Good afternoon
The wizard is entering the intentions with less than 0.3 percent chance of coincidence, I need a minimum of 0.7 percent or the intent of anything_else.
Where can I configure that?
Greetings
You can check intents or anything else using the expression I have provided below. At "intentName" you can replace with the intent you are checking.
anything_else || (intents.size() > 0 && intents[0] == 'intentName' && intents[0].confidence > 0.7)
I tried to implement what you recommended, but now it almost always goes into this node, no matter I put it at 0.3 percent confidence :(
Related
please help :-)
I use syntax intents.confidence > 0.95 for answer first. And quest is:
If confidence is NOT greater than 0.95, I need use second intent.
It!s possible please?
Thanks for help.
V.
You could use something like
intents[0].confidence < 0.95 and intents[1].confidence > 0.5
I am creating a very naive AI (it maybe shouldn't even be called an AI, as it just tests out a lot of possibilites and picks the best one for him), for a board game I am making. This is to simplify the amount of manual tests I will need to do to balance the game.
The AI is playing alone, doing the following things: in each turn, the AI, playing with one of the heroes, attacks one of the max 9 monsters on the battlefield. His goal is to finish the battle as fast as possible (in the least amount of turns) and with the fewest amount of monster activations.
To achieve this, I've implemented a think ahead algorithm for the AI, where instead of performing the best possible move at the moment, he selects a move, based on the possible outcome of future moves of other heroes. This is the code snippet where he does this, it is written in PHP:
/** Perform think ahead moves
*
* #params int $thinkAheadLeft (the number of think ahead moves left)
* #params int $innerIterator (the iterator for the move)
* #params array $performedMoves (the moves performed so far)
* #param Battlefield $originalBattlefield (the previous state of the Battlefield)
*/
public function performThinkAheadMoves($thinkAheadLeft, $innerIterator, $performedMoves, $originalBattlefield, $tabs) {
if ($thinkAheadLeft == 0) return $this->quantify($originalBattlefield);
$nextThinkAhead = $thinkAheadLeft-1;
$moves = $this->getPossibleHeroMoves($innerIterator, $performedMoves);
$Hero = $this->getHero($innerIterator);
$innerIterator++;
$nextInnerIterator = $innerIterator;
foreach ($moves as $moveid => $move) {
$performedUpFar = $performedMoves;
$performedUpFar[] = $move;
$attack = $Hero->getAttack($move['attackid']);
$monsters = array();
foreach ($move['targets'] as $monsterid) $monsters[] = $originalBattlefield->getMonster($monsterid)->getName();
if (self::$debug) echo $tabs . "Testing sub move of " . $Hero->Name. ": $moveid of " . count($moves) . " (Think Ahead: $thinkAheadLeft | InnerIterator: $innerIterator)\n";
$moves[$moveid]['battlefield']['after']->performMove($move);
if (!$moves[$moveid]['battlefield']['after']->isBattleFinished()) {
if ($innerIterator == count($this->Heroes)) {
$moves[$moveid]['battlefield']['after']->performCleanup();
$nextInnerIterator = 0;
}
$moves[$moveid]['quantify'] = $moves[$moveid]['battlefield']['after']->performThinkAheadMoves($nextThinkAhead, $nextInnerIterator, $performedUpFar, $originalBattlefield, $tabs."\t", $numberOfCombinations);
} else $moves[$moveid]['quantify'] = $moves[$moveid]['battlefield']['after']->quantify($originalBattlefield);
}
usort($moves, function($a, $b) {
if ($a['quantify'] === $b['quantify']) return 0;
else return ($a['quantify'] > $b['quantify']) ? -1 : 1;
});
return $moves[0]['quantify'];
}
What this does is that it recursively checks future moves, until the $thinkAheadleft value is reached, OR until a solution was found (ie, all monsters were defeated). When it reaches it's exit parameter, it calculates the state of the battlefield, compared to the $originalBattlefield (the battlefield state before the first move). The calculation is made in the following way:
/** Quantify the current state of the battlefield
*
* #param Battlefield $originalBattlefield (the original battlefield)
*
* returns int (returns an integer with the battlefield quantification)
*/
public function quantify(Battlefield $originalBattlefield) {
$points = 0;
foreach ($originalBattlefield->Monsters as $originalMonsterId => $OriginalMonster) {
$CurrentMonster = $this->getMonster($originalMonsterId);
$monsterActivated = $CurrentMonster->getActivations() - $OriginalMonster->getActivations();
$points+=$monsterActivated*($this->quantifications['activations'] + $this->quantifications['activationsPenalty']);
if ($CurrentMonster->isDead()) $points+=$this->quantifications['monsterKilled']*$CurrentMonster->Priority;
else {
$enragePenalty = floor($this->quantifications['activations'] * (($CurrentMonster->Enrage['max'] - $CurrentMonster->Enrage['left'])/$CurrentMonster->Enrage['max']));
$points+=($OriginalMonster->Health['left'] - $CurrentMonster->Health['left']) * $this->quantifications['health'];
$points+=(($CurrentMonster->Enrage['max'] - $CurrentMonster->Enrage['left']))*$enragePenalty;
}
}
return $points;
}
When quantifying some things net positive points, some net negative points to the state. What the AI is doing, is, that instead of using the points calculated after his current move to decide which move to take, he uses the points calculated after the think ahead portion, and selecting a move based on the possible moves of the other heroes.
