At what level should I import the source code? should I just import
the aima level? or the src sublevel? Thank you for your time.
--- In aima-talk@yahoogroups.com, "Ravi Mohan" <magesmail@...> wrote:
>
>
> If you are having trouble with .project file, delete it. Use the
> "Import project from source" option in eclipse and it will generate a
> new .project file for you.
>
> Cheers,
> Ravi
>
> --- In aima-talk@yahoogroups.com, "inyourmind6684" <dtamayo@> wrote:
> >
> > I'm having trouble opening up the aima-java. I am using eclipse to
> > open the .project file. However, everytime I try to do this, I just
> > open up a blank workspace. There is no project defined and I have no
> > idea why. The very first time I did this, I saw the project and
> > everything. Then after that I seem to have lost the ability to view
> > it. I have tried unpacking the zip several times and even reinstalling
> > the eclipse program several times to make sure the settings were still
> > the same and have yet to be able to view the project again. Can anyone
> > help with this problem? Thank you for your time.
> >
>
If you are having trouble with .project file, delete it. Use the
"Import project from source" option in eclipse and it will generate a
new .project file for you.
Cheers,
Ravi
--- In aima-talk@yahoogroups.com, "inyourmind6684" <dtamayo@...> wrote:
>
> I'm having trouble opening up the aima-java. I am using eclipse to
> open the .project file. However, everytime I try to do this, I just
> open up a blank workspace. There is no project defined and I have no
> idea why. The very first time I did this, I saw the project and
> everything. Then after that I seem to have lost the ability to view
> it. I have tried unpacking the zip several times and even reinstalling
> the eclipse program several times to make sure the settings were still
> the same and have yet to be able to view the project again. Can anyone
> help with this problem? Thank you for your time.
>
I'm having trouble opening up the aima-java. I am using eclipse to
open the .project file. However, everytime I try to do this, I just
open up a blank workspace. There is no project defined and I have no
idea why. The very first time I did this, I saw the project and
everything. Then after that I seem to have lost the ability to view
it. I have tried unpacking the zip several times and even reinstalling
the eclipse program several times to make sure the settings were still
the same and have yet to be able to view the project again. Can anyone
help with this problem? Thank you for your time.
Would you please help me solving this problem? I am trying to upload the lisp codes (aima.lisp) but I encountered a problem.
I pasted the error message in this email. I have tried to look for information to solve this problem via search engine or archive aima mailing list, but no luck so far.
FYI, I am using Ubuntu 7.10 and install the aima-lisp package from Ubuntu repository.
Thank you for your help.
-- Regards,
Moeljono Widjaja Jakarta - Indonesia
===============
moeljono@narnia:~/aima$ clisp i i i i i i i ooooo o ooooooo ooooo ooooo
I I I I I I I 8 8 8 8 8 o 8 8 I \ `+' / I 8 8 8 8 8 8 \ `-+-' / 8 8 8 ooooo 8oooo `-__|__-' 8 8 8 8 8
| 8 o 8 8 o 8 8 ------+------ ooooo 8oooooo ooo8ooo ooooo 8
Copyright (c) Bruno Haible, Michael Stoll 1992, 1993 Copyright (c) Bruno Haible, Marcus Daniels 1994-1997
Copyright (c) Bruno Haible, Pierpaolo Bernardi, Sam Steingold 1998 Copyright (c) Bruno Haible, Sam Steingold 1999-2000 Copyright (c) Sam Steingold, Bruno Haible 2001-2006
[1]> (load "aima.lisp")
;; Loading file aima.lisp ... *** - READ from #<INPUT BUFFERED FILE-STREAM CHARACTER #P"aima.lisp" @150>: #<PACKAGE COMMON-LISP-CONTROLLER> has no external symbol with name "SOURCE-ROOT-PATH-TO-FASL-PATH"
The following restarts are available: ABORT :R1 ABORT Break 1 [2]>
Sir,
In your book AIMA, in the chapter of dynamic bayesian
networks(sample umbrella network),say; if I wish to
calculate the probability of rain on day 5, given evidence of rain
from day 1 to 5 i.e. p(X5|E1:5), using DECAYED-MCMC,
how do I implement the formula( inverse polynomial decay-
{g(t)=(alpha)(T-t+1)e-(1+delta)} given in
Mr. Stuart Russell's UAI-02 paper on decayed MCMC) for the above
problem ?
