Hi,

I have some questions regarding chapter 13.7 "The Wumpus World
Revisited" and the derivation of the distribution of P1,3 given known
and breeze evidence in particular:

1.) The probability that a particular cell contains a pit is 0.2 = 3/15,
so that pits are distributed over every cell except for a starting cell,
here 1,1. Now, when calculating the probability of a configuration
containing n pits in Equation 13.15 the product of each cell's
probability distribution is used because of the absolute independence
relationships. The resulting term is 0.2^n x 0.8^(16-n), although the
starting cell is not allowed to contain a pit, shouldn't it be 0.2^n x
0.8^(15-n) x 1 for configurations with no pit in the starting cell and 0
for configurations with a pit in the starting cell because of a
distribution of <0,1> for the starting cell to contain a pit?

2.) P(fringe) is the prior probability of a fringe configuration.
However, when showing the five possible models of the fringe in Figure
13.7 those three possibilities that do not hold the evidence of
perceived breezes in 1,2 and 2,1 are not shown. Does this fact mean that
P(fringe) is in any kind conditioned on the evidence or is it still the
prior distribution with those three combinations not shown?

3.) Later, P(known) is taken out of the summation and put into the
normalizing constant, the sum over the possible configurations of other
is said to be 1. But, that the sum over other of P(other) is 1 is
unclear to me. Of course, having 12 cells in unknown I have 2^12 = 4096
configurations of pits in these cells and a probability of 1/(2^12) to
get one particular configuration. Summing over 2^12 terms results of
course in 1. Same for fringe with 2 cells (2^2 configurations with a
probability of 1/(2^2) each) and other with 10 cells (2^10
configurations with a probability of 1/(2^10) each). But how is this
related to the prior distribution of <0.2,0.8> of each cell and the
product in Eq. 13.15 for the prior of one configuration?

Using combinatorics I have n!/( k!(n-k)! ) combinations to distribute k
holes over n cells, e.g. for distributing 3 pits over 15 cells
15!/(3!x12!) = 455 combinations.
Considering the 5 possible models in Figure 13.7, we have three cases
for other:
3 pits in the fringe: no pit in other - 10!/(0!x10!) * 0.2^0 * 0.8^10
2 pits in the fringe: one pit in other - 10!/(1!x9!) * 0.2^1 * 0.8^9
1 pit in the fringe: two pits in other - 10!/(2!x8!) * 0.2^2 * 0.8^8

However, these values do, of course, not sum up to 1. Even adding the
case, that is not shown in Figure 13.7 with three pits in other does not
correct that, neither does the summing over all 8 possibilities of pits
in the fringe. Actually the above equations do only fulfill our goal of
having the sum of other equal to 1, if we extend those three cases to
distribute every possible number of holes (with k ranging from 0 to 10
and n being 10) over the cells in other. Am I missing something out
here, e.g. a normalization, or am I on a completely misleading way?
So the fundamental question is:
"Is sum_other P(other) = 1?", "Why is it one?"
and, furthermore, is the result of the summation relevant at all or
could it just be put in the normalizing constant just as we did with
P(known)?

Despite my lousy explanations, I hope that my concerns are
understandable and I hope to receive some comprehensible answers.


Best regards
Dirk

Can you elaborate more on what the problem expects us to do in the
vice versa case?

thanks
kanishka

i want to follow this book in my teaching of AI course. when i was
taught this course at that time i worked on Prolog , but now i want to
work on LISP , i have intel machines , will please someone tell me
which LISP version is best for the codes of this book. and can i
download  that for educational purpose.
abdul samad

---

From: aima-talk@yahoogroups.com [mailto:aima-talk@yahoogroups.com] On Behalf Of a_s_khan_b
Sent: Wednesday, September 12, 2007 2:24 PM
To: aima-talk@yahoogroups.com
Subject: [aima-talk] as an instructor i need a bit help

i want to follow this book in my teaching of AI course. when i was
taught this course at that time i worked on Prolog , but now i want to
work on LISP , i have intel machines , will please someone tell me
which LISP version is best for the codes of this book. and can i
download that for educational purpose.
abdul samad

On Wed, Sep 12, 2007 06:24:13PM -0000, a_s_khan_b wrote:
> i want to follow this book in my teaching of AI course. when i was
> taught this course at that time i worked on Prolog , but now i want to
> work on LISP , i have intel machines , will please someone tell me
> which LISP version is best for the codes of this book. and can i
> download  that for educational purpose.
> abdul samad

More about the Lisp Code of AIMA:
http://aima.cs.berkeley.edu/lisp/doc/overview.html

For Intel machines there is of course Linux variants like www.debian.org which
includes GNU CLISP, a Common Lisp implementation. Debian and GNU CLISP are
completely free.

