Hi

I had a couple of doubts while going through Ch. 15 (Probabilistic
Reasoning over Time)

- How do we represent an HMM as a recursive relational probabilistic
model?

- Ex 15.4 asks us to show that for some temporal probability models &
observation sequences, the procedure described on page 547 returns an
impossible state sequence. How do we go about this?

Any pointers would be greatly appreciated.

Thanks
Shraddha

Hi

I had a couple of questions while going through Ch 15 (Probabilistic
Reasoning over Time)

- How do we represent an HMM as a recursive relational probabilistic
model?

- Ex 15.4 asks us to show that the procedure described on page 547
for finding the most likely state sequence returns an impossible
state sequence, for some temporal probability models and observation
sequences. How do we go abt this?

Any pointers to this will be greatly appreciated

Thanks
Shraddha

Hi, i am taking an AI class with AIMA as text and
planning for a mini course project.

Any ideas, suggestions?

aman

________________________________________________________________________
Yahoo! India Insurance Special: Be informed on the best policies, services,
tools and more.
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Hi,
My proposal is "Vehicle route problem". The problem can be easy or complex
depending on the requirement.


Best regards/chenyu

-----Original Message-----
From: AMANULLAH NURULLAH [mailto:amanumr@...]
Sent: 2004Äê3ÔÂ11ÈÕ 23:11
To: aima-talk@yahoogroups.com
Subject: [aima-talk] Project ideas!

Hi, i am taking an AI class with AIMA as text and
planning for a mini course project.

Any ideas, suggestions?

aman

________________________________________________________________________
Yahoo! India Insurance Special: Be informed on the best policies, services,
tools and more.
Go to: http://in.insurance.yahoo.com/licspecial/index.html




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Hi,
Yes, ¡®Vehicle route problem¡¯ is a search problem. It is a central
problem in distribution management. The classical problem¡¯s
description is:
Fact:
1. brief:
a) One company has many customers for distribution products.
The company has many vehicles fore doing the work. Every customer¡¯s
order is small. Company wants to design a plan for all vehicles to
lower the distribution cost.
2. details:
a) G = (V,E) be an undirected graph where V = {v0,v1,¡­vn} is
a set of vertices representing customers. E ={(vi,vj)|vi,vj belong to
V, i<j} is the edge set.
b) Vertex v0 denotes a depot at which are based m identical
vehicles of capacity Q, where m is a decision variable or a constant.
c) Each customer of V\{v} has a non-negative demand qi, a non-
negative service time si. (waiting, unloading time)
d) A distance matrix (cij) is defined on E. We use the terms
distance and travel time interchangeably.
Problem(VRP---for vehicle route problem
1. designing a set of m vehicles routes having a minimum total
length and such that
a) each route start and ends at the depot
b) each remaining city is visited exactly once by one vehicle
c) the total demand of a route does not exceed
d) The total duration of a route does not exceed a preset
limit L.

Many people think that the core of VRP is TSP (traveling salesman
problem). But it is more difficult than TSP. TSP has only one
salesman.

Many variants of VRP exists,
1. To delete the above fact b. Some customer¡¯s order is very
big so that the order is more than the vehicle¡¯s capacity.
2. Another special vertex appears. It is not customer. It is
highway fee collection point. Therefore the Problem-1 requirement
should be modified.
3. Many different kind of vehicle exists, For example, one
kind of vehicles are for freezing products, one kind of vehicles are
for common products.
4. Every vehicle can have more than 1 route.
5. etc.



There are many different approachs to solve the problem:
1. hill climbing
2. simulated annealing
3. neural network
4. GA
5. ant system
6. etc

I think many knowledge of AIMA can be applied to this problem, and
this problem¡¯s requirement is easy to imagine and new requirement
can be added for more difficulty.