Basically, what the AI is doing, is saying that it isn't the best option at the moment, to attack Monster 1, but IF the other heroes will do this-and-this actions, in the long run, this will be the best outcome.
After selecting a move, the AI performs a single move with the hero, and then repeats the process for the next hero, calculating with +1 moves.
ISSUE: My issue is, that I was presuming, that an AI, that 'thinks ahead' 3-4 moves, should find a better solution than an AI that only performs the best possible move at the moment. But my test cases show differently, in some cases, an AI, that is not using the think ahead option, ie only plays the best possible move at the moment, beats an AI that is thinking ahead 1 single move. Sometimes, the AI that thinks ahead only 3 moves, beats an AI that thinks ahead 4 or 5 moves. Why is this happening? Is my presumption incorrect? If so, why is that? Am I using wrong numbers for weights? I was investigating this, and run a test, to automatically calculate the weights to use, with testing an interval of possible weights, and trying to use the best outcome (ie, the ones, which yield the least number of turns and/or the least number of activations), yet the problem I've described above, still persists with those weights also.
I am limited to a 5 move think ahead with the current version of my script, as with any larger think ahead number, the script gets REALLY slow (with 5 think ahead, it finds a solution in roughly 4 minutes, but with 6 think ahead, it didn't even find the first possible move in 6 hours)
HOW THE FIGHT WORKS: The fight works in the following way: a number of heroes (2-4) controlled by the AI, each having a number of different attacks (1-x), which can be used once or multiple times in a combat, are attacking a number of monsters (1-9). Based on the values of the attack, the monsters lose health, until they die. After each attack, the attacked monster gets enraged if he didn't die, and after each heroes performed a move, all monsters get enraged. When the monsters reach their enrage limit, they activate.
DISCLAIMER: I know that PHP is not the language to use for this kind of operation, but as this is only an in-house project, I've preferred to sacrifice speed, to be able to code this as fast as possible, in my native programming language.
UPDATE: The quantifications that we currently use look something like this:
$Battlefield->setQuantification(array(
'health' => 16,
'monsterKilled' => 86,
'activations' => -46,
'activationsPenalty' => -10
));
If there is randomness in your game, then anything can happen. Pointing that out since it's just not clear from the materials you have posted here.
If there is no randomness and the actors can see the full state of the game, then a longer look-ahead absolutely should perform better. When it does not, it is a clear indication that your evaluation function is providing incorrect estimates of the value of a state.
In looking at your code, the values of your quantifications are not listed and in your simulation it looks like you just have the same player make moves repeatedly without considering the possible actions of the other actors. You need to run a full simulation, step by step in order to produce accurate future states and you need to look at the value estimates of the varying states to see if you agree with them, and make adjustments to your quantifications accordingly.
An alternative way to frame the problem of estimating value is to explicitly predict your chances of winning the round as a percentage on a scale of 0.0 to 1.0 and then choose the move that gives you the highest chance of winning. Calculating the damage done and number of monsters killed so far doesn't tell you much about how much you have left to do in order to win the game.
I am writing an application in X-code. It is gathering the sensor data (gyroscope) and then transforming it throw FFTW. At the end I am getting the result in an array. In the app. I am plotting the graph but there is so much peaks (see the graph in red) and i would like to smooth it.
My array:
double magnitude[S];
...
magnitude[i]=sqrt((fft_result[i][0])*(fft_result[i][0])+ (fft_result[i][1])*(fft_result[i][1]) );
An example array (for 30 samples, normally I am working with 256 samples):
"0.9261901713034604",
"2.436272348237486",
"1.618854900218465",
"1.849221286218342",
"0.8495016887742839",
"0.5716796354304043",
"0.4229791869017677",
"0.3731843430827401",
"0.3254446111798023",
"0.2542702545675339",
"0.25237940627189",
"0.2273716541964159",
"0.2012780334451323",
"0.2116151847259499",
"0.1921943719520009",
"0.1982429400169304",
"0.18001770452247",
"0.1982429400169304",
"0.1921943719520009",
"0.2116151847259499",
"0.2012780334451323",
"0.2273716541964159",
"0.25237940627189",
"0.2542702545675339",
"0.3254446111798023",
"0.3731843430827401",
"0.4229791869017677",
"0.5716796354304043",
"0.8495016887742839",
"1.849221286218342"
How to filter /smooth it? whats about gauss? Any idea how to begin or even giving me a sample code.