Please do help me,
Waiting for your reply,
Regards,
Abhijit.
I notice there is no AIMA Python or Lisp code for any of the planning
algorithms in chapter 11. I'd really like to find out if anyone has
developed any planning code based on the book pseudocode before I
strike out on my own.
Please post a quick reply if you've developed any planning code that
I could look at or use. Thanks.
Best regards,
Brandon
Hi all!
There is an exercise, 5.5(b) which I'm stuck with. I just can't get a
correct approach for it...
Consider we have 3 Groups (GroupA, GroupB, GroupC), 3 Rooms (RoomA,
RoomB, RoomC), 3 Teachers(TeacherA, TeacherB, TeacherC), 3 subjects
(SubjA, SubjB, SubjC) and 3 possible timeframes(Lesson1, Lesson2,
Lesson3).
Then I put constrains, that link teachers to subjects:
teach(TeacherA, SubjA)
teach(TeacherA, SubjC)
teach(TeacherB, SubjB)
teach(TeacherC, SubjC)
These are binary relations, that's excellent.
And now, how to write a constraint, that says: "Only one group can be
in the classroom" or "Only one teacher can teach one subject on the
same lesson" or "Group can have only one subject in the same lesson".
I just can't get an idea how to express that. What should be
variables? For example, we can put variable TimetableRecord, that is a
record and has the following fields: Group, Classroom, Teacher,
Lesson, Subject. But that is nit correct, I know... What approach is
correct?
I know, answers to exercises can';t be post here, but I don't need it.
I just need a hint on how to express restrictions I mentioned above...
Hi Nami, again. I think you are mixing the point I am emphasizing and Mr. Norvig does. I definitely agree with the Mr. Norvig's performance measurement criteria:
"As a general rule, it is better to design a performance measure according to what one
actually wants the in the environment, rather than according to how one thinks the agent
should behave"
I think I didn't say something against to this criteria. however, let's delve into this.
I want to change the enviroment, yes this is my goal.
Q - and what do I have to change the environment? A - I have my agent
Q - What does my agent have to change the environment? A - It has actions to change the environment.
Q - What actions does my agent have to change the environment ? A - It has A,B,C and D.
Q - Can my agent have another actions to change the environment other than the ones I specified above? for example, an E action. A - No, it can't.
Q - So, should I specify my goal according to my actions so that the environment will change the way that maximizes my expactation/goal? A - Yes, Exactly.
Q - But wait, there might appear some side effects stemming from those 4 actions. A - In that case you should have specified it among your actions.
As It is seen, I didn't
claim anything against that performance measure criteria. so giving it as example to refute my claim is kind of off the topic.
[[ In the earlier example I gave, the flaw was a good example, as rather than looking at "How
much has the agent travelled?", they were looking at "How much has the ball rolled"... and ]]
you should consider your agent's afore specified actions , not its human point of view implications; in either case [say it travelled or rolled the ball] your agent was *MOVING* [definition: to excitite engine with some current] to some direction to maximize your goal [ *MOVE* forward as much as possible ]. In this example, as i said before, your agent only carried out the actions you stated before, it *MOVED* and found a lump to maximize its goal. however, if it had carried out another action that would be analogous to my example; such as flying, or bouncing [which you hadn't specified before the experiment].
This example of Mr norvig is similar to teacher someone to play football with this action:
Action: You can take the ball anywhere you want excecpt the border lines. You will use ONLY your feet to take the
ball.
Goal: Throw the ball opposite team's goalpost as much as possible, do not let them do the same thing and do not hurt them.