Thank you for your attention.
Best regards/chenyu (shanghai, China)


--- In aima-talk@yahoogroups.com, setiawan adhiputro <sea12_02@y...>
wrote:
> this "Vehicle route problem", is that about searching problem?
>
> E etech058 <etech058@o...> wrote:Hi,
> My proposal is "Vehicle route problem". The problem can be easy or
complex depending on the requirement.
>
>
> Best regards/chenyu
>
> -----Original Message-----
> From: AMANULLAH NURULLAH [mailto:amanumr@y...]
> Sent: 2004Äê3ÔÂ11ÈÕ 23:11
> To: aima-talk@yahoogroups.com
> Subject: [aima-talk] Project ideas!
>
> Hi, i am taking an AI class with AIMA as text and
> planning for a mini course project.
>
> Any ideas, suggestions?
>
> aman
>
>
______________________________________________________________________
__
> Yahoo! India Insurance Special: Be informed on the best policies,
services, tools and more.
> Go to: http://in.insurance.yahoo.com/licspecial/index.html
>
>
>
>
> Yahoo! Groups Links
>
>
>
>
>
>
> ---------------------------------
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>
>    To visit your group on the web, go to:
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>
>    To unsubscribe from this group, send an email to:
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>    Your use of Yahoo! Groups is subject to the Yahoo! Terms of
Service.
>
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Sent from
	 Mount Zion College of Engineering and Technology,
	 Lena Vilaku,Pilivalam PO,
	 Pudukkottai,
	 Tamil Nadu,
	 India. Pin - 622 003.
	 Ph - +91-4333-277125

Hi,
Yes, ¡®Vehicle route problem¡¯ is a search problem. It is a central
problem in distribution management. The classical problem¡¯s
description is:
Fact:
1.      brief:
a)      One company has many customers for distribution products.
The company has many vehicles fore doing the work. Every customer¡¯s
order is small. Company wants to design a plan for all vehicles to
lower the distribution cost.
2.      details:
a)      G = (V,E) be an undirected graph where V = {v0,v1,¡­vn} is
a set of vertices representing customers. E ={(vi,vj)|vi,vj belong to
V, i<j} is the edge set.
b)      Vertex v0 denotes a depot at which are based m identical
vehicles of capacity Q, where m is a decision variable or a constant.
c)      Each customer of V\{v} has a non-negative demand qi, a non-
negative service time si. (waiting, unloading time)
d)      A distance matrix (cij) is defined on E. We use the terms
distance and travel time interchangeably.
Problem(VRP---for vehicle route problem
1.      designing a set of m vehicles routes having a minimum total
length and such that
a)      each route start and ends at the depot
b)      each remaining city is visited exactly once by one vehicle
c)      the total demand of a route does not exceed
d)      The total duration of a route does not exceed a preset
limit L.

Many people think that the core of VRP is TSP (traveling salesman
problem). But it is more difficult than TSP. TSP has only one
salesman.

Many variants of VRP exists,
1.      To delete the above fact b. Some customer¡¯s order is very
big so that the order is more than the vehicle¡¯s capacity.
2.      Another special vertex appears. It is not customer. It is
highway fee collection point. Therefore the Problem-1 requirement
should be modified.
3.      Many different kind of vehicle exists, For example, one
kind of vehicles are for freezing products, one kind of vehicles are
for common products.
4.      Every vehicle can have more than 1 route.
5.      etc.



There are many different approachs to solve the problem:
1.      hill climbing
2.      simulated annealing
3.      neural network
4.      GA
5.      ant system
6.      etc

I think many knowledge of AIMA can be applied to this problem, and
this problem¡¯s requirement is easy to imagine and new requirement
can be added for more difficulty.




Thank you for your attention.
Best regards/chenyu (shanghai, China)

 

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Hi,
I have found a introduction which is about VRP as a learning problem.
But it is written in Japanease, I can't understand it now. After
asking helping others for translation, I could reply you later.

The website: http://citeseer.ist.psu.edu/
contains many paper about VRP. Maybe keyword of "learning VRP" will
help you found many similars.