However, I don't know if the Lisp Aima Code works well with GNU CLISP?

A list of other free Lisp implementations is on:

http://en.wikibooks.org/wiki/Programming:Common_Lisp/Installation

--
Iván F. Villanueva B.
http://www.ffii.org

Are there any misleading heuristics apart from the non-admissible ones
that would make A* perform inefficiently? I found one example in this
CI  space Search tool
(http://www.cs.ubc.ca/labs/lci/CIspace/Version4/search/help/tutorial2.html).
  Can anyone tell me why it is misleading?

Hi,

I have some questions regarding chapter 13.7 "The Wumpus World
Revisited" and the derivation of the distribution of P1,3 given known
and breeze evidence in particular:

1.) The probability that a particular cell contains a pit is 0.2 = 3/15,
so that pits are distributed over every cell except for a starting cell,
here 1,1. Now, when calculating the probability of a configuration
containing n pits in Equation 13.15 the product of each cell's
probability distribution is used because of the absolute independence
relationships. The resulting term is 0.2^n x 0.8^(16-n), although the
starting cell is not allowed to contain a pit, shouldn't it be 0.2^n x
0.8^(15-n) x 1 for configurations with no pit in the starting cell and 0
for configurations with a pit in the starting cell because of a
distribution of <0,1> for the starting cell to contain a pit?

2.) P(fringe) is the prior probability of a fringe configuration.
However, when showing the five possible models of the fringe in Figure
13.7 those three possibilities that do not hold the evidence of
perceived breezes in 1,2 and 2,1 are not shown. Does this fact mean that
P(fringe) is in any kind conditioned on the evidence or is it still the
prior distribution with those three combinations not shown?

3.) Later, P(known) is taken out of the summation and put into the
normalizing constant, the sum over the possible configurations of other
is said to be 1. But, that the sum over other of P(other) is 1 is
unclear to me. Of course, having 12 cells in unknown I have 2^12 = 4096
configurations of pits in these cells and a probability of 1/(2^12) to
get one particular configuration. Summing over 2^12 terms results of
course in 1. Same for fringe with 2 cells (2^2 configurations with a
probability of 1/(2^2) each) and other with 10 cells (2^10
configurations with a probability of 1/(2^10) each). But how is this
related to the prior distribution of <0.2,0.8> of each cell and the
product in Eq. 13.15 for the prior of one configuration?

Using combinatorics I have n!/( k!(n-k)! ) combinations to distribute k
holes over n cells, e.g. for distributing 3 pits over 15 cells
15!/(3!x12!) = 455 combinations.
Considering the 5 possible models in Figure 13.7, we have three cases
for other:
3 pits in the fringe: no pit in other - 10!/(0!x10!) * 0.2^0 * 0.8^10
2 pits in the fringe: one pit in other - 10!/(1!x9!) * 0.2^1 * 0.8^9
1 pit in the fringe: two pits in other - 10!/(2!x8!) * 0.2^2 * 0.8^8

However, these values do, of course, not sum up to 1. Even adding the
case, that is not shown in Figure 13.7 with three pits in other does not
correct that, neither does the summing over all 8 possibilities of pits
in the fringe. Actually the above equations do only fulfill our goal of
having the sum of other equal to 1, if we extend those three cases to
distribute every possible number of holes (with k ranging from 0 to 10
and n being 10) over the cells in other. Am I missing something out
here, e.g. a normalization, or am I on a completely misleading way?
So the fundamental question is:
"Is sum_other P(other) = 1?", "Why is it one?"
and, furthermore, is the result of the summation relevant at all or
could it just be put in the normalizing constant just as we did with
P(known)?

Despite my lousy explanations, I hope that my concerns are
understandable and I hope to receive some comprehensible answers.