Result: Players use ONLY their feet -as expected - and you punish them since they don't use their HEADs to score-which you didn't include in the action list. This is so ridicilous, teacher should have told the rules better; this is his own fault not the players.
--- 23/06/08 Pzt tarihinde daashmashty <nima.talebi@...> şöyle yazıyor:
Kimden: daashmashty <nima.talebi@...> Konu: [aima-talk] Re: A serious logical error in chapter 2. Kime: aima-talk@yahoogroups.com Tarihi: 23 Haziran 2008 Pazartesi, 11:52
You're argument is that dumping is not in the action list - but that's not the point that's
being made here.
The key that is being conveyed here is this...
"As a general rule, it is better to design a performance measure according to what one
actually wants the in the environment, rather than according to how one thinks the agent
should behave"
----
In the earlier example I gave, the flaw was a good example, as rather than looking at "How
much has the agent travelled?", they were looking at "How much has the ball rolled"... and
the robot discovered an honest solution to make the ball roll faster, which was not what
the designers were looking for.
Analogously here, we're saying that the agent could find a similar `flaw' (from the
designer's perspective) in the reward system, and continually dump and suck - this is just
an observation. .. We are hypothesizing. .. as in "Imagine if..."
...imagine *if* the robot could *dump*, then you'd see that this suck-dump-loop could be
a possibility, and to not allow that to happen, so we need to be careful about how the
reward system is set up.
I hope that clarifies it.
Nima
--- In aima-talk@yahoogrou ps.com, ozgur aydogan <aydogan_ozgur@ ...> wrote:
>
> Hi thanks for the answer, but , frankly, I know what Mr. Norvig wants to point by that
exceptional action of the cleaner. And, I see your example as well. But there is a little
difference between yours and the one I am mentioning about; your robot carried out the
actions you stated at the beginning of your experiment. It went through a direction[must
be only action you defined for it?] looking for the maximum goal you had described. Thus,
it only carried out the action you wanted it to do[and that scratching dog like position was
a result of your goal and action definitions. ]
>
> if you excuse me, I want to exemplify my question:
>
> Firstly, let's forget about the agent and just focus on the actions of it to abstract the
example. Here are the actions:
>
> A, B, C, D and those are mutually exclusive and none of them has side effects[like
DUMPing]. how would you get an extra action [say it E] out of this four ones? you can't
unless you implied it[Unlike your robotic example, It only carried out the actions you
described initially]. Although this looks trivial issue, it lacks information and bad example
for an introductory example. Lastly, If I added an additional action of a Truth Table during
the exam, my teacher would definitely have me failed :S
>
> Thanks again.
>
>
>
>
>
> --- 22/06/08 Pzr tarihinde Nima Talebi <nima.talebi@ ...> şöyle yazıyor:
> Kimden: Nima Talebi <nima.talebi@ ...>
> Konu: Re: [aima-talk] A serious logical error in chapter 2.
> Kime: aima-talk@yahoogrou ps.com
> Tarihi: 22 Haziran 2008 Pazar, 5:42
>
>
>
>
>
>
>
>
>
>
>
> I think the mention of `dump', not being in the action list of the agent, as you
correctly stated, in this environment at least serves no purpose.
> I think (please correct me if I'm wrong), Dr. Norvig mentions the action of a `dump' only
as a means of driving a point home - If allowed to be the designer of it's own performance
measure, it could easily make life easy for itself and, as an *example*, enter a loop of
clean-->dump- ->clean-- >dump... and satisfy itself that it is performing extremely well,
while in reality, it's doing nothing.
>
> Here is another example I've come across which may help...
> There was a robotics experiment (reinforcement learning) where a robot had a ball-
mouse attached to it behind, and it was `rewarded' for how much the ball rolled
*forward*, then allowed to experiment in a room... hopefully to learn to move as fast as
possible by itself.
>
> It was left to explore overnight, and in the morning, it was found sitting at an area in
the room where there was a lump on the ground... and what the agent was found doing
was this...
>
> Best way to draw a picture is this - imagine a dog rubbing it's behind on the carpet as to
scratch it.
