----- Original Message -----
From: "meshteb" <meshteb@...>
Sent: August 12, 2002 6:44 AM

> Can you please help me to examine the AI literature to discover
> whether or not the following tasks can  currently be solved by
> computers?
> 1. Playing a decent game of table tennis

Depends on your definition of "decent". :) There are some "decent"
games out there, I beleive; I'm not quite sure why you need AI for that
though....

> 2. Driving in the center of Washington.

If it's computer simultion, possibly. An AI agent, driving a physical car real
time,
anywhere, not just Washington; doubdfully :) Just consider how many
volumes of information you need to process in milliseconds and make a decision!

> 3. Writing an intentionally funny story.

There were such attempts, even long time ago. Remeber reading somewhere examples
and some of them made me laugh, but computers lack imagination to come up
w/ smth intentionally unique...

> 4. Translating spoken English into spoken Swedish in real time.

This is from pattern recognition (speech, specifically) and NLP.
There's a lot of research going on in these areas. Depending on how
much of vocabulary and intonations and accents you want the system to recognize
and translate properly, it's possible I believe.

The last two are more "tangible" and "feasible" to implement.
Chances are there are software programs that make it real.

-s

PS: All of the above is my opinion only and not necessarily other members of
this group ;) For the lack of time, I did not provide any concrete examples
to support my statements; I hope some people can provide them for me
or some time later I'll do it myself :)

Dear everybody,
I am an instructure at Sri Lanka Institute of Information Technology.
I really apreciate if you can  explain this output from
aima.examples.TestSearch?



Trivial search space based on adding 2 or 3
DFS:
State: (46) Depth: 23 Cost: 46.0 by applying add2
State: (44) Depth: 22 Cost: 44.0 by applying add2
State: (42) Depth: 21 Cost: 42.0 by applying add2
State: (40) Depth: 20 Cost: 40.0 by applying add2
State: (38) Depth: 19 Cost: 38.0 by applying add2
State: (36) Depth: 18 Cost: 36.0 by applying add2
State: (34) Depth: 17 Cost: 34.0 by applying add2
State: (32) Depth: 16 Cost: 32.0 by applying add2
State: (30) Depth: 15 Cost: 30.0 by applying add2
State: (28) Depth: 14 Cost: 28.0 by applying add2
State: (26) Depth: 13 Cost: 26.0 by applying add2
State: (24) Depth: 12 Cost: 24.0 by applying add2
State: (22) Depth: 11 Cost: 22.0 by applying add2
State: (20) Depth: 10 Cost: 20.0 by applying add2
State: (18) Depth: 9 Cost: 18.0 by applying add2
State: (16) Depth: 8 Cost: 16.0 by applying add2
State: (14) Depth: 7 Cost: 14.0 by applying add2
State: (12) Depth: 6 Cost: 12.0 by applying add2
State: (10) Depth: 5 Cost: 10.0 by applying add2
State: (8) Depth: 4 Cost: 8.0 by applying add2
State: (6) Depth: 3 Cost: 6.0 by applying add2
State: (4) Depth: 2 Cost: 4.0 by applying add2
State: (2) Depth: 1 Cost: 2.0 by applying add2
Starting at state: (0)

Depth Bounded (depth 7):
No solution

BFS:
State: (23) Depth: 8 Cost: 30.0 by applying add2
State: (21) Depth: 7 Cost: 28.0 by applying add3
State: (18) Depth: 6 Cost: 24.0 by applying add3
State: (15) Depth: 5 Cost: 20.0 by applying add3
State: (12) Depth: 4 Cost: 16.0 by applying add3
State: (9) Depth: 3 Cost: 12.0 by applying add3
State: (6) Depth: 2 Cost: 8.0 by applying add3
State: (3) Depth: 1 Cost: 4.0 by applying add3
Starting at state: (0)


Uniform cost:
State: (23) Depth: 11 Cost: 24.0 by applying add3
State: (20) Depth: 10 Cost: 20.0 by applying add2
State: (18) Depth: 9 Cost: 18.0 by applying add2
State: (16) Depth: 8 Cost: 16.0 by applying add2
State: (14) Depth: 7 Cost: 14.0 by applying add2
State: (12) Depth: 6 Cost: 12.0 by applying add2
State: (10) Depth: 5 Cost: 10.0 by applying add2
State: (8) Depth: 4 Cost: 8.0 by applying add2
State: (6) Depth: 3 Cost: 6.0 by applying add2
State: (4) Depth: 2 Cost: 4.0 by applying add2
State: (2) Depth: 1 Cost: 2.0 by applying add2
Starting at state: (0)

Iterated Deepening Search:
State: (23) Depth: 8 Cost: 30.0 by applying add3
State: (20) Depth: 7 Cost: 26.0 by applying add3
State: (17) Depth: 6 Cost: 22.0 by applying add3
State: (14) Depth: 5 Cost: 18.0 by applying add3
State: (11) Depth: 4 Cost: 14.0 by applying add3
State: (8) Depth: 3 Cost: 10.0 by applying add3
State: (5) Depth: 2 Cost: 6.0 by applying add3
State: (2) Depth: 1 Cost: 2.0 by applying add2
Starting at state: (0)