Best regards
Dirk

Hey anybody know where to find the errata list for 2nd edition 2006
reprint (LPE, Pearson Education)ISBN : 81-7758-367-0

Regards
Abhishek Vaid

--- In aima-talk@yahoogroups.com, "abhi_944" <abhi_944@...> wrote:
>
> Hey anybody know where to find the errata list for 2nd edition 2006
> reprint (LPE, Pearson Education)ISBN :

Well, I have the 9th print of the 2nd edition, and when I checked the
errata on:

http://aima.cs.berkeley.edu/errata.html

Printing 3 or 4 (the latest on this page), all the errors has been
corrected in my book. This implies that either:

- all the errors has been found and corrected in following prints,
- no one updated the above errata with new submissions.

In your case I would do the same, compare errors mentioned in the
above errata with your book. If everything is corrected, then I guess
you have the latest. The errata applies to both the official and the
international version (which is what you seems to be having).

Sharkie

Will there be a next edition of the AIMA text book?

Yes, there will be.  The authors are discussing the process of writing
a third edition now, but don't yet have a schedule.

-Peter Norvig

On 10/1/07, per.nyblom <per.nyblom@...> wrote:
> Will there be a next edition of the AIMA text book?
>
>
>
>
> Yahoo! Groups Links
>
>
>
>

Peter Norvig wrote:
>
> Yes, there will be.  The authors are discussing
> the process of writing a third edition now,
> but don't yet have a schedule.
>
> -Peter Norvig
>
> On 10/1/07, per.nyblom <per.nyblom@...> wrote:
>> Will there be a next edition of the AIMA text book?
>>

It would be nice if future editions of the AIMA textbook
were to include some treatment of the various independent
AI projects that are out there (on the fringe?) nowadays.

http://mind.sourceforge.net/Mind.html in JavaScript
is an AI Mind tutorial program that demonstrates the
important technique of "spreading activation" at work.

http://AIMind-I.com by Mr. Frank J. Russo is a program
in Forth spawned by the SourceForge Mind project but
running independently and now able to receive e-mails.

http://mentifex.virtualentity.com is a site devoted to
Wikipedia-based open-source artificial intelligence --
the idea that Wikipedia is where students of AI may
not only learn the multidisciplinary subjects needed
for creating true AI, but may turn around and write
contributions to the very articles being studied.

This is with regards to AIMA 2nd Ed, page 36.

The description says that if a performance penalty of one point is
assessed, the agent will fare poorly. I'm at a bit of a loss to explain
how the agent could ever perform well in that situation. If, as the first
bullet indicates, the agent gets one point at each time step for a clean
square, and loses a point for each movement, wouldn't that mean the agent
could never possibly score higher than 1 or lower than 0 at the end of its
run if a peformance penalty is assessed?

(This same scenario is referenced in question 2.9, but I believe my
question only asks for clarification, not an answer.)

..Chuck..


P.S. The aima-java class TrivialVacuumEnvironment.java awards 10 points
for a clean square, rather than the one point that seems warranted based
on the rules outlined on page 36. Which is correct? I emailed Ravi Mohan
about that, but never received a response.


--
"The idea that any one of us [presidential candidates] can bring about
this change is a fantasy, it is not the truth! We need you to bring about
the change on all these issues, we need you involved, we need you taking
responsibility!"
		 --John Edwards

Chuck,

> . Which is correct? I emailed Ravi Mohan
> about that, but never received a response.


  I had replied to you as soon  as I saw your email (The reply is
duplicated below, Please note the date. Maybe your filters plonked it
into your spam folder? )


Regds,
Ravi

*********************************************************

--- Ravi Mohan <magesmail@...> wrote:

> Date: Mon, 24 Sep 2007 04:10:46 -0700 (PDT)
> From: Ravi Mohan <magesmail@...>
> Subject: Re:
> aima.basic.vaccum.TrivialVaccumEnvironment.java
> To: Chuck Wolber <chuckw@...>
>
> Hi Chuck,
>
>
> > Since the Vaccum cleaner environment referred to
> in
> > question 2.7 refers to
> > the assumptions made on page 36, I am confused at
> > how a reward of ten
> > points was associated with cleaning a location?
> >
>
> See Section 3.2 (Example Problems) for where the
> path
> cost comes from. I am not sure where I got the
> performance measure of +10, but it looks reasonable
> to me.

