WoW Is A Virtual Operant Conditioning Chamber

It totally is!

We were conversing about using Operant Conditioning Chambers (also called Pavlovian conditioning by a select few) in class today in order to procure more “conventional” behavior response patterns when developing computational stimulation types for artificial intelligence parameters. As I was listening to the lecture and playing around in the lab trying to complete some of the coursework, I was utterly absorbed with a consciousness. World of Warcraft, in essence, is a Skinner Box, which is a basic device that we have all seen where a manipulandum is influenced by a subject creating a behavioral response. Like a rat hitting a lever and getting some cheese. WoW is analogous to this arrangement in the sense that it simply advances the minimalism of a Skinner box with more refined manipulant timetables of fortification to supply incentives for rhythmic events. This is most evident with gear rotation in the game, for example arena seasons while having some great pool of averages, is essentially a random agenda. And if Skinner gave us anything, we know that random occurrence have been fantastically successful when generating condition behavior (you can substantiate this is indeed the case by glancing over Operant Conditioning of Cortical Unit Activity by Eberhard E. Fetz from the University of Washington School of Medicine).

Furthermore, there is even more psychological ties to why the game has been so successful, albeit the content not being very inimitable. It is fundamentally a virtual setting that is so alluring to players because it meets the basic requirements of Maslow’s Hierarchy of Needs. Just to point out a few within the games sphere, physiological needs are met because you can eat or drink (or get drunk which I really never understood the entire appeal of online alcohol), safety needs are met because you are provided health and well-being through your guild and peers, social needs are met through social constructs through varies mediums of communication, likewise you are grouped with your guild to the point that it is tagged directly above your name, esteem needs are met because the entire game is built around having gear, and showing it off to people, etc.

Huh. I think Blizzard hired a staff of psychologists to analyze the game, and then make appropriate suggestions on how to tailor it to make it more repetitively playable.


SharePoint Back-Propagation Neural Network Problem

Yeah, I know what you are thinking, but I’m not full of shit, and I know often times I bring SharePoint to probably levels it shouldn’t be taken to, but whatever. It’s actually a side project I am working on that is looking to aggregate several sets of data into a forecasting model type environment since SharePoint lends itself pretty well to the data aggregation part, and partially well to the data mining part, well, I mean it at least it kinda of exposes the required objects through the API that would otherwise be required to do it.

Ok, so for people that haven’t worked with AI before, the highest level introduction possible…

So there are basically two types of artificial intelligence, you have weak artificial intelligence, and you have strong artificial intelligence. Weak artificial intelligence doesn’t really have the capability to evolve that well, so it can be argued whether it really qualified as AI at all. It doesn’t really constitute the presence of a pattern that mimics human behavior and the concept of evolved choice, but more relies on the clever programming and raw computing power to represent behavior that may be considered to be “human”.

On the other hand, there also exists the concept of strong artificial intelligence, which is a lot different, since it implies that the behavior, and choice patterns, of humans can logically be represented. So, in essence, your patterned programming is instead representative of the human mind. I haven’t really seen anything in application that has done this, but in theory this is what an expert system that targeted a business application should adhere to, something like SharePoint, however weak AI might be a stepping stone into such arguments.

Regardless, if SharePoint, as a primary business application platform, were to be used coupled with an AI system, it would be composed / could use / whatever of three main concepts:

Expert Systems

Neural Networks (or Artificial Neural Networks [ANN])

Evolutionary Algorithms

OK, so there are several parts and concepts that make it up, The problem that I was running into was building a Back-Propagation Neural Network, however if I could get the rudimentary concept to work I plan on extending it to hopefully work with Dynamic link matching (Neuronal Modeling), which is my real interest. What’s this? Well, I am not very adapt at its concepts, but have studied it for a wee bit, and it basically is how one could theoretically use pre-defined neural systems for the recognition of external objects, which is neato cheato.

Dynamic link matching is one of the most robust mechanisms known mostly in the realm for physical pattern recognitions (or, in a broader sense, translation invariant object recognition) as it doesn’t have leave much error that is left for distortion (which generally occurs because expressions change so much during the templating process [also known as topographic mapping] and depth skews) of the inputted objects. Dynamic link matching is heavily dependent on the concept of wavlets, Gabor wavelet transform more specifically (which are responsible for grey-value distributions). The most notable thing about DLM is its low level of error rate, because it compensates well for depth and deformation within the template scan.

After the template scan has occurred, the fun stuff appears to start happening.

You can generally see something like a humanface (represented by the circular object) several little dotted nodes across it (for which the plane the image is mapped on is a neural sheets of hypercolumns), which is representative of a neuron, which, going back to the wavlet talks, also has an associated jet value, which is orchestrates the grey-value distribution.

When the actual matching is performed of the inputted object against the stored template, it leverages network self-organization. I will talk about this maybe in a later post, because there has been no posting of my problematic code yet which is starting to annoy me.