A* Search:
State: (23) Depth: 11 Cost: 24.0 by applying add3
State: (20) Depth: 10 Cost: 20.0 by applying add2
State: (18) Depth: 9 Cost: 18.0 by applying add2
State: (16) Depth: 8 Cost: 16.0 by applying add2
State: (14) Depth: 7 Cost: 14.0 by applying add2
State: (12) Depth: 6 Cost: 12.0 by applying add2
State: (10) Depth: 5 Cost: 10.0 by applying add2
State: (8) Depth: 4 Cost: 8.0 by applying add2
State: (6) Depth: 3 Cost: 6.0 by applying add2
State: (4) Depth: 2 Cost: 4.0 by applying add2
State: (2) Depth: 1 Cost: 2.0 by applying add2
Starting at state: (0)

Greedy Search:
State: (23) Depth: 8 Cost: 30.0 by applying add2
State: (21) Depth: 7 Cost: 28.0 by applying add3
State: (18) Depth: 6 Cost: 24.0 by applying add3
State: (15) Depth: 5 Cost: 20.0 by applying add3
State: (12) Depth: 4 Cost: 16.0 by applying add3
State: (9) Depth: 3 Cost: 12.0 by applying add3
State: (6) Depth: 2 Cost: 8.0 by applying add3
State: (3) Depth: 1 Cost: 4.0 by applying add3
Starting at state: (0)

Generalized missionaries and cannibals (3,3,1)
Breadth-first search skipping repeated states
State: (0:3,0:3,0:1) Depth: 11 Cost: 11.0 by applying (0,2,-1)
State: (0:3,2:1,1:0) Depth: 10 Cost: 10.0 by applying (0,1,1)
State: (0:3,1:2,0:1) Depth: 9 Cost: 9.0 by applying (0,2,-1)
State: (0:3,3:0,1:0) Depth: 8 Cost: 8.0 by applying (0,1,1)
State: (0:3,2:1,0:1) Depth: 7 Cost: 7.0 by applying (2,0,-1)
State: (2:1,2:1,1:0) Depth: 6 Cost: 6.0 by applying (1,1,1)
State: (1:2,1:2,0:1) Depth: 5 Cost: 5.0 by applying (2,0,-1)
State: (3:0,1:2,1:0) Depth: 4 Cost: 4.0 by applying (0,1,1)
State: (3:0,0:3,0:1) Depth: 3 Cost: 3.0 by applying (0,2,-1)
State: (3:0,2:1,1:0) Depth: 2 Cost: 2.0 by applying (0,1,1)
State: (3:0,1:2,0:1) Depth: 1 Cost: 1.0 by applying (0,2,-1)
Starting at state: (3:0,3:0,1:0)

Breadth-first search with repeated states
State: (0:3,0:3,0:1) Depth: 11 Cost: 11.0 by applying (0,2,-1)
State: (0:3,2:1,1:0) Depth: 10 Cost: 10.0 by applying (0,1,1)
State: (0:3,1:2,0:1) Depth: 9 Cost: 9.0 by applying (0,2,-1)
State: (0:3,3:0,1:0) Depth: 8 Cost: 8.0 by applying (0,1,1)
State: (0:3,2:1,0:1) Depth: 7 Cost: 7.0 by applying (2,0,-1)
State: (2:1,2:1,1:0) Depth: 6 Cost: 6.0 by applying (1,1,1)
State: (1:2,1:2,0:1) Depth: 5 Cost: 5.0 by applying (2,0,-1)
State: (3:0,1:2,1:0) Depth: 4 Cost: 4.0 by applying (0,1,1)
State: (3:0,0:3,0:1) Depth: 3 Cost: 3.0 by applying (0,2,-1)
State: (3:0,2:1,1:0) Depth: 2 Cost: 2.0 by applying (0,1,1)
State: (3:0,1:2,0:1) Depth: 1 Cost: 1.0 by applying (0,2,-1)
Starting at state: (3:0,3:0,1:0)

A* Search:
State: (0:3,0:3,0:1) Depth: 11 Cost: 11.0 by applying (0,2,-1)
State: (0:3,2:1,1:0) Depth: 10 Cost: 10.0 by applying (0,1,1)
State: (0:3,1:2,0:1) Depth: 9 Cost: 9.0 by applying (0,2,-1)
State: (0:3,3:0,1:0) Depth: 8 Cost: 8.0 by applying (0,1,1)
State: (0:3,2:1,0:1) Depth: 7 Cost: 7.0 by applying (2,0,-1)
State: (2:1,2:1,1:0) Depth: 6 Cost: 6.0 by applying (1,1,1)
State: (1:2,1:2,0:1) Depth: 5 Cost: 5.0 by applying (2,0,-1)
State: (3:0,1:2,1:0) Depth: 4 Cost: 4.0 by applying (0,1,1)
State: (3:0,0:3,0:1) Depth: 3 Cost: 3.0 by applying (0,2,-1)
State: (3:0,2:1,1:0) Depth: 2 Cost: 2.0 by applying (0,1,1)
State: (3:0,1:2,0:1) Depth: 1 Cost: 1.0 by applying (0,2,-1)
Starting at state: (3:0,3:0,1:0)

