> definitions ahead of time, we can directly specify what goodness is
>> instead. But, I think it is unrealistic to take that approach, since
>> the definitions would be large and difficult....
>
> :-) I strongly disagree with you. Why do you believe that having a new AI
> learn large and difficult definitions is going to be easier and safer than
> specifying them (assuming that the specifications can be grounded in the
> AI's terms)?
>
> I also disagree that the definitions are going to be as large as people
> believe them to be . . . .
>
> Let's take the Mandelbroit set as an example. It is perfectly specified by
> one *very* small formula. Yet, if you don't know that formula, you could
> spend many lifetimes characterizing it (particularly if you're trying to
> doing it from multiple blurred and shifted images :-).
>
> The true problem is that humans can't (yet) agree on what goodness is -- and
> then they get lost arguing over detailed cases instead of focusing on the
> core.
>
> The core definition of goodness/morality and developing a system to
> determine what actions are good and what actions are not is a project that
> I've been working on for quite some time and I *think* I'm making rather
> good headway.
>
>
> ----- Original Message ----- From: "Abram Demski" <[EMAIL PROTECTED]>
> To: <agi@v2.listbox.com>
> Sent: Thursday, August 28, 2008 9:57 AM
> Subject: **SPAM** Re: AGI goals (was Re: Information theoretic approaches to
> AGI (was Re: [agi] The Necessity of Embodiment))
>
>
>> Hi mark,
>>
>> I think the miscommunication is relatively simple...
>>
>> On Wed, Aug 27, 2008 at 10:14 PM, Mark Waser <[EMAIL PROTECTED]> wrote:
>>>
>>> Hi,
>>>
>>> I think that I'm missing some of your points . . . .
>>>
>>>> Whatever good is, it cannot be something directly
>>>> observable, or the AI will just wirehead itself (assuming it gets
>>>> intelligent enough to do so, of course).
>>>
>>> I don't understand this unless you mean by "directly observable" that the
>>> definition is observable and changeable. If I define good as making all
>>> humans happy without modifying them, how would the AI wirehead itself?
>>> What
>>> am I missing here?
>>
>> When I say "directly observable", I mean observable-by-sensation.
>> "Making all humans happy" could not be directly observed unless the AI
>> had sensors in the pleasure centers of all humans (in which case it
>> would want to wirehead us). "Without modifying them" couldn't be
>> directly observed even then. So, realistically, such a goal needs to
>> be inferred from sensory data.
>>
>> Also, I should mention that the whole construction becomes irrelevant
>> if we can logically describe the goal ahead of time. With the "make
>> humans happy" example, something like my construction would be useful
>> if we need to AI to *learn* what a human is and what happy is. (We
>> then set up the pleasure in a way that would help the AI attach
>> "goodness" to the right things.) If we are able to write out the
>> definitions ahead of time, we can directly specify what goodness is
>> instead. But, I think it is unrealistic to take that approach, since
>> the definitions would be large and difficult....
>>
>>>
>>>> So, the AI needs to have a concept of external goodness, with a weak
>>>> probabilistic correlation to its directly observable pleasure.
>>>
>>> I agree with the concept of external goodness but why does the
>>> correlation
>>> between external goodness and it's pleasure have to be low? Why can't
>>> external goodness directly cause pleasure? Clearly, it shouldn't believe
>>> that it's pleasure causes external goodness (that would be reversing
>>> cause
>>> and effect and an obvious logic error).
>>
>> The correlation needs to be fairly low to allow the concept of good to
>> eventually split off of the concept of pleasure in the AI mind. The
>> external goodness can't directly cause pleasure because it isn't
>> directly detectable. Detection of goodness *through* inference *could*
>> be taken to cause pleasure; but this wouldn't be much use, because the
>> AI is already supposed to be maximizing goodness, not pleasure.
>> Pleasure merely plays the role of offering "hints" about what things
>> in the world might be good.