In addition, I found some useful maganizes about this problem. I have
found them in my local library (shanghai library). Maybe you could
find them in your locals too.
1. JORS (Journal of the Operation Research)
    ISSN: 0160-5682

2. Annals of Operational Research
    ISSN: 0254-5330

3. Management Science
    ISSN: 0025-1909


If anyone have any useful reference about it, please let me know.
Thank you for your attention.
Best regards/chenyu



--- In aima-talk@yahoogroups.com, seA <sea12_02@y...> wrote:
> can i represent this VRP as a Reinforcement Learning problem?
becaus im thinking about using RL for my mini project,any idea?
>
> regards,
> seA/indonesia
>
> ³ÂÓí <chenyu468@y...> wrote:
> Hi,
> Yes, ¡®Vehicle route problem¡¯ is a search problem. It is a central
> problem in distribution management. The classical problem¡¯s
> description is:
> Fact:
> 1.      brief:
> a)      One company has many customers for distribution products.
> The company has many vehicles fore doing the work. Every customer¡¯
s
> order is small. Company wants to design a plan for all vehicles to
> lower the distribution cost.
> 2.      details:
> a)      G = (V,E) be an undirected graph where V = {v0,v1,?
amp;shy;vn} is
> a set of vertices representing customers. E ={(vi,vj)|vi,vj belong
to
> V, i<j} is the edge set.
> b)      Vertex v0 denotes a depot at which are based m identical
> vehicles of capacity Q, where m is a decision variable or a
constant.
> c)      Each customer of V\{v} has a non-negative demand qi, a non-
> negative service time si. (waiting, unloading time)
> d)      A distance matrix (cij) is defined on E. We use the terms
> distance and travel time interchangeably.
> Problem(VRP---for vehicle route problem
> 1.      designing a set of m vehicles routes having a minimum total
> length and such that
> a)      each route start and ends at the depot
> b)      each remaining city is visited exactly once by one vehicle
> c)      the total demand of a route does not exceed
> d)      The total duration of a route does not exceed a preset
> limit L.
>
> Many people think that the core of VRP is TSP (traveling salesman
> problem). But it is more difficult than TSP. TSP has only one
> salesman.
>
> Many variants of VRP exists,
> 1.      To delete the above fact b. Some customer¡¯s order is very
> big so that the order is more than the vehicle¡¯s capacity.
> 2.      Another special vertex appears. It is not customer. It is
> highway fee collection point. Therefore the Problem-1 requirement
> should be modified.
> 3.      Many different kind of vehicle exists, For example, one
> kind of vehicles are for freezing products, one kind of vehicles
are
> for common products.
> 4.      Every vehicle can have more than 1 route.
> 5.      etc.
>
>
>
> There are many different approachs to solve the problem:
> 1.      hill climbing
> 2.      simulated annealing
> 3.      neural network
> 4.      GA
> 5.      ant system
> 6.      etc
>
> I think many knowledge of AIMA can be applied to this problem, and
> this problem¡¯s requirement is easy to imagine and new requirement
> can be added for more difficulty.
>
>
>
>
> Thank you for your attention.
> Best regards/chenyu (shanghai, China)
>
>
>
> Do you Yahoo!?
> Yahoo! Mail - More reliable, more storage, less spam

i'm looking for a thesis topic in parallel computing. i'm wondering if i can
find a parallel algorithm for VRP.

---
alkin/philippines


--------- Original Message ---------

DATE: Mon, 22 Mar 2004 03:54:02
From: ³ÂÓí <chenyu468@...>
To: aima-talk@yahoogroups.com
Cc:

>Hi,
>I have found a introduction which is about VRP as a learning problem.
>But it is written in Japanease, I can't understand it now. After
>asking helping others for translation, I could reply you later.
>
>The website: http://citeseer.ist.psu.edu/
>contains many paper about VRP. Maybe keyword of "learning VRP" will
>help you found many similars.
>
>
>
>In addition, I found some useful maganizes about this problem. I have
>found them in my local library (shanghai library). Maybe you could
>find them in your locals too.
>1. JORS (Journal of the Operation Research)
>   ISSN: 0160-5682
>
>2. Annals of Operational Research
>   ISSN: 0254-5330
>
>3. Management Science
>   ISSN: 0025-1909
>
>
>If anyone have any useful reference about it, please let me know.
>Thank you for your attention.
>Best regards/chenyu
>
>
>
>--- In aima-talk@yahoogroups.com, seA <sea12_02@y...> wrote:
>> can i represent this VRP as a Reinforcement Learning problem?
>becaus im thinking about using RL for my mini project,any idea?
>>
>> regards,
>> seA/indonesia
>>
>> ³ÂÓí <chenyu468@y...> wrote:
>> Hi,
>> Yes, ¡®Vehicle route problem¡¯ is a search problem. It is a central
>> problem in distribution management. The classical problem¡¯s
>> description is:
>> Fact:
>> 1.      brief:
>> a)      One company has many customers for distribution products.
>> The company has many vehicles fore doing the work. Every customer¡¯
>s
>> order is small. Company wants to design a plan for all vehicles to
>> lower the distribution cost.
>> 2.      details:
>> a)      G = (V,E) be an undirected graph where V = {v0,v1,?
>amp;shy;vn} is
>> a set of vertices representing customers. E ={(vi,vj)|vi,vj belong
>to
>> V, i<j} is the edge set.
>> b)      Vertex v0 denotes a depot at which are based m identical
>> vehicles of capacity Q, where m is a decision variable or a
>constant.
>> c)      Each customer of V\{v} has a non-negative demand qi, a non-
>> negative service time si. (waiting, unloading time)
>> d)      A distance matrix (cij) is defined on E. We use the terms
>> distance and travel time interchangeably.
>> Problem(VRP---for vehicle route problem
>> 1.      designing a set of m vehicles routes having a minimum total
>> length and such that
>> a)      each route start and ends at the depot
>> b)      each remaining city is visited exactly once by one vehicle
>> c)      the total demand of a route does not exceed
>> d)      The total duration of a route does not exceed a preset
>> limit L.
>>
>> Many people think that the core of VRP is TSP (traveling salesman
>> problem). But it is more difficult than TSP. TSP has only one
>> salesman.
>>
>> Many variants of VRP exists,
>> 1.      To delete the above fact b. Some customer¡¯s order is very
>> big so that the order is more than the vehicle¡¯s capacity.
>> 2.      Another special vertex appears. It is not customer. It is
>> highway fee collection point. Therefore the Problem-1 requirement
>> should be modified.
>> 3.      Many different kind of vehicle exists, For example, one
>> kind of vehicles are for freezing products, one kind of vehicles
>are
>> for common products.
>> 4.      Every vehicle can have more than 1 route.
>> 5.      etc.
>>
>>
>>
>> There are many different approachs to solve the problem:
>> 1.      hill climbing
>> 2.      simulated annealing
>> 3.      neural network
>> 4.      GA
>> 5.      ant system
>> 6.      etc
>>
>> I think many knowledge of AIMA can be applied to this problem, and
>> this problem¡¯s requirement is easy to imagine and new requirement
>> can be added for more difficulty.
>>
>>
>>
>>
>> Thank you for your attention.
>> Best regards/chenyu (shanghai, China)
>>
>>
>>
>> Do you Yahoo!?
>> Yahoo! Mail - More reliable, more storage, less spam
>
>
>
>
>
>Yahoo! Groups Links
>
>
>
>
>
>



Need a new email address that people can remember
Check out the new EudoraMail at
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On Tue, 23 Mar 2004, al kin wrote:

>
> i'm looking for a thesis topic in parallel computing. i'm wondering if i
> can find a parallel algorithm for VRP.

Why don't you attempt to parallelize existing ones?

> ---
> alkin/philippines

--
Serguei A. Mokhov            |  /~\    The ASCII
Computer Science Department  |  \ / Ribbon Campaign
Concordia University         |   X    Against HTML
Montreal, Quebec, Canada     |  / \      Email!

Hello All,

I have to choose from the following tools to do an Experimental
Paper as part of my Advanced AI Course.

Can anyone please help me choosing based on which is the most used
and currenlty recent technology as I am new in this field.