Thank you for your help!
best regards
josef
Simplest way to smooth would be to replace each sample with the average of it and its 2 neighbors.
The simpliest idea would be taking average of 2 points and putting them into an array. Something like
double smooth_array[S];
for (i = 0; i<S-2; i++)
smooth_array[i]=(magnitude[i] + magnitude[i+1])/2;
smooth_array[S-1]=magnitude[S-1];
It is not best one, but I think it should be ok.
If you need the scientific approach - use some kind of approximation / approximation algorithms. Something like least squares function approximation or even full SE13/SE35 etc. algorithms.
I have been struggling badly with this challenge my lecturer has provided. I have programmed the files that set up the class needed for this solution but I have no idea how to implement it, here is the class in question were I need to add the algorithm.
#include "Solver.h"
int* Solver::findNumPaths(const MazeCollection& mazeCollection)
{
int *numPaths = new int[mazeCollection.NUM_MAZES];
return numPaths;
}
and here is the problem description we have been provided. does anybody know how to implement this or set me on the right track, Thank you!
00C, we need your help again.
Angry with being thwarted, the diabolically evil mastermind Dr Russello Kane has unleashed a scurry of heavy-armed squirrels to attack the BCB and eliminate all the delightfully beautiful and intellectual superior computing students.
We need to respond to this threat at short notice and have plans to partially barricade the foyer of the BCB. The gun-toting squirrels will enter the BCB at square [1,1] and rush towards the exit shown at [10,10].
A square that is barricaded is impassable to the furry rodents. Importantly, the squirrel bloodlust is such that they will only ever move towards the exit – either moving one square to the right, or one square down. The squirrels will never move up or to the left, even if a barricade is blocking their approach.
Our boffins need to run a large number of tests to determine how barricade placement will impede the movement of the squirrels. In each test, a number of squares will be barricaded and you must determine the total number of different paths from the start to the exit (adhering to the squirrel movement patterns noted above).
A number of our boffins have been heard to mumble something incoherent about a recursive counting algorithm, others about the linkage between recursion and iteration, but I’m sure, OOC, you know better than to be distracted by misleading advice.
Start w/ the obvious:
int count = 0;
void countPaths( x, y ) {
if ( x==10 && y==10 ) {
count++;
return;
}
if ( can-move-right )
countPaths( x+1, y );
if ( can-mopve-down )
countPaths( x, y+1 );
}
Start by calling countPaths(0,0).
Not the most efficient by a long shot, but it'll work. Then look for ways to optimize (for example, you end up re-computing paths from the squares close to the goal a LOT -- reducing that work could make a big difference).
All suggestions and links to relevant info welcome here. This is the scenario:
Let us say I have a .wav file of someone speaking (and therefore all the samples associated with it).
I would like to run an algorithm on the series of samples to detect when an event happens i.e. the beginning and the end of an envelope. I would then use this starting and end point to extract that data to be used elsewhere.
What would be the best way to tackle this? Any pseudocode? Example code? Source code?
I will eventually be writing this in C.
Thanks!
EDIT 1
Parsing the wav file is not a problem. But some pseudo-code for the envelope detection would be nice! :)
The usual method is:
take absolute value of waveform, abs(x[t])
low pass filter (say 10 Hz cut-off)
apply threshold
You could use the same method as an old fashioned analog meter. Rectify the sample vector, pass the absolute value result though a low pass filter (FIR, IIR, moving average, etc.), than compare against some threshold. For a more accurate event time, you will have to subtract the group delay time of the low pass filter.
Added: You might also need to remove DC beforehand (say with a high-pass filter or other DC blocker equivalent to capacitive coupling).
Source code of simple envelope detectors can be found in the Music-DSP Source Code Archive.
I have written an activity detector class in Java. It's part of my open-source Java DSP collection.
first order low pass filter C# Code：
double old_y = 0;
double R1Filter(double x, double rct)
{
if (rct == 0.0)
return 0;
if (x > old_y)
old_y = old_y-(old_y - x)*rct/256;
else
old_y = old_y + (x - old_y) * rct/256;
return old_y;
}
When rct=2, it works like this：
The signal = (ucm + ucm * ma * Cos(big_omega * x)) * (Cos(small_omega1 * x) + Cos(small_omega2 * x) )
where ucm=3,big_omega=200,small_omega1=4,small_omega2=12 and ma=0.8
Pay attention that the filter may change the phase of the base band signal.