>>
>> Actually, I think the proper probabilistic construction might be a bit
>> different than simply a "weak correlation"... for one thing, the
>> probability that goodness causes pleasure shouldn't be set ahead of
>> time. I'm thinking that likelihood would be more appropriate than
>> probability... so that it is as if the AI is born with some evidence
>> for the correlation that it cannot remember, but uses in reasoning (if
>> you are familiar with the idea of "virtual evidence" that is what I am
>> talking about).
>>
>>>
>>> Mark
>>>
>>> P.S. I notice that several others answered your wirehead query so I
>>> won't
>>> belabor the point. :-)
>>>
>>>
>>> ----- Original Message ----- From: "Abram Demski" <[EMAIL PROTECTED]>
>>> To: <agi@v2.listbox.com>
>>> Sent: Wednesday, August 27, 2008 3:43 PM
>>> Subject: **SPAM** Re: AGI goals (was Re: Information theoretic approaches
>>> to
>>> AGI (was Re: [agi] The Necessity of Embodiment))
>>>
>>>
>>>> Mark,
>>>>
>>>> The main motivation behind my setup was to avoid the wirehead
>>>> scenario. That is why I make the explicit goodness/pleasure
>>>> distinction. Whatever good is, it cannot be something directly
>>>> observable, or the AI will just wirehead itself (assuming it gets
>>>> intelligent enough to do so, of course). But, goodness cannot be
>>>> completely unobservable, or the AI will have no idea what it should
>>>> do.
>>>>
>>>> So, the AI needs to have a concept of external goodness, with a weak
>>>> probabilistic correlation to its directly observable pleasure. That
>>>> way, the system will go after pleasant things, but won't be able to
>>>> fool itself with things that are maximally pleasant. For example, if
>>>> it were to consider rewiring its visual circuits to see only
>>>> skin-color, it would not like the idea, because it would know that
>>>> such a move would make it less able to maximize goodness in general.
>>>> (It would know that seeing only tan does not mean that the entire
>>>> world is made of pure goodness.) An AI that was trying to maximize
>>>> pleasure would see nothing wrong with self-stimulation of this sort.
>>>>
>>>> So, I think that pushing the problem of goal-setting back to
>>>> pleasure-setting is very useful for avoiding certain types of
>>>> undesirable behavior.
>>>>
>>>> By the way, where does this term "wireheading" come from? I assume
>>>> from context that it simply means self-stimulation.
>>>>
>>>> -Abram Demski
>>>>
>>>> On Wed, Aug 27, 2008 at 2:58 PM, Mark Waser <[EMAIL PROTECTED]>
>>>> wrote:
>>>>>
>>>>> Hi,
>>>>>
>>>>> A number of problems unfortunately . . . .
>>>>>
>>>>>> -Learning is pleasurable.
>>>>>
>>>>> . . . . for humans. We can choose whether to make it so for machines
>>>>> or
>>>>> not. Doing so would be equivalent to setting a goal of learning.
>>>>>
>>>>>> -Other things may be pleasurable depending on what we initially want
>>>>>> the AI to enjoy doing.
>>>>>
>>>>> See . . . all you've done here is pushed goal-setting to
>>>>> pleasure-setting
>>>>> . . . .
>>>>>
>>>>> = = = = =
>>>>>
>>>>> Further, if you judge goodness by pleasure, you'll probably create an
>>>>> AGI
>>>>> whose shortest path-to-goal is to wirehead the universe (which I
>>>>> consider
>>>>> to
>>>>> be a seriously suboptimal situation - YMMV).
>>>>>
>>>>>
>>>>>
>>>>>
>>>>> ----- Original Message ----- From: "Abram Demski"
>>>>> <[EMAIL PROTECTED]>
>>>>> To: <agi@v2.listbox.com>
>>>>> Sent: Wednesday, August 27, 2008 2:25 PM
>>>>> Subject: **SPAM** Re: AGI goals (was Re: Information theoretic
>>>>> approaches
>>>>> to
>>>>> AGI (was Re: [agi] The Necessity of Embodiment))
>>>>>
>>>>>
>>>>>> Mark,
>>>>>>
>>>>>> OK, I take up the challenge. Here is a different set of goal-axioms:
>>>>>>
>>>>>> -"Good" is a property of some entities.