> In effect - that is what it had learned to do. It was (incorrectly) being rewarded because
the series of motions it had learned and was carrying was indeed honoring (cheating)
the reward system...
> Â * the ball was rolling forward faster than ever before.
> This is analogous to the vacuum cleaner taking a dump and sucking it backup.  I
hope I've answered your question? =)
>
> Nima
>
> On Sun, Jun 22, 2008 at 10:58 AM, aydogan_ozgur <aydogan_ozgur@ yahoo.com>
wrote:
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> In Performance Measures of Chapter 2, Mr. Norvig refutes the goal of
>
> counting the number of cleaning a square by claiming that the
>
> cleaner,in which case, can successionly clean and DUMP the dirt of
>
> the same square, which is clearly worthless.
>
>
>
> However, there is a nontrivial glitch in this proposition. The Actions
>
> of the cleaner are
>
>
>
> to move right,
>
> to move left,
>
> to clean or
>
> to do nothing.
>
>
>
> In other words, there is no DUMP action, which Mr. Norvig uses to
>
> refute the proposition. So, I think, he can't just make up another
>
> action to refute the proposition. I hope, I am not exagerrating the case?
>
>
>
> Thanks for reading.
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> ____________ _________ _________ _________ _________ _________ _
> Yahoo! kullaniyor musunuz? http://tr.mail. yahoo.com
> Istenmeyen postadan biktiniz mi? Istenmeyen postadan en iyi korunma
> Yahoo! Posta'da
>
Yahoo! kullaniyor musunuz?
Istenmeyen postadan biktiniz mi? Istenmeyen postadan en iyi korunma Yahoo! Posta'da http://tr.mail.yahoo.com
You're argument is that dumping is not in the action list - but that's not the
point that's
being made here.
The key that is being conveyed here is this...
"As a general rule, it is better to design a performance measure according to
what one
actually wants the in the environment, rather than according to how one thinks
the agent
should behave"
----
In the earlier example I gave, the flaw was a good example, as rather than
looking at "How
much has the agent travelled?", they were looking at "How much has the ball
rolled"... and
the robot discovered an honest solution to make the ball roll faster, which was
not what
the designers were looking for.
Analogously here, we're saying that the agent could find a similar `flaw' (from
the
designer's perspective) in the reward system, and continually dump and suck -
this is just
an observation... We are hypothesizing... as in "Imagine if..."
...imagine *if* the robot could *dump*, then you'd see that this suck-dump-loop
could be
a possibility, and to not allow that to happen, so we need to be careful about
how the
reward system is set up.
I hope that clarifies it.
Nima
--- In aima-talk@yahoogroups.com, ozgur aydogan <aydogan_ozgur@...> wrote:
>
> Hi thanks for the answer, but , frankly, I know what Mr. Norvig wants to point
by that
exceptional action of the cleaner. And, I see your example as well. But there is
a little
difference between yours and the one I am mentioning about; your robot carried
out the
actions you stated at the beginning of your experiment. It went through a
direction[must
be only action you defined for it?] looking for the maximum goal you had
described. Thus,
it only carried out the action you wanted it to do[and that scratching dog like
position was
a result of your goal and action definitions.]
>
> if you excuse me, I want to exemplify my question:
>
> Firstly, let's forget about the agent and just focus on the actions of it to
abstract the
example. Here are the actions:
>
> A, B, C, D and those are mutually exclusive and none of them has side
effects[like
DUMPing]. how would you get an extra action [say it E] out of this four ones?
you can't
unless you implied it[Unlike your robotic example, It only carried out the
actions you
described initially]. Although this looks trivial issue, it lacks information
and bad example
for an introductory example. Lastly, If I added an additional action of a Truth
Table during
the exam, my teacher would definitely have me failed :S
>
> Thanks again.
>
>
>
>
>
> --- 22/06/08 Pzr tarihinde Nima Talebi <nima.talebi@...> şöyle yazıyor:
> Kimden: Nima Talebi <nima.talebi@...>
> Konu: Re: [aima-talk] A serious logical error in chapter 2.