Greedy Search:
State: (0:3,0:3,0:1) Depth: 11 Cost: 11.0 by applying (0,2,-1)
State: (0:3,2:1,1:0) Depth: 10 Cost: 10.0 by applying (0,1,1)
State: (0:3,1:2,0:1) Depth: 9 Cost: 9.0 by applying (0,2,-1)
State: (0:3,3:0,1:0) Depth: 8 Cost: 8.0 by applying (0,1,1)
State: (0:3,2:1,0:1) Depth: 7 Cost: 7.0 by applying (2,0,-1)
State: (2:1,2:1,1:0) Depth: 6 Cost: 6.0 by applying (1,1,1)
State: (1:2,1:2,0:1) Depth: 5 Cost: 5.0 by applying (2,0,-1)
State: (3:0,1:2,1:0) Depth: 4 Cost: 4.0 by applying (0,1,1)
State: (3:0,0:3,0:1) Depth: 3 Cost: 3.0 by applying (0,2,-1)
State: (3:0,2:1,1:0) Depth: 2 Cost: 2.0 by applying (0,1,1)
State: (3:0,1:2,0:1) Depth: 1 Cost: 1.0 by applying (0,2,-1)

Look at the Java code for aima.examples.TestSearch.java.  This test is
solving two problems. The TwoThreeState problem is to get to a state
consisting of a number that is evenly divisible by 23, starting at zero. The
two operators are to add 2 or add 3. Each search prints out the path to the
goal in reverse order. So a depth-first search achieves the goal with a path
of length 23, each time applying the add2 operator. Breadth First Search
takes only 8 steps, most of them add3. A* Search takes 11 steps.  Why more?
Because the costs of the operators is 2 for add2 and 4 for add3, so the
optimal solution perfers add2.

The other problem is missionaries and cannibals.  The state (3,3,1) means 3
missionaries, 3 cannibals and 1 boat on the left side of the river; the goal
is to get them all to the other bank. All the search techniques shown solve
the problem in 11 steps.

-Peter

----- Original Message -----
From: "dpbatagoda" <dharshana@...>
To: <aima-talk@yahoogroups.com>
Sent: Friday, August 16, 2002 9:04 PM
Subject: [aima-talk] Pls Help Me