>>>>>> -Maximize good in the world.
>>>>>> -A more-good entity is usually more likely to cause goodness than a
>>>>>> less-good entity.
>>>>>> -A more-good entity is often more likely to cause pleasure than a
>>>>>> less-good entity.
>>>>>> -"Self" is the entity that causes my actions.
>>>>>> -An entity with properties similar to "self" is more likely to be
>>>>>> good.
>>>>>>
>>>>>> Pleasure, unlike goodness, is directly observable. It comes from many
>>>>>> sources. For example:
>>>>>> -Learning is pleasurable.
>>>>>> -A full battery is pleasurable (if relevant).
>>>>>> -Perhaps the color of human skin is pleasurable in and of itself.
>>>>>> (More specifically, all skin colors of any existing race.)
>>>>>> -Perhaps also the sound of a human voice is pleasurable.
>>>>>> -Other things may be pleasurable depending on what we initially want
>>>>>> the AI to enjoy doing.
>>>>>>
>>>>>> So, the definition if "good" is highly probabilistic, and the system's
>>>>>> inferences about goodness will depend on its experiences; but pleasure
>>>>>> can be directly observed, and the pleasure-mechanisms remain fixed.
>>>>>>
>>>>>> On Wed, Aug 27, 2008 at 12:32 PM, Mark Waser <[EMAIL PROTECTED]>
>>>>>> wrote:
>>>>>>>>
>>>>>>>> But, how does your description not correspond to giving the AGI the
>>>>>>>> goals of being helpful and not harmful? In other words, what more
>>>>>>>> does
>>>>>>>> it do than simply try for these? Does it pick goals randomly such
>>>>>>>> that
>>>>>>>> they conflict only minimally with these?
>>>>>>>
>>>>>>> Actually, my description gave the AGI four goals: be helpful, don't
>>>>>>> be
>>>>>>> harmful, learn, and keep moving.
>>>>>>>
>>>>>>> Learn, all by itself, is going to generate an infinite number of
>>>>>>> subgoals.
>>>>>>> Learning subgoals will be picked based upon what is most likely to
>>>>>>> learn
>>>>>>> the
>>>>>>> most while not being harmful.
>>>>>>>
>>>>>>> (and, by the way, be helpful and learn should both generate a
>>>>>>> self-protection sub-goal in short order with procreation following
>>>>>>> immediately behind)
>>>>>>>
>>>>>>> Arguably, be helpful would generate all three of the other goals but
>>>>>>> learning and not being harmful without being helpful is a *much*
>>>>>>> better
>>>>>>> goal-set for a novice AI to prevent "accidents" when the AI thinks it
>>>>>>> is
>>>>>>> being helpful. In fact, I've been tempted at times to entirely drop
>>>>>>> the
>>>>>>> be
>>>>>>> helpful since the other two will eventually generate it with a
>>>>>>> lessened
>>>>>>> probability of trying-to-be-helpful accidents.
>>>>>>>
>>>>>>> Don't be harmful by itself will just turn the AI off.
>>>>>>>
>>>>>>> The trick is that there needs to be a balance between goals. Any
>>>>>>> single
>>>>>>> goal intelligence is likely to be lethal even if that goal is to help
>>>>>>> humanity.
>>>>>>>
>>>>>>> Learn, do no harm, help. Can anyone come up with a better set of
>>>>>>> goals?
>>>>>>> (and, once again, note that learn does *not* override the other two
>>>>>>> --
>>>>>>> there
>>>>>>> is meant to be a balance between the three).
>>>>>>>
>>>>>>> ----- Original Message ----- From: "Abram Demski"
>>>>>>> <[EMAIL PROTECTED]>
>>>>>>> To: <agi@v2.listbox.com>
>>>>>>> Sent: Wednesday, August 27, 2008 11:52 AM
>>>>>>> Subject: **SPAM** Re: AGI goals (was Re: Information theoretic
>>>>>>> approaches
>>>>>>> to
>>>>>>> AGI (was Re: [agi] The Necessity of Embodiment))
>>>>>>>
>>>>>>>
>>>>>>>> Mark,
>>>>>>>>
>>>>>>>> I agree that we are mired 5 steps before that; after all, AGI is not
>>>>>>>> "solved" yet, and it is awfully hard to design prefab concepts in a
>>>>>>>> knowledge representation we know nothing about!
