/*
 * NARS-Examples-MultiSteps.txt  
 * Pei Wang [All Rights Reserved]
 * last modified: August 15, 2006  
 *
 * Examples showing conclusions of multiple-step inference in the Java applet.
 *
 * Each example consists of 
 *   (1) input tasks and inference steps, 
 *   (2) the communication log displayed in the Main Window, 
 *   (3) a brief explanation,
 * separated by "====================".
 *   
 * To run an example, copy the input tasks and inference steps and paste them
 * into the Input Window of NARS, then click "OK" in the Input Window.
 *
 * To reset the memory between examples, selecting menu item "Memory/Initialize".
 */


// Context Sensitivity 

Paris {-- city %1%
100
Boston {-- city %1%
1
?x {-- city 
100
Paris {-- city 
1 
?x {-- city 
100
====================
0
  >>>  <{Paris} --> city> %1.00;0.90%
100
  >>>  <{Boston} --> city> %1.00;0.90%
1
  >>>  <{?1} --> city> %1.00;0.00%
4
     <<<  <{Boston} --> city> %1.00;0.90%
96
  >>>  <{Paris} --> city> %1.00;0.00%
1
     <<<  <{Paris} --> city> %1.00;0.90%
0
  >>>  <{?1} --> city> %1.00;0.00%
19
     <<<  <{Paris} --> city> %1.00;0.90%
====================
When a question ("?x {-- city") has equally good answers ("<{Boston} --> city> 
%1.00;0.90%" and "<{Paris} --> city> %1.00;0.90%"), the reported answer is
usually provided by the relatively active concept in the current context.



// Contradiction

coffee --> beverage %1%
Java --> coffee %1% 
Java --> coffee %0% 
10 
Java --> coffee 
coffee --> beverage 
10
====================
0
  >>>  <coffee --> beverage> %1.00;0.90%
0
  >>>  <Java --> coffee> %1.00;0.90%
0
  >>>  <Java --> coffee> %0.00;0.90%
10
  >>>  <Java --> coffee> %1.00;0.00%
0
  >>>  <coffee --> beverage> %1.00;0.00%
1
     <<<  <Java --> coffee> %0.50;0.94%
0
     <<<  <coffee --> beverage> %1.00;0.90%  
====================
A contradiction makes the system unsure on directly related questions, but will
not make the system to derive an arbitrary conclusion, as in propositional logic.



// Deduction Chain

Tweety {-- robin %1%
robin --> bird %1%
bird --> animal %1%
10 
Tweety {-- bird 
Tweety {-- animal 
10
====================
0
  >>>  <{Tweety} --> robin> %1.00;0.90%
0
  >>>  <robin --> bird> %1.00;0.90%
0
  >>>  <bird --> animal> %1.00;0.90%
10
  >>>  <{Tweety} --> bird> %1.00;0.00%
0
  >>>  <{Tweety} --> animal> %1.00;0.00%
1
     <<<  <{Tweety} --> bird> %1.00;0.81%
1
     <<<  <{Tweety} --> animal> %1.00;0.72%
====================
Though the frequency of the conclusions remains to be 1, the confidence 
is getting lower as the deduction chain is longer.



// Similarity Chain

dog <-> cat %0.9%
cat <-> tiger %0.9%
tiger <-> lion %0.9%
dog <-> lion
25
====================
0
  >>>  <cat <-> dog> %0.90;0.90%
0
  >>>  <cat <-> tiger> %0.90;0.90%
0
  >>>  <lion <-> tiger> %0.90;0.90%
0
  >>>  <dog <-> lion> %1.00;0.00%
23
     <<<  <dog <-> lion> %0.72;0.70% 
====================
For incomplete similarity, both frequency and the confidence decrease 
alone an inference chain.



// Induction and Revision

bird --> swimmer 
swimmer --> bird 
1
swan --> bird %1%
swan --> swimmer %1%
20 
gull --> bird %1%
gull --> swimmer %1%
20
crow --> bird %1%
crow --> swimmer %0%
20
====================
0
  >>>  <bird --> swimmer> %1.00;0.00%
0
  >>>  <swimmer --> bird> %1.00;0.00%
1
  >>>  <swan --> bird> %1.00;0.90%
0
  >>>  <swan --> swimmer> %1.00;0.90%
11
     <<<  <swimmer --> bird> %1.00;0.44%
0
     <<<  <bird --> swimmer> %1.00;0.44%
9
  >>>  <gull --> bird> %1.00;0.90%
0
  >>>  <gull --> swimmer> %1.00;0.90%
10
     <<<  <swimmer --> bird> %1.00;0.61%
0
     <<<  <bird --> swimmer> %1.00;0.61%
10
  >>>  <crow --> bird> %1.00;0.90%
0
  >>>  <crow --> swimmer> %0.00;0.90%
9
     <<<  <bird --> swimmer> %0.66;0.70%
====================
(1) Question may be remembered before available knowledge arrives, or after answers
are reported;
(2) The system can change its mind when new evidence is taken into consideration;
(3) Positive evidence has the same effect on symmetric inductive conclusions, but
negative evidence does not.