> Feel free to alter it to whatever makes sense :-). Any value that is
nx for n > 1 should work.
>
> Thanks,
> Ravi
>
>
>
>
**********************************************************************

--- In aima-talk@yahoogroups.com, Chuck Wolber <chuckw@...> wrote:
>
>
> This is with regards to AIMA 2nd Ed, page 36.
>
> The description says that if a performance penalty of one point is
> assessed, the agent will fare poorly. I'm at a bit of a loss to explain
> how the agent could ever perform well in that situation. If, as the
first
> bullet indicates, the agent gets one point at each time step for a
clean
> square, and loses a point for each movement, wouldn't that mean the
agent
> could never possibly score higher than 1 or lower than 0 at the end
of its
> run if a peformance penalty is assessed?
>
> (This same scenario is referenced in question 2.9, but I believe my
> question only asks for clarification, not an answer.)
>
> ..Chuck..
>
>

>

On Tue, 2 Oct 2007, Ravi Mohan wrote:

> > Which is correct? I emailed Ravi Mohan about that, but never received
> > a response.
>
> I had replied to you as soon as I saw your email (The reply is
> duplicated below, Please note the date. Maybe your filters plonked it
> into your spam folder? )

Weird. You're right, I did receive it, at least my mail server logs say
so, but it wasn't trapped in my spam folder nor do I recall seeing it in
my inbox.


> > Since the Vaccum cleaner environment referred to in question 2.7
> > refers to the assumptions made on page 36, I am confused at how a
> > reward of ten points was associated with cleaning a location?
>
> See Section 3.2 (Example Problems) for where the path cost comes from. I
> am not sure where I got the performance measure of +10, but it looks
> reasonable to me.
>
> Feel free to alter it to whatever makes sense :-). Any value that is nx
> for n > 1 should work.

Thanks, that explains a lot.

I'm still curious as to what the value of setting the performance penalty
to -1 where the reward is +1. It seems as if they'll simply cancel each
other out. Or perhaps I am over thinking it and missing the fact that
question 2.9 speaks from the standpoint of an agent that cannot possibly
maximize its performance measure.

..Chuck..

--
"The idea that any one of us [presidential candidates] can bring about
this change is a fantasy, it is not the truth! We need you to bring about
the change on all these issues, we need you involved, we need you taking
responsibility!"
		 --John Edwards

• If the agent starts with both the squares as dirty and then continues for 1000 time steps as before i.e not stopping if both the squares are clean then it would score -ve 998.

• Similarly if the agent would have started with one of the squares as dirty and continues for 1000 time steps then it would end up in a score of -ve 999.

On Mon, Oct 01, 2007 08:20:57AM -0700, Peter Norvig wrote:
> Yes, there will be.  The authors are discussing the process of writing
> a third edition now, but don't yet have a schedule.

It would be great if at least some parts of the book will be publish under a
creative commons license. For other good books already under such a license,
see:
     http://textbookrevolution.org/

--
Iván F. Villanueva B.
Open General Artificial Intelligence  --  ogai.org
FFII.org Deutschland                  --  de.ffii.org
FFII.org España                       --  es.ffii.org

hi all
I think there is a critical point in chapter 3,section 4,page 75 of
seconde edition of AIMA,about worth case time of uniform-cost search.
authors defined C* as a cost of the optimal solution and used it to
determine the asymptotic complexity of this search, the point is that
C* is a problem dependent variable and could not be predicted before
solving the problem and has no upper or lower bound. for that it's not
useful for comparing the time consuming. please help me to understand
why C* is definded and what's the use of it?

thanx
Ehsan

Hi,

I'm trying to use the FOL KB in fol.lisp, and the only doc I found is
http://aima.cs.berkeley.edu/lisp/doc/overview-LOGIC.html

I want to be able to tell and ask FOL sentences, but I'm getting the
following errors, can anybody help please!

=============

CG-USER(11): (setf kb (make-fol-kb))
#S(FOL-KB :POSITIVE-CLAUSES #<EQ hash-table with 0 entries @
#x218330d2>
           :NEGATIVE-CLAUSES #<EQ hash-table with 0 entries @
#x21833482>
           :TEMP-ADDED NIL)
CG-USER(12): (tell kb "(forall(x,P(x)))")
NIL
CG-USER(15): (ask kb '(forall (x) (P x)))
Error: Attempt to take the value of the unbound variable `???'.
[condition type: UNBOUND-VARIABLE]


Thanks,
Dongnod

--
Buitms Mail (http://mail.buitms.edu.pk)
i have computer with intel architecture and running XP. which LISP version can
be run
on my system. plz provide me with the link from where i can download .
abbdul samad

--
This message has been scanned for viruses and
dangerous content by MailScanner, and is
believed to be clean.