> Kime: aima-talk@yahoogroups.com
> Tarihi: 22 Haziran 2008 Pazar, 5:42
>
>
>
>
>
>
>
>
>
>
>
> I think the mention of `dump', not being in the action list of the
agent, as you
correctly stated, in this environment at least serves no purpose.
> I think (please correct me if I'm wrong), Dr. Norvig mentions the action of a
`dump' only
as a means of driving a point home - If allowed to be the designer of it's own
performance
measure, it could easily make life easy for itself and, as an *example*, enter a
loop of
clean-->dump-->clean-->dump... and satisfy itself that it is performing
extremely well,
while in reality, it's doing nothing.
>
> Here is another example I've come across which may help...
> There was a robotics experiment (reinforcement learning) where a robot had a
ball-
mouse attached to it behind, and it was `rewarded' for how much the ball rolled
*forward*, then allowed to experiment in a room... hopefully to learn to move as
fast as
possible by itself.
>
> It was left to explore overnight, and in the morning, it was found sitting at
an area in
the room where there was a lump on the ground... and what the agent was found
doing
was this...
>
> Best way to draw a picture is this - imagine a dog rubbing it's behind on the
carpet as to
scratch it.
> In effect - that is what it had learned to do. It was (incorrectly) being
rewarded because
the series of motions it had learned and was carrying was
indeed honoring (cheating)
the reward system...
> Â * the ball was rolling forward faster than ever before.
> This is analogous to the vacuum cleaner taking a dump and sucking it backup.
 I
hope I've answered your question? =)
>
> Nima
>
> On Sun, Jun 22, 2008 at 10:58 AM, aydogan_ozgur <aydogan_ozgur@ yahoo.com>
wrote:
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> In Performance Measures of Chapter 2, Mr. Norvig refutes the goal
of
>
> counting the number of cleaning a square by claiming that the
>
> cleaner,in which case, can successionly clean and DUMP the dirt of
>
> the same square, which is clearly worthless.
>
>
>
> However, there is a nontrivial glitch in this proposition. The Actions
>
> of the cleaner are
>
>
>
> to move right,
>
> to move left,
>
> to clean or
>
> to do nothing.
>
>
>
> In other words, there is no DUMP action, which Mr. Norvig uses to
>
> refute the proposition. So, I think, he can't just make up another
>
> action to refute the proposition. I hope, I am not exagerrating the case?
>
>
>
> Thanks for reading.
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> ___________________________________________________________________
> Yahoo! kullaniyor musunuz? http://tr.mail.yahoo.com
> Istenmeyen postadan biktiniz mi? Istenmeyen postadan en iyi korunma
> Yahoo! Posta'da
>
Hi thanks for the answer, but , frankly, I know what Mr. Norvig wants to point by that exceptional action of the cleaner. And, I see your example as well. But there is a little difference between yours and the one I am mentioning about; your robot carried out the actions you stated at the beginning of your experiment. It went through a direction[must be only action you defined for it?] looking for the maximum goal you had described. Thus, it only carried out the action you wanted it to do[and that scratching dog like position was a result of your goal and action definitions.]
if you excuse me, I want to exemplify my question:
Firstly, let's forget about the agent and just focus on the actions of it to abstract the example. Here are the actions:
A, B, C, D and those are mutually exclusive and none of them has side effects[like
DUMPing]. how would you get an extra action [say it E] out of this four ones? you can't unless you implied it[Unlike your robotic example, It only carried out the actions you described initially]. Although this looks trivial issue, it lacks information and bad example for an introductory example. Lastly, If I added an additional action of a Truth Table during the exam, my teacher would definitely have me failed :S
Thanks again.
--- 22/06/08 Pzr tarihinde Nima Talebi <nima.talebi@...> şöyle yazıyor:
Kimden: Nima Talebi <nima.talebi@...> Konu: Re: [aima-talk] A serious logical error in chapter 2. Kime: aima-talk@yahoogroups.com Tarihi: 22 Haziran 2008 Pazar, 5:42
I think the mention of `dump', not being in the action list of the agent, as you correctly stated, in this environment at least serves no purpose.