> Dear everybody,
> I am an instructure at Sri Lanka Institute of Information Technology.
> I really apreciate if you can  explain this output from
> aima.examples.TestSearch?
>
>
>
> Trivial search space based on adding 2 or 3
> DFS:
> State: (46) Depth: 23 Cost: 46.0 by applying add2
> State: (44) Depth: 22 Cost: 44.0 by applying add2
> State: (42) Depth: 21 Cost: 42.0 by applying add2
> State: (40) Depth: 20 Cost: 40.0 by applying add2
> State: (38) Depth: 19 Cost: 38.0 by applying add2
> State: (36) Depth: 18 Cost: 36.0 by applying add2
> State: (34) Depth: 17 Cost: 34.0 by applying add2
> State: (32) Depth: 16 Cost: 32.0 by applying add2
> State: (30) Depth: 15 Cost: 30.0 by applying add2
> State: (28) Depth: 14 Cost: 28.0 by applying add2
> State: (26) Depth: 13 Cost: 26.0 by applying add2
> State: (24) Depth: 12 Cost: 24.0 by applying add2
> State: (22) Depth: 11 Cost: 22.0 by applying add2
> State: (20) Depth: 10 Cost: 20.0 by applying add2
> State: (18) Depth: 9 Cost: 18.0 by applying add2
> State: (16) Depth: 8 Cost: 16.0 by applying add2
> State: (14) Depth: 7 Cost: 14.0 by applying add2
> State: (12) Depth: 6 Cost: 12.0 by applying add2
> State: (10) Depth: 5 Cost: 10.0 by applying add2
> State: (8) Depth: 4 Cost: 8.0 by applying add2
> State: (6) Depth: 3 Cost: 6.0 by applying add2
> State: (4) Depth: 2 Cost: 4.0 by applying add2
> State: (2) Depth: 1 Cost: 2.0 by applying add2
> Starting at state: (0)
>
> Depth Bounded (depth 7):
> No solution
>
> BFS:
> State: (23) Depth: 8 Cost: 30.0 by applying add2
> State: (21) Depth: 7 Cost: 28.0 by applying add3
> State: (18) Depth: 6 Cost: 24.0 by applying add3
> State: (15) Depth: 5 Cost: 20.0 by applying add3
> State: (12) Depth: 4 Cost: 16.0 by applying add3
> State: (9) Depth: 3 Cost: 12.0 by applying add3
> State: (6) Depth: 2 Cost: 8.0 by applying add3
> State: (3) Depth: 1 Cost: 4.0 by applying add3
> Starting at state: (0)
>
>
> Uniform cost:
> State: (23) Depth: 11 Cost: 24.0 by applying add3
> State: (20) Depth: 10 Cost: 20.0 by applying add2
> State: (18) Depth: 9 Cost: 18.0 by applying add2
> State: (16) Depth: 8 Cost: 16.0 by applying add2
> State: (14) Depth: 7 Cost: 14.0 by applying add2
> State: (12) Depth: 6 Cost: 12.0 by applying add2
> State: (10) Depth: 5 Cost: 10.0 by applying add2
> State: (8) Depth: 4 Cost: 8.0 by applying add2
> State: (6) Depth: 3 Cost: 6.0 by applying add2
> State: (4) Depth: 2 Cost: 4.0 by applying add2
> State: (2) Depth: 1 Cost: 2.0 by applying add2
> Starting at state: (0)
>
> Iterated Deepening Search:
> State: (23) Depth: 8 Cost: 30.0 by applying add3
> State: (20) Depth: 7 Cost: 26.0 by applying add3
> State: (17) Depth: 6 Cost: 22.0 by applying add3
> State: (14) Depth: 5 Cost: 18.0 by applying add3
> State: (11) Depth: 4 Cost: 14.0 by applying add3
> State: (8) Depth: 3 Cost: 10.0 by applying add3
> State: (5) Depth: 2 Cost: 6.0 by applying add3
> State: (2) Depth: 1 Cost: 2.0 by applying add2
> Starting at state: (0)
>
> A* Search:
> State: (23) Depth: 11 Cost: 24.0 by applying add3
> State: (20) Depth: 10 Cost: 20.0 by applying add2
> State: (18) Depth: 9 Cost: 18.0 by applying add2
> State: (16) Depth: 8 Cost: 16.0 by applying add2
> State: (14) Depth: 7 Cost: 14.0 by applying add2
> State: (12) Depth: 6 Cost: 12.0 by applying add2
> State: (10) Depth: 5 Cost: 10.0 by applying add2
> State: (8) Depth: 4 Cost: 8.0 by applying add2
> State: (6) Depth: 3 Cost: 6.0 by applying add2
> State: (4) Depth: 2 Cost: 4.0 by applying add2
> State: (2) Depth: 1 Cost: 2.0 by applying add2
> Starting at state: (0)
>
> Greedy Search:
> State: (23) Depth: 8 Cost: 30.0 by applying add2
> State: (21) Depth: 7 Cost: 28.0 by applying add3
> State: (18) Depth: 6 Cost: 24.0 by applying add3
> State: (15) Depth: 5 Cost: 20.0 by applying add3
> State: (12) Depth: 4 Cost: 16.0 by applying add3
> State: (9) Depth: 3 Cost: 12.0 by applying add3
> State: (6) Depth: 2 Cost: 8.0 by applying add3
> State: (3) Depth: 1 Cost: 4.0 by applying add3
> Starting at state: (0)
>
> Generalized missionaries and cannibals (3,3,1)
> Breadth-first search skipping repeated states
> State: (0:3,0:3,0:1) Depth: 11 Cost: 11.0 by applying (0,2,-1)
> State: (0:3,2:1,1:0) Depth: 10 Cost: 10.0 by applying (0,1,1)
> State: (0:3,1:2,0:1) Depth: 9 Cost: 9.0 by applying (0,2,-1)
> State: (0:3,3:0,1:0) Depth: 8 Cost: 8.0 by applying (0,1,1)
> State: (0:3,2:1,0:1) Depth: 7 Cost: 7.0 by applying (2,0,-1)
> State: (2:1,2:1,1:0) Depth: 6 Cost: 6.0 by applying (1,1,1)
> State: (1:2,1:2,0:1) Depth: 5 Cost: 5.0 by applying (2,0,-1)
> State: (3:0,1:2,1:0) Depth: 4 Cost: 4.0 by applying (0,1,1)
> State: (3:0,0:3,0:1) Depth: 3 Cost: 3.0 by applying (0,2,-1)
> State: (3:0,2:1,1:0) Depth: 2 Cost: 2.0 by applying (0,1,1)
> State: (3:0,1:2,0:1) Depth: 1 Cost: 1.0 by applying (0,2,-1)
> Starting at state: (3:0,3:0,1:0)
>
> Breadth-first search with repeated states
> State: (0:3,0:3,0:1) Depth: 11 Cost: 11.0 by applying (0,2,-1)
> State: (0:3,2:1,1:0) Depth: 10 Cost: 10.0 by applying (0,1,1)
> State: (0:3,1:2,0:1) Depth: 9 Cost: 9.0 by applying (0,2,-1)
> State: (0:3,3:0,1:0) Depth: 8 Cost: 8.0 by applying (0,1,1)
> State: (0:3,2:1,0:1) Depth: 7 Cost: 7.0 by applying (2,0,-1)
> State: (2:1,2:1,1:0) Depth: 6 Cost: 6.0 by applying (1,1,1)
> State: (1:2,1:2,0:1) Depth: 5 Cost: 5.0 by applying (2,0,-1)
> State: (3:0,1:2,1:0) Depth: 4 Cost: 4.0 by applying (0,1,1)
> State: (3:0,0:3,0:1) Depth: 3 Cost: 3.0 by applying (0,2,-1)
> State: (3:0,2:1,1:0) Depth: 2 Cost: 2.0 by applying (0,1,1)
> State: (3:0,1:2,0:1) Depth: 1 Cost: 1.0 by applying (0,2,-1)
> Starting at state: (3:0,3:0,1:0)
>
> A* Search:
> State: (0:3,0:3,0:1) Depth: 11 Cost: 11.0 by applying (0,2,-1)
> State: (0:3,2:1,1:0) Depth: 10 Cost: 10.0 by applying (0,1,1)
> State: (0:3,1:2,0:1) Depth: 9 Cost: 9.0 by applying (0,2,-1)
> State: (0:3,3:0,1:0) Depth: 8 Cost: 8.0 by applying (0,1,1)
> State: (0:3,2:1,0:1) Depth: 7 Cost: 7.0 by applying (2,0,-1)
> State: (2:1,2:1,1:0) Depth: 6 Cost: 6.0 by applying (1,1,1)
> State: (1:2,1:2,0:1) Depth: 5 Cost: 5.0 by applying (2,0,-1)
> State: (3:0,1:2,1:0) Depth: 4 Cost: 4.0 by applying (0,1,1)
> State: (3:0,0:3,0:1) Depth: 3 Cost: 3.0 by applying (0,2,-1)
> State: (3:0,2:1,1:0) Depth: 2 Cost: 2.0 by applying (0,1,1)
> State: (3:0,1:2,0:1) Depth: 1 Cost: 1.0 by applying (0,2,-1)
> Starting at state: (3:0,3:0,1:0)
>
> Greedy Search:
> State: (0:3,0:3,0:1) Depth: 11 Cost: 11.0 by applying (0,2,-1)
> State: (0:3,2:1,1:0) Depth: 10 Cost: 10.0 by applying (0,1,1)
> State: (0:3,1:2,0:1) Depth: 9 Cost: 9.0 by applying (0,2,-1)
> State: (0:3,3:0,1:0) Depth: 8 Cost: 8.0 by applying (0,1,1)
> State: (0:3,2:1,0:1) Depth: 7 Cost: 7.0 by applying (2,0,-1)
> State: (2:1,2:1,1:0) Depth: 6 Cost: 6.0 by applying (1,1,1)
> State: (1:2,1:2,0:1) Depth: 5 Cost: 5.0 by applying (2,0,-1)
> State: (3:0,1:2,1:0) Depth: 4 Cost: 4.0 by applying (0,1,1)
> State: (3:0,0:3,0:1) Depth: 3 Cost: 3.0 by applying (0,2,-1)
> State: (3:0,2:1,1:0) Depth: 2 Cost: 2.0 by applying (0,1,1)
> State: (3:0,1:2,0:1) Depth: 1 Cost: 1.0 by applying (0,2,-1)
>
>
>
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Can the following tasks be solved by computers:
a)Playing a descent game of bridge at a competitive level.
b)Discovering and proving new mathematical theorems.
c)Giving competent legal advice in a specialized area of law