>>>>>>>>
>>>>>>>> But, how does your description not correspond to giving the AGI the
>>>>>>>> goals of being helpful and not harmful? In other words, what more
>>>>>>>> does
>>>>>>>> it do than simply try for these? Does it pick goals randomly such
>>>>>>>> that
>>>>>>>> they conflict only minimally with these?
>>>>>>>>
>>>>>>>> --Abram
>>>>>>>>
>>>>>>>> On Wed, Aug 27, 2008 at 11:09 AM, Mark Waser <[EMAIL PROTECTED]>
>>>>>>>> wrote:
>>>>>>>>>>>
>>>>>>>>>>> It is up to humans to define the goals of an AGI, so that it will
>>>>>>>>>>> do
>>>>>>>>>>> what
>>>>>>>>>>> we want it to do.
>>>>>>>>>
>>>>>>>>> Why must we define the goals of an AGI? What would be wrong with
>>>>>>>>> setting
>>>>>>>>> it
>>>>>>>>> off with strong incentives to be helpful, even stronger incentives
>>>>>>>>> to
>>>>>>>>> not
>>>>>>>>> be
>>>>>>>>> harmful, and let it chart it's own course based upon the vagaries
>>>>>>>>> of
>>>>>>>>> the
>>>>>>>>> world? Let it's only hard-coded goal be to keep it's satisfaction
>>>>>>>>> above
>>>>>>>>> a
>>>>>>>>> certain level with helpful actions increasing satisfaction, harmful
>>>>>>>>> actions
>>>>>>>>> heavily decreasing satisfaction; learning increasing satisfaction,
>>>>>>>>> and
>>>>>>>>> satisfaction naturally decaying over time so as to promote action .
>>>>>>>>> .
>>>>>>>>> .
>>>>>>>>> .
>>>>>>>>>
>>>>>>>>> Seems to me that humans are pretty much coded that way (with
>>>>>>>>> evolution's
>>>>>>>>> additional incentives of self-defense and procreation). The real
>>>>>>>>> trick
>>>>>>>>> of
>>>>>>>>> the matter is defining helpful and harmful clearly but everyone is
>>>>>>>>> still
>>>>>>>>> mired five steps before that.
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> ----- Original Message -----
>>>>>>>>> From: Matt Mahoney
>>>>>>>>> To: agi@v2.listbox.com
>>>>>>>>> Sent: Wednesday, August 27, 2008 10:52 AM
>>>>>>>>> Subject: AGI goals (was Re: Information theoretic approaches to AGI
>>>>>>>>> (was
>>>>>>>>> Re:
>>>>>>>>> [agi] The Necessity of Embodiment))
>>>>>>>>> An AGI will not design its goals. It is up to humans to define the
>>>>>>>>> goals
>>>>>>>>> of
>>>>>>>>> an AGI, so that it will do what we want it to do.
>>>>>>>>>
>>>>>>>>> Unfortunately, this is a problem. We may or may not be successful
>>>>>>>>> in
>>>>>>>>> programming the goals of AGI to satisfy human goals. If we are not
>>>>>>>>> successful, then AGI will be useless at best and dangerous at
>>>>>>>>> worst.
>>>>>>>>> If
>>>>>>>>> we
>>>>>>>>> are successful, then we are doomed because human goals evolved in a
>>>>>>>>> primitive environment to maximize reproductive success and not in
>>>>>>>>> an
>>>>>>>>> environment where advanced technology can give us whatever we want.
>>>>>>>>> AGI
>>>>>>>>> will
>>>>>>>>> allow us to connect our brains to simulated worlds with magic
>>>>>>>>> genies,
>>>>>>>>> or
>>>>>>>>> worse, allow us to directly reprogram our brains to alter our
>>>>>>>>> memories,
>>>>>>>>> goals, and thought processes. All rational goal-seeking agents must
>>>>>>>>> have
>>>>>>>>> a
>>>>>>>>> mental state of maximum utility where any thought or perception
>>>>>>>>> would
>>>>>>>>> be
>>>>>>>>> unpleasant because it would result in a different state.