// Mixed Inference 

swan --> bird %1%
swan --> swimmer %1%
10 
gull --> bird %1%
gull --> swimmer %1%
20
crow --> bird %1%
crow --> swimmer %0%
40
robin --] feathered %1% 
bird --] feathered %1%
80 
robin --> swimmer 
100 
====================
0
  >>>  <swan --> bird> %1.00;0.90%
0
  >>>  <swan --> swimmer> %1.00;0.90%
10
  >>>  <gull --> bird> %1.00;0.90%
0
  >>>  <gull --> swimmer> %1.00;0.90%
20
  >>>  <crow --> bird> %1.00;0.90%
0
  >>>  <crow --> swimmer> %0.00;0.90%
40
  >>>  <robin --> [feathered]> %1.00;0.90%
0
  >>>  <bird --> [feathered]> %1.00;0.90%
80
  >>>  <robin --> swimmer> %1.00;0.00%
83
     <<<  <robin --> swimmer> %0.66;0.24% 
====================
The final conclusion is produced using induction, abduction, deduction, and revision.
The selection of inference rule is knowledge driven, not specified explicitly in the
input.



// Confidence and Revision 

Willy {-- whale %1%
whale --] black %1%
10 
Willy {-- swimmer %1%
fish --> swimmer %1%
10 
Willy {-] black
Willy {-- fish 
10 
Willy {-] black %0% 
Willy {-- fish %0% 
10
====================
0
  >>>  <{Willy} --> whale> %1.00;0.90%
0
  >>>  <whale --> [black]> %1.00;0.90%
10
  >>>  <{Willy} --> swimmer> %1.00;0.90%
0
  >>>  <fish --> swimmer> %1.00;0.90%
10
  >>>  <{Willy} --> [black]> %1.00;0.00%
0
  >>>  <{Willy} --> fish> %1.00;0.00%
1
     <<<  <{Willy} --> [black]> %1.00;0.81%
1
     <<<  <{Willy} --> fish> %1.00;0.44%
8
  >>>  <{Willy} --> [black]> %0.00;0.90%
0
  >>>  <{Willy} --> fish> %0.00;0.90%
1
     <<<  <{Willy} --> [black]> %0.00;0.90%
0
     <<<  <{Willy} --> fish> %0.00;0.90%
1
     <<<  <{Willy} --> [black]> %0.32;0.92%
0
     <<<  <{Willy} --> fish> %0.08;0.90% 
====================
Even when all the input judgments using the default confidence value, different rules
produce conclusions with difference confidence, which have different sensitivity when
facing the same amount of new evidence.



// Compositionality

light --> traffic_signal %0.2% 
[red] --> traffic_signal %0.2% 
10
(&, [red], light) --> traffic_signal
10 
light_1 {-- (&, [red], light) %1%
light_1 {-- traffic_signal %1%
10
light_2 {-- (&, [red], light) %1%
light_2 {-- traffic_signal %1%
20
=========
0
  >>>  <light --> traffic_signal> %0.20;0.90%
0
  >>>  <[red] --> traffic_signal> %0.20;0.90%
10
  >>>  <(&,[red],light) --> traffic_signal> %1.00;0.00%
1
     <<<  <(&,[red],light) --> traffic_signal> %0.36;0.84%
9
  >>>  <{light_1} --> (&,[red],light)> %1.00;0.90%
0
  >>>  <{light_1} --> traffic_signal> %1.00;0.90%
10
  >>>  <{light_2} --> (&,[red],light)> %1.00;0.90%
0
  >>>  <{light_2} --> traffic_signal> %1.00;0.90%
9
     <<<  <(&,[red],light) --> traffic_signal> %0.44;0.86%
10
     <<<  <(&,[red],light) --> traffic_signal> %0.50;0.87%   
====================
Initially, the meaning of compound term "(&,[red],light)" is determined by the
meaning of its components "red" and "light", but it will no longer be the case
when the system gets experience about the compound that cannot be reduced to its
components.



// Relational Induction

(*, vinegar, baking_soda) --> neutralization %1%
vinegar --> acid %1%
baking_soda --> base %1%
50
(*, acid, base) --> neutralization 
100
====================
0
  >>>  <(*,vinegar,baking_soda) --> neutralization> %1.00;0.90%
0
  >>>  <vinegar --> acid> %1.00;0.90%
0
  >>>  <baking_soda --> base> %1.00;0.90%
50
  >>>  <(*,acid,base) --> neutralization> %1.00;0.00%
73
     <<<  <(*,acid,base) --> neutralization> %1.00;0.28% 
====================
Double induction on both arguments of the "neutralization" relation



// Fuzzy Concept 

John {-- boy %1%
John {-- (/, taller_than, {Tom}, _) %1%
10
David {-- boy %1%
David {-- (/, taller_than, {Tom}, _) %1%
20
Karl {-- boy %1%
Karl {-- (/, taller_than, {Tom}, _) %0%
40
Tom {-- (/, taller_than, _, boy)
100
====================
0
  >>>  <{John} --> boy> %1.00;0.90%
0
  >>>  <{John} --> (/,taller_than,{Tom},_)> %1.00;0.90%
10
  >>>  <{David} --> boy> %1.00;0.90%
0
  >>>  <{David} --> (/,taller_than,{Tom},_)> %1.00;0.90%
20
  >>>  <{Karl} --> boy> %1.00;0.90%
0
  >>>  <{Karl} --> (/,taller_than,{Tom},_)> %0.00;0.90%
40
  >>>  <{Tom} --> (/,taller_than,_,boy)> %1.00;0.00%
91
     <<<  <{Tom} --> (/,taller_than,_,boy)> %0.66;0.70%
====================
John's degree of membership to fuzzy concept "tall boy" depends on the extent 
to which he is taller than the other boys.