Dear all:

I just bought this book (AIMA) and now I'm reading it. I would like to
know there is any "exercise resolution in each chapter" available here or
where can I get these resolution copies?

Please let me know if anyone has a clue. Thank you very much.

Wijarn

Wijarn,

Peter responded to a similar message before (#43) in the archives.

Best regards,
Brandon

--- In aima-talk@y..., "wijarn13" <wijarn13@y...> wrote:
> Dear all:
>
> I just bought this book (AIMA) and now I'm reading it. I would like
to
> know there is any "exercise resolution in each chapter" available
here or
> where can I get these resolution copies?
>
> Please let me know if anyone has a clue. Thank you very much.
>
> Wijarn

Hi,
   I'm supposed to think of some problem domains that i can apply the
techniques of uninformed and informed searches. Can anyone help in
this area? thanks!

Gavin

Hello

Could you give me any information about System Expert? (links, books...)

how does it work?

do u have any example to learn more about s.e.?

do i need any "special software" like clisp, prolog?

Thanks a lot

Hello

Could you give me any information about System Expert? (links, books...)

how does it work?

do u have any example to learn more about s.e.?

do i need any "special software" like clisp, prolog?

Thanks a lot

---

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I'm sorry that no French translation is planned. However, the new second
edition will be out soon, and if anyone would like to do a French
translation, let us know.

-Peter Norvig

----- Original Message -----
From: "serge_stinckwich" <sergio36@...>
To: <aima-talk@yahoogroups.com>
Sent: Sunday, July 28, 2002 1:38 PM
Subject: [aima-talk] AIMA french translation ?


Hi,

do you know if any french translation of AIMA is planned ?

It's a shame that such a good book is not available in french ...

Cheers,

-- Serge Stinckwich

hello
I copied this example of a book about CLIPS
This program has "blocks" and the objective is to put the block C over the block E
Does anybody know when it stop? The objective is (objetivo (poner C)(encima-de E)) but i don't
understand when it stop.
Could you help me, please?