I think (please correct me if I'm wrong), Dr. Norvig mentions the action of a `dump' only as a means of driving a point home - If allowed to be the designer of it's own performance measure, it could easily make life easy for itself and, as an *example*, enter a loop of clean-->dump-->clean-->dump... and satisfy itself that it is performing extremely well, while in reality, it's doing nothing.
Here is another example I've come across which may help...
There was a robotics experiment (reinforcement learning) where a robot had a ball-mouse attached to it behind, and it was `rewarded' for how much the ball rolled *forward*, then allowed to experiment in a room... hopefully to learn to move as fast as possible by itself.
It was left to explore overnight, and in the morning, it was found sitting at an area in the room where there was a lump on the ground... and what the agent was found doing was this...
Best way to draw a picture is this - imagine a dog rubbing it's behind on the carpet as to scratch it.
In effect - that is what it had learned to do. It was (incorrectly) being rewarded because the series of motions it had learned and was carrying was indeed honoring (cheating) the reward system...
* the ball was rolling forward faster than ever before.
This is analogous to the vacuum cleaner taking a dump and sucking it backup. I hope I've answered your question? =)
In Performance Measures of Chapter 2, Mr. Norvig refutes the goal of
counting the number of cleaning a square by claiming that the
cleaner,in which case, can successionly clean and DUMP the dirt of
the same square, which is clearly worthless.
However, there is a nontrivial glitch in this proposition. The Actions
of the cleaner are
to move right,
to move left,
to clean or
to do nothing.
In other words, there is no DUMP action, which Mr. Norvig uses to
refute the proposition. So, I think, he can't just make up another
action to refute the proposition. I hope, I am not exagerrating the case?
Thanks for reading.
Yahoo! kullaniyor musunuz?
Istenmeyen postadan biktiniz mi? Istenmeyen postadan en iyi korunma Yahoo! Posta'da http://tr.mail.yahoo.com
I think the mention of `dump', not being in the action list of the agent, as you correctly stated, in this environment at least serves no purpose.
I think (please correct me if I'm wrong), Dr. Norvig mentions the action of a `dump' only as a means of driving a point home - If allowed to be the designer of it's own performance measure, it could easily make life easy for itself and, as an *example*, enter a loop of clean-->dump-->clean-->dump... and satisfy itself that it is performing extremely well, while in reality, it's doing nothing.
Here is another example I've come across which may help...
There was a robotics experiment (reinforcement learning) where a robot had a ball-mouse attached to it behind, and it was `rewarded' for how much the ball rolled *forward*, then allowed to experiment in a room... hopefully to learn to move as fast as possible by itself.
It was left to explore overnight, and in the morning, it was found sitting at an area in the room where there was a lump on the ground... and what the agent was found doing was this...
Best way to draw a picture is this - imagine a dog rubbing it's behind on the carpet as to scratch it.
In effect - that is what it had learned to do. It was (incorrectly) being rewarded because the series of motions it had learned and was carrying was indeed honoring (cheating) the reward system...
* the ball was rolling forward faster than ever before.
This is analogous to the vacuum cleaner taking a dump and sucking it backup. I hope I've answered your question? =)
Nima
On Sun, Jun 22, 2008 at 10:58 AM, aydogan_ozgur <aydogan_ozgur@...> wrote:
In Performance Measures of Chapter 2, Mr. Norvig refutes the goal of
counting the number of cleaning a square by claiming that the
cleaner,in which case, can successionly clean and DUMP the dirt of
the same square, which is clearly worthless.
However, there is a nontrivial glitch in this proposition. The Actions
of the cleaner are
to move right,
to move left,
to clean or
to do nothing.
In other words, there is no DUMP action, which Mr. Norvig uses to
refute the proposition. So, I think, he can't just make up another
action to refute the proposition. I hope, I am not exagerrating the case?
