PtrMan PtrMan

3.0.4

no derivation of eternal sequences
no evidental base overlap allowed anymore
no derivation of non-eternalized implications
no image transformation for images with size > 2 anymore
only deriving most general and most specific in var intro
termlink record for image transformations
anticipation again restricted to input (yes, not everything is always a step forward :) )
anticipation tolerance handling improved (ANSNA ideas)

Sim	Language	Commercial?
JaamSim	Java	OSS
SimPy	Python	OSS
VirtuaPlant	Python
ManPy	Python	OSS
AnyLogic		yes
SIMUL8		yes
Simio		yes
OptQuest OptTek		yes

"Feed forward NAL-based Neural Network".

Introduction

Neural networks are one way to represent any computable function. They can be trained with a training algorithm. Non-bayesian NN's can't handle uncertainty. This paper proposes a way on how to leaverage some of the advantages of NN's to represent and learn a function which also computes uncertainty. This is archived by using functions which use NAL truth values. Advances in deep learning training methods may also get leaveraged in the future, but this is not explored in this work.

Core Idea

All values are represented as NAL-Truth Values
ring of addition is replaced with revision, ring of multiplication is replaced with deduction (TODO - verify if this makes sense)

Keybase proof

I hereby claim:

I am ptrman on github.
I am ptrman (https://keybase.io/ptrman) on keybase.
I have a public key ASCls5hKcUFjaxvGN7WDbMzdkIjUQcwyU8btD0A9UutuIgo

To claim this, I am signing this object:

Tools for AI/AGI: All ordered by subjectivly usability

keywords: tools, tooling, tool

Probabilistic Programming

ProbLog

Er und sie saßen in der Bank und Sie schrieb ein Gedicht. He and she sat in the bench and she wrote a poem.

	#![allow(non_snake_case)]

	extern crate rand;

	use std::default::Default;

	use rand::Rng;
	use rand::distributions::{Normal, Distribution};

	// automatic differentiation

	;;; PYTHAG.CL
	;;; "Pythagoras" -- a program that demonstrates heuristically-guided
	;;; concept formation in mathematics.

	;;; (C) Copyright 1995 by Steven L. Tanimoto.
	;;; This program is described in Chapter 10 ("Learning") of
	;;; "The Elements of Artificial Intelligence Using Common Lisp," 2nd ed.,
	;;; published by W. H. Freeman, 41 Madison Ave., New York, NY 10010.
	;;; Permission is granted for noncommercial use and modification of
	;;; this program, provided that this copyright notice is retained

	# bug: investigate looping wen crawling www.wired.com

	import os

	class Config(object):
	def __init__(self):
	# force to download https
	self.forceHttps = True

	import hashlib

	// https://de.wikipedia.org/wiki/Bin%C3%A4re_Exponentiation#Pseudocode_(Algorithmus)
	double powi(double v, int pow) {
	double res = 1;

	// we just use the 8 bit
	for(int i=7;i>=0;i--) {
	res = res*res;

	//if( pow & (1 << i) )
	// res = res * v;