1. Hugins (Distributed Belief Networks)
2. Grass Hopper (Mobile Agents)
3. ILOG (Planner)
4. CBR
5. KQML - XML (Agent Communication Language)
6. Matlab Toolboxes (Neural Networks, Image Processing ...)
7. NETICA - Norsys (Uncertainty Reasoning)

I found some of the commerical sites related to these tools but not
all of them, if you may know the related site for 3,4 or 5 Please
send them to me.

I know that they may tackle different aspects in AI, but the Idea is
that I want the most used and recent topic.. ALso I would appreciate
if you can provide me with some related experimental papers or links
to papers or to good search engines in the related fields.

Thanks a lot and really appreciate your help.
Noha.

hello,

im new here. im not sure if the question im going to pose here has
already been answered.

in the "logic" section of AIMA code, how does ask-each function (w.r.t
FOL.lisp) work?
Lets say ive created an fol-kb.
(setq kb (make-fol-kb))
(tell kb "Classroom(C1)")
(tell kb "Classroom(C2)")
(tell kb "IsBigger(C1,C2)")

1.if i want to query the kb to ask/ask-each if Classroom C1 is bigger
than C2 then how do I do it?
2.the clauses that i have added above to the kb - are they in the
right form?

regards
rahul

Dear Dr. Norvig,

I'm sending some typos I found in AIMA

Hope they help

Regards,
Raicho

------------------------------------------------------------
1.Chapter 14, page 515, figure 14.14


  replace

  x,w <- Weighted-Sample(bn)

  with

  x,w <- Weighted-Sample(bn,e)

2. chapter 14, page 527, line 10

  replace

  T(Tall(Nate) /\ T(Heavy(Nate)) = 0.4

  with

  T(Tall(Nate) /\ Heavy(Nate)) = 0.4

3.chapter 15, page 545

  replace

  bk+1:t = BACKWARD(bk+2:t,ek+1:t)

  with

  bk+1:t = BACKWARD(bk+2:t,ek+1)

4.Appendix A, page 982

  The typo is in the formula for F(x)
  We can't integrate over variable x because x is the upper boundary
for the integral

5.chaprer 16 page 596

  replace

  U(x1,...,xn) = f[f1(x1),...,fn(xn) , ]

  with

  U(x1,...,xn) = f[f1(x1),...,fn(xn)] ,

6.Chapter 17 page 637, figure 17.11

  The utility expectation for player O when E has a mixed strategy
should be -3*p + 4*(1-p) instead of 3*p + 4*(1-p)
  In the second case the utility expectation for E when O has a mixed
strategy should be -3*q + 4*(1-q) instead of 3*q + 4*(1-q)

7.Chapter 18 page 669, line 5 from the bottom
  replace

  "Thus, the probability that it agrees with a given example is at
least"

  with
  "Thus, the probability that it agrees with a given example is at
most"

8.Chapter 23 page 837, figure 23.1

  return sequence,best[i]

  should be replaced with

  return sequence,best[n]

  because the overal sequence probability is acumulated in best[n]

9.Chapter 24 page 869

  In the formula of the Gaussian filter sigma ^ 2 should be replaced
with sigma or it should be placed under the square root

Hi, this query is regarding the book "paradigms of AI
programming" by Peter Norvig.

I am trying to experiment of the STUDENT program in
chapter 7.   I downloaded 'paip.zip' and followed the
instructions, but i have the followng problem.  Can
some one help?  I am using the gnome common lisp.

When i say
(load "auxfns.lisp")

GCL says
Loading auxfns.lisp
Finished loading auxfns.lisp
T



Then i say
(requires "examples")

and the GCL says

Loading /home/nurullah/AI/Project/paip/examples.lisp
Loading /home/nurullah/AI/Project/paip/tutor.lisp
Loading /home/nurullah/AI/Project/paip/auxfns.lisp
Warning: SYMBOL is being redefined.
Finished loading
/home/nurullah/AI/Project/paip/auxfns.lisp



Error: (QUOTE SET-CHAPTER) is not of type SYMBOL.
Fast links are on: do (si::use-fast-links nil) for
debugging
Error signalled by DEFUN.
Broken at DEFUN.  Type :H for Help.