Thanks a lot

it's the program:

//start
(deftemplate objetivo(slot poner)(slot encima-de))
(deffacts estado-inicial
   (pila A B C)
   (pila D E F)
   (objetivo (poner C)(encima-de E))
   (pila)
)

(defrule mover-directamente
   ?objetivo <-(objetivo (poner ?bloque1)(encima-de ?bloque2))
   ?pila-1 <- (pila ?bloque1 $?resto1)
   ?pila-2 <- (pila ?bloque2 $?resto2)
   =>
   (retract ?objetivo ?pila-1 ?pila-2)
   (assert (pila $?resto1))
   (assert (pila ?bloque1 ?bloque2 $?resto2))
   (printout t ?bloque1 " puesto encima de " ?bloque2 "." crlf)
)

(defrule poner-en-el-piso
   ?objetivo <- (objetivo (poner ?bloque1) (encima-de piso))
   ?pila-1 <-(pila ?bloque1 $?resto)
   =>
   (retract ?objetivo ?pila-1)
   (assert (pila ?bloque1))
   (assert (pila $?resto))
   (printout t ?bloque1 " puesto en el piso " crlf)
)

(defrule despejar-bloque-superior
   (objetivo (poner ?bloque1))
   (pila ?arriba $? ?bloque1 $?)
   =>
   (assert (objetivo (poner ?arriba)(encima-de piso)))
)

(defrule despejar-bloque-inferior
   (objetivo (encima-de ?bloque1))
   (pila ?arriba $? ?bloque1 $?)
   =>
   (assert (objetivo (poner ?arriba)(encima-de piso)))
)
//end

In the chapter on uninformed search algorithms (I think it's chap. 3,
but I don't have the book in front of me), there is a diagram showing
the search pattern for a bidirectional search.

Essentially, a breadth-first search from both the start and end nodes
is illustrated, which looks like a circular tree-like pattern
extending outward from both nodes.

The question is asked in the book whether this pattern is the best one
for a bidirectional search. I believe that breadth-first search would
be necessary for both nodes because you essentially could "pass by"
the tree generated for one of the nodes otherwise. It looks to me as
though this would only be true for uninformed searches though.

Am I right? Are breadth-first algorithms the best kind to use for
uninformed bidirectional search?

Thanks,
Brandon

----- Original Message -----
From: "Alberto Vera" <artificialintelligenceperu@...>
Sent: September 10, 2002 11:37 AM

> hello
> I copied this example of a book about CLIPS

Which book exactly, if you don't mind me asking?

> This program has "blocks" and the objective is to put the block C over the
block E
> Does anybody know when it stop?

When the objective is met. ;)

> The objective is (objetivo (poner C)(encima-de E)) but i don't
> understand when it stop.

Did you try to run it? :)
It stops when the facts base doesn't have facts that cause any of
the rules to fire anymore, i.e. there are no more matched rules
on the agenda. (If you don't know what I'm talking about, ask)

In your case when the objective is gone, it stops.

Here is the sample printout of with options (watch rules) and
(watch facts) set. An ==> means a fact is added to a facts base,
and an <== means, the fact is retracted from there. The rules
are placed onto agenda when their left-hand-side (LHS) matches the facts in
the facts base, the order they fire in (in case several rules
matched at a time) is determined by rules' priorities and/or a
conflict-resolution policy.

CLIPS> (watch facts)
CLIPS> (watch rules)
CLIPS> (load blocks.clp)
Defining deftemplate: objetivo
Defining deffacts: estado-inicial
Defining defrule: mover-directamente +j+j+j
Defining defrule: poner-en-el-piso +j+j
Defining defrule: despejar-bloque-superior =j+j
Defining defrule: despejar-bloque-inferior =j+j
TRUE
CLIPS> (reset)
==> f-0     (initial-fact)
==> f-1     (pila A B C)
==> f-2     (pila D E F)
==> f-3     (objetivo (poner C) (encima-de E))
==> f-4     (pila)
CLIPS> (run)
FIRE    1 despejar-bloque-superior: f-3,f-1
==> f-5     (objetivo (poner A) (encima-de piso))
FIRE    2 poner-en-el-piso: f-5,f-1
<== f-5     (objetivo (poner A) (encima-de piso))
<== f-1     (pila A B C)
==> f-6     (pila A)
==> f-7     (pila B C)
A puesto en el piso
FIRE    3 despejar-bloque-superior: f-3,f-7
==> f-8     (objetivo (poner B) (encima-de piso))
FIRE    4 poner-en-el-piso: f-8,f-7
<== f-8     (objetivo (poner B) (encima-de piso))
<== f-7     (pila B C)
==> f-9     (pila B)
==> f-10    (pila C)
B puesto en el piso
FIRE    5 despejar-bloque-inferior: f-3,f-2
==> f-11    (objetivo (poner D) (encima-de piso))
FIRE    6 poner-en-el-piso: f-11,f-2
<== f-11    (objetivo (poner D) (encima-de piso))
<== f-2     (pila D E F)
==> f-12    (pila D)
==> f-13    (pila E F)
D puesto en el piso
FIRE    7 mover-directamente: f-3,f-10,f-13
<== f-3     (objetivo (poner C) (encima-de E))
<== f-10    (pila C)
<== f-13    (pila E F)
==> f-14    (pila C E F)
C puesto encima de E.
CLIPS> (facts)
f-0     (initial-fact)
f-4     (pila)
f-6     (pila A)
f-9     (pila B)
f-12    (pila D)
f-14    (pila C E F)
For a total of 6 facts.
CLIPS>

Hi Gavin,

----- Original Message -----
From: "gavtks78" <gavint@...>
Sent: August 22, 2002 8:48 PM

> Hi,
>   I'm supposed to think of some problem domains that i can apply the
     ^^^^^^^^^^^^^^^^^^^^^
> techniques of uninformed and informed searches. Can anyone help in
> this area? thanks!