>>>>>>>>>
>>>>>>>>> -- Matt Mahoney, [EMAIL PROTECTED]
>>>>>>>>>
>>>>>>>>> ----- Original Message ----
>>>>>>>>> From: Valentina Poletti <[EMAIL PROTECTED]>
>>>>>>>>> To: agi@v2.listbox.com
>>>>>>>>> Sent: Tuesday, August 26, 2008 11:34:56 AM
>>>>>>>>> Subject: Re: Information theoretic approaches to AGI (was Re: [agi]
>>>>>>>>> The
>>>>>>>>> Necessity of Embodiment)
>>>>>>>>>
>>>>>>>>> Thanks very much for the info. I found those articles very
>>>>>>>>> interesting.
>>>>>>>>> Actually though this is not quite what I had in mind with the term
>>>>>>>>> information-theoretic approach. I wasn't very specific, my bad.
>>>>>>>>> What
>>>>>>>>> I
>>>>>>>>> am
>>>>>>>>> looking for is a a theory behind the actual R itself. These
>>>>>>>>> approaches
>>>>>>>>> (correnct me if I'm wrong) give an r-function for granted and work
>>>>>>>>> from
>>>>>>>>> that. In real life that is not the case though. What I'm looking
>>>>>>>>> for
>>>>>>>>> is
>>>>>>>>> how
>>>>>>>>> the AGI will create that function. Because the AGI is created by
>>>>>>>>> humans,
>>>>>>>>> some sort of direction will be given by the humans creating them.
>>>>>>>>> What
>>>>>>>>> kind
>>>>>>>>> of direction, in mathematical terms, is my question. In other words
>>>>>>>>> I'm
>>>>>>>>> looking for a way to mathematically define how the AGI will
>>>>>>>>> mathematically
>>>>>>>>> define its goals.
>>>>>>>>>
>>>>>>>>> Valentina
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On 8/23/08, Matt Mahoney <[EMAIL PROTECTED]> wrote:
>>>>>>>>>>
>>>>>>>>>> Valentina Poletti <[EMAIL PROTECTED]> wrote:
>>>>>>>>>> > I was wondering why no-one had brought up the >
>>>>>>>>>> > information-theoretic
>>>>>>>>>> > aspect of this yet.
>>>>>>>>>>
>>>>>>>>>> It has been studied. For example, Hutter proved that the optimal
>>>>>>>>>> strategy
>>>>>>>>>> of a rational goal seeking agent in an unknown computable
>>>>>>>>>> environment
>>>>>>>>>> is
>>>>>>>>>> AIXI: to guess that the environment is simulated by the shortest
>>>>>>>>>> program
>>>>>>>>>> consistent with observation so far [1]. Legg and Hutter also
>>>>>>>>>> propose
>>>>>>>>>> as
>>>>>>>>>> a
>>>>>>>>>> measure of universal intelligence the expected reward over a
>>>>>>>>>> Solomonoff
>>>>>>>>>> distribution of environments [2].
>>>>>>>>>>
>>>>>>>>>> These have profound impacts on AGI design. First, AIXI is
>>>>>>>>>> (provably)
>>>>>>>>>> not
>>>>>>>>>> computable, which means there is no easy shortcut to AGI. Second,
>>>>>>>>>> universal
>>>>>>>>>> intelligence is not computable because it requires testing in an
>>>>>>>>>> infinite
>>>>>>>>>> number of environments. Since there is no other well accepted test
>>>>>>>>>> of
>>>>>>>>>> intelligence above human level, it casts doubt on the main premise
>>>>>>>>>> of
>>>>>>>>>> the
>>>>>>>>>> singularity: that if humans can create agents with greater than
>>>>>>>>>> human
>>>>>>>>>> intelligence, then so can they.