On Tue, 2 Oct 2007, Abhijeet Sinha wrote:

> Now in the next description, it is proposed that the vacuum-cleaner
> agent would be wasting energy if it is allowed to move to the other
> square if both the squares are clean. So very rightly the performance
> measure deducts one point for moving into a clean square.

I interpreted the question a bit differently. I saw no indication that the
performance award of one point for being on a clean square would go away.
The phrase "if the performance measure includes a penalty of one point for
each movement" seems to indicate that this change is "included" with the
previously stated performance measure of "one point for each clean square
at each time step".

This would mean that the agent would be awarded a point for being on a
clean square and then lose it when it wastes energy going to another clean
square. The performance measure score would always be zero.


> So having one more assumption makes out earlier agent ir-rational.

I concur because the spirit of the question seems to indicate that the
penalty performance measure does not work in tandem with the original
performance measure. I hope the wording can be reconsidered in the third
edition.

Just for fun... based on my reasoning above, wouldn't you agree that since
the score would always be zero no matter what, the agent would be
rational? Clearly it would have maximized its score.

..Chuck..

This is a pretty late response to an old question.  I'm not sure if
the original requester is still interested, but here is my attempt to
explain.

The book actually does NOT say that it is currently unknown how the
pattern database approach can be applied to the Rubik's cube problem.
    What's unknown is the formulation of "Disjoint Pattern Databases"
to solve the Rubik's cube problem.

Korf's 1995 paper actually does NOT use a disjoint pattern database
approach to solve the problem, which would require moves of one
pattern database to be disjoint from moves of another pattern
database.  When disjoint pattern databases are used, disjoint moves
from different pattern databases are "summed" as opposed to "max-ed",
which is what Korf did.


David


--- In aima-talk@yahoogroups.com, Jan-Georg Smaus <smaus@...> wrote:
>
> Hi everyone,
>
> the book explains pattern databases on page 107 (2nd ed.). It says
that it
> is currently unknown how how the pattern database approach can be
applied
> to Rubik's cube. However there is now the work by Korf
>
> @InProceedings{Kor97,
>   author = 	 {Richard E. Korf},
>   title = 	 {Finding Optimal Solutions to {R}ubik's {C}ube Using
> Pattern Databases},
>   booktitle = 	 {Proceedings of the 14th National Conference on
> Artificial
>                   Intelligence and 9th Innovative Applications of
>                   Artificial Intelligence Conference},
>   pages =  {700-705},
>   year =  1997,
>   publisher =  {MIT Press}
> }
>
> which addresses this question. It would be nice if the authors could
> mention this work.
>
> Cheers,
> Jan Smaus, Freiburg
>

Hello,

I am new to this list.  I fixed the java ant build script, and would
like to check it into the project hosted on google code.  Can someone
add me as a developer to the project, or let me know where to mail the
build script fix to?

Also the jar file on the data project download seems to be corrupted.

Peter

Peter,
I am the maintainer of the java code. Send me the "fix" and a two line
description of your changes and if needed I will add it to the code.

Regds,
Ravi

PS:- The data files download and unzip perfectly on my machine, but I
use (Ubuntu) Linux, so it may be something windows specific.

--- In aima-talk@yahoogroups.com, "Peter Wiley-Cordone" <pcordone@...> w

I try to find answer book for this lecture, can any one help me to find
problems solution for AI- A modern approach ?

I am finding the pseudocode in the text almost no help at all and would
appreciate it if someone could help me with it.  For example, I think I
understand how the ALPHA-BETA-SEARCH algorithm that is described in
figure 6.7 works, but my understanding of the algorithm comes from
sources other than the pseudocode.  If someone could do a virtual step-
though of the code (as if using a debugger) using the example in figure
6.5 (or another example if it is easier), I might be able to get used
to the pseudocode style of the authors and interpret the later
pseudocode examples.  I don't understand what the variables a and s
represent.  As I get deeper into the book, I am finding myself more and
more frustrated because of not understanding the pseudocode examples.