*You* are supposed to think, not us ;-) You could also search google or
somthing on the subj. When you think, also consider and compare
performance and complexity of implementation of each search technique for a
specific domain.

-s

Thank you very much for your help.

The book's title in Spanish is: Sistemas Expertos. Principios y Programacion

I hope this title in English might be: Expert Systems. Principles and Programming

Author: Joseph Giarratano and Gary Riley

Third Edition

 it was published in English by PWS Publishing Company and its ISBN: 0-534-95053-1

Best Regards

  Serguei Mokhov <mokhov@...> escribió:

----- Original Message -----
From: "Alberto Vera" <artificialintelligenceperu@...>
Sent: September 10, 2002 11:37 AM

> hello
> I copied this example of a book about CLIPS

Which book exactly, if you don't mind me asking?

Hello

I made this rule:

(defrule warning
   ?suma <- (numerrores ?error)
   (> ?error 1)
   =>
   (retract ?suma)  
   (printout t ?error " WARNING " ?error crlf)
)

I want to execute this if ?error is > 1, but i don't see any message.

What is wrong in my rule?

Can you help me?

Thanks

Hi,

I would be much interested in translating the new second edition.
I own the first version in English and I think it would be great to
make it more available to French people.

I'm not an AI expert neither a translation professional but I have
plenty of free time. Does it matter? Would you have requirements?

Please keep in touch and tell me how we could collaborate.

François (Paris, France)

--- In aima-talk@y..., "Peter Norvig" <peter@n...> wrote:
> I'm sorry that no French translation is planned. However, the new
second
> edition will be out soon, and if anyone would like to do a French
> translation, let us know.
>
> -Peter Norvig
>
> ----- Original Message -----
> From: "serge_stinckwich" <sergio36@c...>
> To: <aima-talk@y...>
> Sent: Sunday, July 28, 2002 1:38 PM
> Subject: [aima-talk] AIMA french translation ?
>
>
> Hi,
>
> do you know if any french translation of AIMA is planned ?
>
> It's a shame that such a good book is not available in french ...
>
> Cheers,
>
> -- Serge Stinckwich

----- Original Message -----
From: "Alberto Vera" <artificialintelligenceperu@...>
Sent: September 11, 2002 3:34 PM

> (defrule warning
>    ?suma <- (numerrores ?error)
>    (> ?error 1)
>    =>
>    (retract ?suma)
>    (printout t ?error " WARNING " ?error crlf)
> )
>
> I want to execute this if ?error is > 1, but i don't see any message.
>
> What is wrong in my rule?

Personally, I don't see anything wrong with your rule with exception of one
little thing,
but you haven't provided much of a context you are trying
to test your rule in anyway. For it to fire, you have to have
a fact (numerrores N) where N>1 in your facts base,
and change (> ?error 1) to (test (> ?error 1)) because
(> ?error 1) isn't really a fact and you are tyring to match it
as if it were one.

hth,
-s

Would using MIT Scheme 7.5 for W98 compile the  source code from the
text by changing the file extensions from .lisp to .scm?

Thanks.

wein3150 writes:
  > Would using MIT Scheme 7.5 for W98 compile the  source code from the
  > text by changing the file extensions from .lisp to .scm?
  >
  > Thanks.

scheme is not lisp, so I don't think so... But why don't you just try
it and see for yourself???

Joachim.

----- Original Message -----
From: <joachim@...>
Sent: September 20, 2002 3:37 AM

> wein3150 writes:
>  > Would using MIT Scheme 7.5 for W98 compile the  source code from the
>  > text by changing the file extensions from .lisp to .scm?
>  >
>  > Thanks.
>
> scheme is not lisp, so I don't think so...

Not exactly LISP, but a subset.

> But why don't you just try
> it and see for yourself???

I agree here :)

-s

--- In aima-talk@y..., "Serguei Mokhov" <mokhov@c...> wrote:
> ----- Original Message -----
> From: <joachim@a...>
> Sent: September 20, 2002 3:37 AM
>
> > wein3150 writes:
> >  > Would using MIT Scheme 7.5 for W98 compile the  source code
from the
> >  > text by changing the file extensions from .lisp to .scm?
> >  >
> >  > Thanks.
> >
> > scheme is not lisp, so I don't think so...
>
> Not exactly LISP, but a subset.
>
> > But why don't you just try
> > it and see for yourself???
>
> I agree here :)
>
> -s

Okay, so I put the lisp converted files into the same directory as
the compiler.  Now, command-wise running this stuff,
what,

(load "filename.scm")?

--- wein3150 a scritto:
> Hi,
> Can anyone tell me if MIT Scheme 7.5 for W98 should
> work okay with the lisp source code form the text?
No. Although similar, common lisp and scheme are
sufficiently different such that the code in the book
will not -- in general -- work with scheme.

Two approaches now seem obvious (to me at least),
either port the code to scheme or obtain a common lisp
compiler. Dependant on your personal bias, skills and
experience one should now be apparent.

If it were *me* I would start out by looking at one of
free common lisp compilers which, I *think*, run under
W98. For example, the excellent clisp or corman lisp
spring to mind. Or trial editions of the excellent
xanalys lispworks or franz allegro common lisp.