Then i try saying
(do-examples :all)

GCL says

0

Then whichever chapter # i specify,  the GCL says the
following

Error: The function SET-CHAPTER is undefined.
Fast links are on: do (si::use-fast-links nil) for
debugging
Error signalled by LET.
Backtrace: system:universal-error-handler > evalhook >
do-examples > block > let > let > tagbody > setq > + >
do-chapter > LET



Broken at DEFUN.


Can somebody help me run the PAIP code.

thanx
aman



________________________________________________________________________
Yahoo! India Insurance Special: Be informed on the best policies, services,
tools and more.
Go to: http://in.insurance.yahoo.com/licspecial/index.html

AMANULLAH NURULLAH <amanumr@...> writes:

> I am trying to experiment of the STUDENT program in
> chapter 7.   I downloaded 'paip.zip' and followed the
> instructions, but i have the followng problem.  Can
> some one help?  I am using the gnome common lisp.

I suggest that you use an implementation more conforming to the ANSI
Common Lisp standard, such as CMUCL.  Although things are improving
for GCL, its level of compliance may not be adequate for running the
PAIP code.


Paolo
--
Why Lisp? http://alu.cliki.net/RtL%20Highlight%20Film

Sent from
	 Mount Zion College of Engineering and Technology,
	 Lena Vilaku,Pilivalam PO,
	 Pudukkottai,
	 Tamil Nadu,
	 India. Pin - 622 003.
	 Ph - +91-4333-277125

Hi. Peter

Your game contained a "W32.Beagle.V@mm" virus. Could you please treat clean it with anti-virus software and re-send it.

Tahk you.

Augustine 

Hi, this query is regarding the book "paradigms of AI
programming" by Peter Norvig.

I am trying to experiment of the STUDENT program in
chapter 7.   I downloaded 'paip.zip' and followed the
instructions, but i have the followng problem.  Can
some one help?  I am using the gnome common lisp.

When i say
(load "auxfns.lisp")

GCL says
Loading auxfns.lisp
Finished loading auxfns.lisp
T
                                                     
                                                     
                                                
Then i say
(requires "examples")

and the GCL says

Loading /home/nurullah/AI/Project/paip/examples.lisp
Loading /home/nurullah/AI/Project/paip/tutor.lisp
Loading /home/nurullah/AI/Project/paip/auxfns.lisp
Warning: SYMBOL is being redefined.
Finished loading
/home/nurullah/AI/Project/paip/auxfns.lisp
                                                     
                                                     
                                                
Error: (QUOTE SET-CHAPTER) is not of type SYMBOL.
Fast links are on: do (si::use-fast-links nil) for
debugging
Error signalled by DEFUN.
Broken at DEFUN.  Type :H for Help.

Then i try saying
(do-examples :all)

GCL says

0

Then whichever chapter # i specify,  the GCL says the
following

Error: The function SET-CHAPTER is undefined.
Fast links are on: do (si::use-fast-links nil) for
debugging
Error signalled by LET.
Backtrace: system:universal-error-handler > evalhook >
do-examples > block > let > let > tagbody > setq > + >
do-chapter > LET
                                                     
                                                     
                                                
Broken at DEFUN.


Can somebody help me run the PAIP code.

thanx
aman



________________________________________________________________________
Yahoo! India Insurance Special: Be informed on the best policies, services, tools and more.
Go to: http://in.insurance.yahoo.com/licspecial/index.html

I am starting on some of the computing exercises in chapter 2 and
have a question regarding task environment.  According to the book,
task environment is specified by performance measure, environment,
actuators, and sensors.  The 1st two of these I can understand - but
why are acuators and sensors included in the task environment?
Shouldn't the task environment be the elements of interest outisde
of the agent?  Aren't sensors and actuators part of the agent
itself?  Upon first seeing this, I thought that my interpretation of
agent may be wrong - maybe the agent is simply the program/function
and the actuators and sensors are external entities that the agent
interacts with.  But there are several examples in this chapter
where the actuators and sensors are considered to be part of the
agent.  Can someone clear this up?  Is it just me or does the book
seem to be altering the definition of agent in different parts of
the chapter?