Anyhoo, I don’t remember what I was writing about now. Oh yeah, Back-Propagation. So I was working on that for a client, and my god, what a pain in the butt getting some of it to work with SharePoint was. My main problem was getting the god damn weights to update correctly. What I finally settled on was this:


private readonly Unit[][] neuralNet;

public double[] neuralData;

public static double PrimaryDerivationOfActivation(double Argument)
return (Argument * (1 – Argument));

protected void UpdateWeights(double learningCount, double influence, double decayRate)
for (int i = 1; i < neuralNet.Length; i++) { for (int j = 0; j < neuralNet[i].Length; j++) { Unit unit = neuralNet[i][j]; foreach (Link link in unit.InputLinks) { double lr = (((learningCount * link.Source.GetOutput()) * unit.neuralData[0]) * PrimaryDerivationOfActivation(unit.GetOutput())) + (influence * unit.neuralData[1]); unit.neuralData[1] = lr; link.Weight = (link.Weight + lr) - (decayRate * link.Weight); } } } } [/csharp] Whew, I am glad I finally got the mother to work. Anyways, I will hopefully be releasing the forecasting system if the client is hip to it, and hopefully an API that allows other developers to extend other AI applications into SharePoint in order to maybe build other applications. Or I may be the only person interested in it. Meh. :)


Knowledge Management Schemes

Knowledge administration has emerged into the business line of attack aiming at solving current business confrontations increasing effectiveness of nucleus processing of business at the same time simultaneously developing incessant originality and novelty.

Knowledge management more specifically utilizes tools for processing and technique scheming combining relevant managerial data, knowledge and information to develop business worth and enabling the organizations to take advantage of on its insubstantial e.g., knowledge, as well as the intellectual property, enabling businesses to reach their ultimate goal and the same time maxing out on competency.

Knowledge management needs are based on model shifting in the business realm where knowledge is center to organizing performance. Knowledge Management provides businesses variety of tools and techniques and line of attack to apply to existing business processing.

Knowledge management involves more than production of information, rather it involves the capturing of data at the basis, the transmission and testing of the facts, as well as the communiqué of information established on or resulting from the data to those acting on the information.

Thus, knowledge management is an effective scheme that integrates processing, people and technology, linking them in union. Data mining is employed in knowledge management and is the pivotal scheme in the strategy, since it discovers novel knowledge pulling it from pending information and data, thus growing existing knowledge assets of the business.

We discuss the vital aspects of the changes by thrashing out essential notions of ‘data mining’ and how it relates to any type of science. Subsequently, we will elaborate more details on the key data mining methods, including its disadvantage and advantages. This will include the contributions to the structuring of significant knowledge property. In the text, data mining is usually explained at two levels: a broad perspective and a narrow perspective.

Knowledge Discovery in Databases (KDD), whilst the extensive viewpoint equates data mining to its procedure, it narrows the perspective making data mining a step in the process KDD.

Data mining regardless of where it fits is defined as the ‘nontrivial extraction of implicit, which was at one time known as the potential helpful information from data.

Information is easily comprehensible by humans with use of data mining, since it employs a combination of techniques, including machine learning, statistical and visualization. The process of data mining includes sifting and extracting. Thus, data mining sifts through large amounts of information and extracts relevant parts of the data for exacting analysis of a trouble. Thus, data mining’s conventional data examination together with essential statistical techniques, enforces heavy use of artificial intelligence.

As in the case of probing data mining, thus, the emphasis is not focusing on extracting necessary, rather it focuses on generating of a theory. Data mining helps users discover helpful information, such as patterns and trends, which are hidden within the business data, by using sophisticated statistical analysis and replica modus operandi.

Data mining assist users with overwhelming data collections that have increased over time. Data mining helps optimize business decisions, improve communication, raise the value of each customer, and civilizing customer approval. Retailers employ data mining schemes to help them understand the patterns of customer purchasing, detecting fraud, product warranty organization, and recognizing high-quality credit risks.

Over time, data mining has become fashionable for reason of:

  1. The chief reason for reputation of data mining is that its strategies due to huge amounts of data collected already, and novelty developing data requiring processing beyond conventional line of attack. Thus, the collected data from scientific, business and government orgs abroad is massive. Human analysts would have difficulty coping with the ever growing and overpowering surplus of data without the assistance of data mining.
  2. Humans analyzing data are inclined to make mistakes since the insufficiency of the human psyche (i.e., the circumscribed level-headedness difficulty) to solve multifaceted multifactor dependence of data, and thus sometimes lacking objectiveness in analysis. Humans try to draw from results basing the information on experiences and experimentation, which was gained from investigation from other schemes. Data mining on the other hand, reflects data conveyed without preconceiving theory.
  3. Another benefit of data mining predominantly in the case of huge collections of data is that it cost less than what it could cost a company hiring in a group of experts. Data mining does not eliminate human participation, rather data mining significantly simplifies the workload, allowing analysts capable in statistics or programming to handle the procedure of extracting knowledge from data.