Hope this helps,

:)w

______________________________________________________________________
Mio Yahoo!: personalizza Yahoo! come piace a te
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>Serguei Mokhov ha scritto:
> Not exactly LISP, but a subset.
Without meaning to be painfully pedantic, if you mean
by LISP (or lisp since this looks a bit quieter on the
page :) that you are talking about common lisp --
which I belive the examples to be implemented in --
then, no, scheme is not "a subset" of lisp. There are
elements in the scheme standare that are not -- or can
not -- be implemented in common lisp.

If however, you mean by *a* "LISP," that scheme can
trace ancedance from the early work by McCarthy and
others, then yes, scheme is a lisp. However, rather
like the dolphin and the blue whale, they are somewhat
different in form and function. Hope this helps.

Cetaceanly yours,

:)w

______________________________________________________________________
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----- Original Message -----

From: Will Deakin

To: aima-talk@yahoogroups.com

Sent: Friday, September 20, 2002 9:33 PM

Subject: Re: [aima-talk] Compiler for Source

--- wein3150 a scritto:
> Hi,
> Can anyone tell me if MIT Scheme 7.5 for W98 should
> work okay with the lisp source code form the text?
No. Although similar, common lisp and scheme are
sufficiently different such that the code in the book
will not -- in general -- work with scheme.

Two approaches now seem obvious (to me at least),
either port the code to scheme or obtain a common lisp
compiler. Dependant on your personal bias, skills and
experience one should now be apparent.

If it were *me* I would start out by looking at one of
free common lisp compilers which, I *think*, run under
W98. For example, the excellent clisp or corman lisp
spring to mind. Or trial editions of the excellent
xanalys lispworks or franz allegro common lisp.

Hope this helps,

:)w

______________________________________________________________________
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http://it.yahoo.com/mail_it/foot/?http://it.my.yahoo.com/


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----- Original Message -----
From: "Will Deakin" <anisotropia9@...>
Sent: September 20, 2002 5:44 PM

> >Serguei Mokhov ha scritto:
> > Not exactly LISP, but a subset.
> Without meaning to be painfully pedantic, if you mean
> by LISP (or lisp since this looks a bit quieter on the
> page :) that you are talking about common lisp --

I wasn't talking about any particular flavour of lisp.

> which I belive the examples to be implemented in --
> then, no, scheme is not "a subset" of lisp. There are

Okay, not a *proper* subset :)

> If however, you mean by *a* "LISP," that scheme can
> trace ancedance from the early work by McCarthy and

Yes.

-s

My understanding is that this goup if for discussions
directly pertaining to 'Artificial Intelligence: A
modern Approach' by Stuart Russell and Peter Norvig.

Might this dicussion be better suited to
comp.land.lisp?


--- Serguei Mokhov <mokhov@...> wrote:
> ----- Original Message -----
> From: "Will Deakin" <anisotropia9@...>
> Sent: September 20, 2002 5:44 PM
>
> > >Serguei Mokhov ha scritto:
> > > Not exactly LISP, but a subset.
> > Without meaning to be painfully pedantic, if you
> mean
> > by LISP (or lisp since this looks a bit quieter on
> the
> > page :) that you are talking about common lisp --
>
> I wasn't talking about any particular flavour of
> lisp.
>
> > which I belive the examples to be implemented in
> --
> > then, no, scheme is not "a subset" of lisp. There
> are
>
> Okay, not a *proper* subset :)
>
> > If however, you mean by *a* "LISP," that scheme
> can
> > trace ancedance from the early work by McCarthy
> and
>
> Yes.
>
> -s
>
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=====
Quamvis tam longo possis satur esse libello,
     lector, adhuc a me disitcha pauca petis.
sed Lupus usuram puerique diaria poscunt.
     Lector, solve. Taces dissimulasque? Vale.

    --Marcus Valerius Martialis, liber XI, CVIII

__________________________________________________
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--- Owain Roberts <owain_roberts@...> ha
scritto:
> My understanding is that this goup if for
> discussions directly pertaining to 'Artificial
> Intelligence: A modern Approach' by Stuart Russell
and
> Peter Norvig.
Yes. The answer I gave was in response to a thread as
to whether the aima code would run under scheme. I was
trying to explain why this was not so and that
there were a number of practical steps that could be
carried out to run the examples.

For completeness, I should have added that IIRC the
examples are also implemented in a number of other
languages. This would suggest a third approach of not
using lisp but java, say. However, I don't know enough
about this to be of any futher assistance.

Why is this not "on topic?" Unless, of course, the
first question was not.

> Might this dicussion be better suited to
> comp.land.lisp?
Further dicussion (sic) would. Or any one of a number
of other places.

Best Regards,

Will



______________________________________________________________________
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I was just curious if anyone knew what the heuristics generated by
the ABSOLVER program, mentioned on page 103, were or a web site that
explains them.

It made me smile when I read that it had been able to generate them
but then they weren't listed :O

-shane

----- Original Message -----
From: "flamesplash" <flamesplash@...>
Sent: September 22, 2002 11:01 PM

> I was just curious if anyone knew what the heuristics generated by
> the ABSOLVER program, mentioned on page 103, were or a web site that
> explains them.

www.google.com

Keyword: ABSOLVER

That document might be of interest:

<http://www.cs.ualberta.ca/~holte/CMPUT651/unpublished.ps>

-s