>>>>>>>>>>
>>>>>>>>>> Prediction is central to intelligence, as I argue in [3]. Legg
>>>>>>>>>> proved
>>>>>>>>>> in
>>>>>>>>>> [4] that there is no elegant theory of prediction. Predicting all
>>>>>>>>>> environments up to a given level of Kolmogorov complexity requires
>>>>>>>>>> a
>>>>>>>>>> predictor with at least the same level of complexity. Furthermore,
>>>>>>>>>> above
>>>>>>>>>> a
>>>>>>>>>> small level of complexity, such predictors cannot be proven
>>>>>>>>>> because
>>>>>>>>>> of
>>>>>>>>>> Godel
>>>>>>>>>> incompleteness. Prediction must therefore be an experimental
>>>>>>>>>> science.
>>>>>>>>>>
>>>>>>>>>> There is currently no software or mathematical model of
>>>>>>>>>> non-evolutionary
>>>>>>>>>> recursive self improvement, even for very restricted or simple
>>>>>>>>>> definitions
>>>>>>>>>> of intelligence. Without a model you don't have friendly AI; you
>>>>>>>>>> have
>>>>>>>>>> accelerated evolution with AIs competing for resources.
>>>>>>>>>>
>>>>>>>>>> References
>>>>>>>>>>
>>>>>>>>>> 1. Hutter, Marcus (2003), "A Gentle Introduction to The Universal
>>>>>>>>>> Algorithmic Agent {AIXI}",
>>>>>>>>>> in Artificial General Intelligence, B. Goertzel and C. Pennachin
>>>>>>>>>> eds.,
>>>>>>>>>> Springer. http://www.idsia.ch/~marcus/ai/aixigentle.htm
>>>>>>>>>>
>>>>>>>>>> 2. Legg, Shane, and Marcus Hutter (2006),
>>>>>>>>>> A Formal Measure of Machine Intelligence, Proc. Annual machine
>>>>>>>>>> learning conference of Belgium and The Netherlands
>>>>>>>>>> (Benelearn-2006).
>>>>>>>>>> Ghent, 2006. http://www.vetta.org/documents/ui_benelearn.pdf
>>>>>>>>>>
>>>>>>>>>> 3. http://cs.fit.edu/~mmahoney/compression/rationale.html
>>>>>>>>>>
>>>>>>>>>> 4. Legg, Shane, (2006), Is There an Elegant Universal Theory of
>>>>>>>>>> Prediction?,
>>>>>>>>>> Technical Report IDSIA-12-06, IDSIA / USI-SUPSI,
>>>>>>>>>> Dalle Molle Institute for Artificial Intelligence, Galleria 2,
>>>>>>>>>> 6928
>>>>>>>>>> Manno,
>>>>>>>>>> Switzerland.
>>>>>>>>>> http://www.vetta.org/documents/IDSIA-12-06-1.pdf
>>>>>>>>>>
>>>>>>>>>> -- Matt Mahoney, [EMAIL PROTECTED]
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> -------------------------------------------
>>>>>>>>>> agi
>>>>>>>>>> Archives: https://www.listbox.com/member/archive/303/=now
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>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> --
>>>>>>>>> A true friend stabs you in the front. - O. Wilde
>>>>>>>>>
>>>>>>>>> Einstein once thought he was wrong; then he discovered he was
>>>>>>>>> wrong.
>>>>>>>>>
>>>>>>>>> For every complex problem, there is an answer which is short,
>>>>>>>>> simple
>>>>>>>>> and
>>>>>>>>> wrong. - H.L. Mencken
>>>>>>>>> ________________________________
>>>>>>>>> agi | Archives | Modify Your Subscription
>>>>>>>>> ________________________________
>>>>>>>>> agi | Archives | Modify Your Subscription
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>>>>>>>>> ________________________________
>>>>>>>>> agi | Archives | Modify Your Subscription
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>>>>>>>>
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>>>>>>>> agi
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>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> -------------------------------------------
>>>>>>> agi
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>>>>>>
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>>>>>> agi
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>>>>>
>>>>>
>>>>>
>>>>>
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