In Performance Measures of Chapter 2, Mr. Norvig refutes the goal of
counting the number of cleaning a square by claiming that the
cleaner,in which case, can successionly clean and DUMP the dirt of
the same square, which is clearly worthless.
However, there is a nontrivial glitch in this proposition. The Actions
of the cleaner are
to move right,
to move left,
to clean or
to do nothing.
In other words, there is no DUMP action, which Mr. Norvig uses to
refute the proposition. So, I think, he can't just make up another
action to refute the proposition. I hope, I am not exagerrating the case?
Thanks for reading.
Hi Again,
Has anyone tried to solve this problem? I have failed to prove what the question
is asking in
the following PDF...
http://ai.autonomy.net.au/chrome/site/neurons/linear-transfer-fns.pdf
...I don't think I've made any mistakes in what I *have* done, however I think
I've missed
something along the way - could someone point it out to me?
Thank you!
Nima
Hi Everybody.
I've created a tutorial as part of my own learning while reading AIMA, and would
like to share
it with anyone who would be interested.
http://ai.autonomy.net.au/wiki/Tutorial/Mathematics/NeuralNetByHand
Any corrections and constructive criticism would be welcomed, and I hope you
find it useful.
Nima
Thanks for your feedback. I'm aware of the wikipedia pages, this is more about putting the algorithms to practice, which is what I've attempted to do, in hope to make it useful for myself and hopefully others.
I'm after an objective feedback and would like to know if what I have demonstrated is correct, or otherwise. I don't feel comfortable with myself being the only person verifying the correctness of my implementation.
I'd still like any feedback on this and would greatly appreciate it.
Nima
On Thu, Jun 5, 2008 at 5:47 AM, Ivan F. Villanueva B. <ivan@...> wrote:
On Wed, Jun 04, 2008 10:43:55AM -0000, daashmashty wrote:
Hi all,
I've been reading AIMA religiously, and making my notes on a wiki. In trying to
get a clear
understanding of MiniMax and Alpha-Beta pruning, I've tried to illustrate it via
an example...
* http://ai.autonomy.net.au/wiki/Algorithms/AlphaBetaPruning
* http://ai.autonomy.net.au/wiki/Algorithms/MiniMax
...however I do not have complete confidence in this yet, could anyone here have
a look and
tell me if it is indeed correct, or if it is incorrect, where the error is.
A big thanks in advance =)
Nima
Hello,
in a Bayesian setting, how can I express known estimation errors? I am
working on a problem where I want to model estimation volatility, but
also biases on the estimation and estimation volatility.
So say I one want to guess who will win the NBA basketball game Boston
Celtics vs Detroit Pistons this evening. If I am an expert, not only
will my belief match the objective probability (if it exists at all),
but I will have a good understanding of how good my guess will be.
Furthermore say I am trading a contract in a prediction market and am
estimating how good guesses of other people is. Currently the contract
trades at .32$ which means the implied probability of the event BC,
'Boston Celtics wins', is 32%. What is a good algorithm to derive
other agents' estimation error? I might form the belief that the
estimation error of the implied probability of BC is ±10%.
Hello,
First I would like to say I read AIMA 2nd edition for fun and it was fantastic.
I have been studying the hybrid Bayesian network for an application and am
wearing out
pages 501 and 502 which talk about combining a discrete true/false parent
(Subsidy) and
a continuous parent (Harvest) into a linear Gaussian distribution.
I am simply not seeing where the variables required as parameters for the
Gaussian are
coming from. It seems that the h variable is the Harvest, and Subsidy would be
some
probability.
The extra variables are:
a-true, b-true, stddev-true
a-false, b-false, stddev-false
Would someone take pity on me? :)
Thanks,
Scott
It might be easier to think of it as 8 values. For example, these might be the eight values:
[.2, .2, .1, .1, .1, .1 .1, .1]
Notice that these sum to 1.0 (since they are mmutually exclusive and exhaustive possibilities). Because we know they sum to 1.0, if we know the first 7, we must know the last one. The first 7 sum to .9, so the last one must be .1. Therefore, if we want to save space, we can drop the last one:
[.2, .2, .1, .1, .1, .1 .1]
There are 7 values, and we can derive the fact that the missing 8th value must be .1.