Thanks,

Bob

Hello everyone,
I am trying to implement "wumpus problem" by prolog according to
chapter 7 of AIMA version 1 first order logic description.

Mr.Author called the system containing this goal-based agent as
action-valued system because its action has been classified as "great
desired, good desired, medium desired, risky desired".

My implementation of 1 rules is follows:
1. if percept contains "glitter" and the agent doesn't hold gold, the
agent will do "grab" action. This is great desired.

The above rule is a little different from Mr.author's logic
description and it causes deadloop problem.

The problem is following:
1. For the first time when the agent in the "glitter" position, it
doesn't contain gold. Therefore the agent's grab operation will
result in "grab nothing". Therefore after agent get the next percept,
it will do this "great desired operation(grab)" again. The result
fails, grab, ..... fail, grab, dead loop happens.

2. I think the dead loop problem exists in many places of wumpus
problem.
For example:
2.1. In book, medium desired action is "move to Ok position and the
position is visited". I think "move" action contains "turn left, turn
right".
"risky desired action" is "move to not ok position and the position
is not visited".
     2.1.a Now test data is:
two pit exists, one at [1,3], the other at [3,1], gold at [4,2],
wumpus at [4,4].

     2.2 agent at [1,1] and start exploration.
        move to [2,1] and face "breezy", then move to [2,2] and
face "breezy" again. Then the agent faces a dead loop although it
clearly exists a safe path from [1,1] to [4,2].
I think the causation of the problem is "action-value system", the
conservative action" has higher desirability than "risky action". it
is impossible to try the "risky action" although these action exists.


Do you think my understanding is in error?
If yes, please give me your idea?
If not, could you tell me how to handle the fault of "action-valued
system".


Thank you for your attention.
best regards
chenyu (shanghai, China)

Dear Dr. Norvig,

Recently I read AIMA and I'm sending a list of the errors I found
I hope it helps

Raicho


1.Chapter 14, page 515, figure 14.14


	 replace

	 x,w <- Weighted-Sample(bn)

	 with
	 x,w <- Weighted-Sample(bn,e)

2. chapter 14, page 527, line 10

	 replace

	 T(Tall(Nate) /\ T(Heavy(Nate)) = 0.4

	 with

	 T(Tall(Nate) /\ Heavy(Nate)) = 0.4
3.chapter 15, page 545

	 replace


	 bk+1:t = BACKWARD(bk+2:t,ek+1:t)


	 with
	 bk+1:t = BACKWARD(bk+2:t,ek+1)
4.Appendix A, page 982

	 The typo is in the formula for F(x)
	 We can't integrate over variable x because x is the upper
boundary for the integral

5.chaprer 16 page 596

	 replace

	 U(x1,...,xn) = f[f1(x1),...,fn(xn) , ]

	 with

	 U(x1,...,xn) = f[f1(x1),...,fn(xn)] ,

6.Chapter 17 page 637, figure 17.11

	 The utility expectation for player O when E has a mixed
strategy should be -3*p + 4*(1-p) instead of 3*p + 4*(1-p)
	 In the second case the utility expectation for E when O has a
mixed strategy should be -3*q + 4*(1-q) instead of 3*q

+ 4*(1-q)

7.Chapter 18 page 669, line 5 from the bottom
	 replace

	 "Thus, the probability that it agrees with a given example is
at least"

	 with
	 "Thus, the probability that it agrees with a given example is
at most"

8.Chapter 23 page 837, figure 23.1

	 return sequence,best[i]

	 should be replaced with

	 return sequence,best[n]

	 because the overal sequence probability is accumulated in best
[n]

9.Chapter 24 page 869

	 In the formula of the Gaussian filter sigma ^ 2 should be
replaced with sigma or it should be placed under the square

root