-Peter
On Sun, May 18, 2008 at 2:53 AM, meskabnada <meskabnada@...> wrote:
Hi,
In the book p.482 it is mentioned that the full joint distribution
table has seven independent members (2*2*2 - 1 because the number must
sum to 1). I didn't understand why it is 7. the provided explanation is
not clear for me.
Hi,
In the book p.482 it is mentioned that the full joint distribution
table has seven independent members (2*2*2 - 1 because the number must
sum to 1). I didn't understand why it is 7. the provided explanation is
not clear for me.
Thanks for you cooperation
zayan
Well there is a better (and new) detail in the end of the chapter which states that a multilayer ff nn is not a perceptron, so within the book, it's all consistent again, and I'll trust the book more than wikipedia, but if anyone has anything further to add, be my guest.
Nima
On Fri, May 2, 2008 at 11:37 PM, daashmashty <nima.talebi@...> wrote:
Are multilayer feed-forward neural networks __not__ classed as perceptrons, while single
layer ones are? On sites unrelated to AIMA (such as wikipedia), I see many references to
multilayer perceptrons, in the book I haven't - what is the correct distinction?
Are multilayer feed-forward neural networks __not__ classed as perceptrons,
while single
layer ones are? On sites unrelated to AIMA (such as wikipedia), I see many
references to
multilayer perceptrons, in the book I haven't - what is the correct distinction?
Hello,
Patch for
http://www.cs.berkeley.edu/~russell/code/search/algorithms/simple.lisp
so that iterative-deepening-search works.
--- search/algorithms/simple.lisp.orig 2008-02-26 11:43:18.000000000 +0200
+++ search/algorithms/simple.lisp 2008-02-26 11:31:38.000000000 +0200
@@ -36,9 +36,13 @@
"Search depth-first, but only up to LIMIT branches deep in the tree."
(cond ((goal-test problem node) node)
((>= (node-depth node) limit) :cut-off)
- (t (for each n in (expand node problem) do
+ (t (let (cut-off)
+ (for each n in (expand node problem) do
(let ((solution (depth-limited-search problem limit n)))
- (when solution (RETURN solution)))))))
+ (cond
+ ((eq solution :cut-off) (setf cut-off :cut-off))
+ (solution (RETURN solution)))))
+ cut-off))))
;;;; Search Algorithms That Use Heuristic Information
David Tolpin
http://davidashen.net/
Quran's Invariance & Artificial Intelligence (Modern Unified Perspective)
The challenge of finding the invariant synchronous words of the Quran
http://intelligentquran.net/
When I was going through the 8 puzzle example in fig. 3.4 of AIMA 2nd edition, I found that from the given start state (7 2 4 5 0 6 8 3 1), it is NOT possible to reach the given Goal state ( 0 1 2 3 4 5 6 7 8) since both the states belong to two disjoint sets and it is not possible to transform from a state in one set to a state in another set , immaterial of how many moves we make.
Any similar comments and discussions appreciated.
Thanks Ram
Be a better friend, newshound, and know-it-all with Yahoo! Mobile. Try it now.
Hi,
I've read section 13.3 of the book on de Finetti's theorem and cannot
figure out how to solve exercise 13.3. Why would you have a "range of
values" for A ^ B ?( where ^ represents intersection. )
Any help will be appreciated.
Regards,
Aashish
I
think I’ve found an error in the first edition, first printing that is
not mentioned yet:
In
chapter 19, section 4 (“Multilayer Feed-Forward Networks”),
subsection “Back-propagation as gradient descent search” (page 580)
in the fifth line from the bottom:
[…]
“on each example as a function of the all the weights” […]
should
be
[…]
“on each example as a function of all the weights” […]