Information about the Shaivite Temple #Blessed

THCv and Marijuana Lineages Forum Temple Coin Forum Information about the Shaivite Temple #Blessed

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    Temple Coin is a Scrypt Coin Clone, and is being shared on Facebook rather than with Bitcoiners. February 20th, 2018, we will launch a Bitshares Clone and OpenLedger type Exchange, and we will use ChainLink Middleware to make the exchange Agnostic, and to allow the Assets to move off of the exchange.

    We are looking for people who want to make Coins for their Towns, States, Churches, Temples, Clubs, Government Organizations, Non-Profits, etc. These will mostly be Cryptonote Coins, meaning they will use the Cryptonight (Kryptonite, so no mining Machines taking over completely) algorithm for town coins. But we will also create other Coin Clones, such as Graphene Social Media Blockchains that operate like Steemit, and Connect your Coins to our Network to raise their value.

    At the Shaivite Temple we are dedicated to sharing the blessings of Lord Shiva. We are currently working on a Breeding Program to create unseen Marijuana Flavor Strains with THCv (a rare Cannabinoid). We are achieving this using Top Shelf Medical Marijuana Flavor Genetics combined with African Strains such as Malawi, South African Kwazulu and Early Durban. We know we will achieve creating a flavor Strain like none other, as no one else is using African Genetics on purpose, and when they do they only use Durban Poison. For example, Cherry Pie is Durban Poison crossed with Grandaddy Purple, and Girl Scout Cookies is Durban Poison F1 crossed with OG Kush. But no one has taken that information and decided to use it in a Breeding program, utilizing the Genetic Diversity of African Strains; with the intent of creating high THCv strains. The seeds and clones for these seeds will be available through the Temple, and will be given away free in many cases (small amounts).

    We will also be showing people how to create Lab Grade (Reagent Grade), Medical Grade and Terpene Enhanced Hash. We will never use the word “Concentrates”, but instead “Hashish”, “Hash” or “Charas” as the word “Concentrates” ignores the benefits and individual effects of Terpenes. Terpenes have their own Religious, Medical and Recreational uses. We will also be showing people how to make things like Bhang.

    This site will be dedicated to Teaching people the Teachings of Lord Shiva, as well as other Religions; and various other Historical Lessons. As well as Organic Chemistry, Biochemistry and Neuroscience. The Temple itself will create gatherings for exchange of Clones and Seeds by Temple members, as well as for Festivals, Holidays and Ceremonies. There will be videos made of everything so as to reach as many people as possible.

    We will not be proselytizing and are not out to convert anyone, but we accept anyone who is interested in what we are doing for Lord Shiva.

    • This topic was modified 5 months, 2 weeks ago by  shaivi5_wp.
    • This topic was modified 5 months, 1 week ago by  shaivi5_wp.
    #390 Reply


    For anyone that needs this completely explained, I am about to completely explain it.

    So, what problem am I Solving here? Why isn’t Steemit enough?

    Steemit is not an end result, it is an example. And to prove how wrong the implementation was (not detrimental, just impairing), you need look no further than the Namesake. Steemit is meant to be a Copy of Reddit, but with Money being given out for Votes. And that is a great idea, I am not discounting that.

    But here is the Problem I am solving. There are already websites where people make Money. People make Money on Youtube, people make money by using their Twitter Followers to advertise for Tide or Iphone, or whatever Snoop Dogg can sell on his feed (nothing against Snoop, and I am not finished explaining yet), and people are on Instagram all day hoping to make a Brand that they can sell, or become a Model; then on Facebook people are using their Pages and Feeds to sell things.

    And Youtube, that is the best example of the Problem that needs Solving. YouTube has Adsense which allows you to be paid, directly to a Bank Account. But you have to apply, and give them your Social Security Number, then actually earning money on YouTube is nearly impossible unless you get a sponsor, and join a network of Youtubers who you can have your videos attached to. And it doesn’t matter really how many likes you get, or comments, it is all about views. And on top of that, you have to put ads in your videos that have nothing to do with your videos at all, and people don’t really watch or click on anyways. Steemit is the example, Youtube is the Problem, are you starting to undersstand the Solution?

    Now, further, Forums. Most people don’t use forums, but there are examples like the Joe Rogan Forum, where there are MMA people, and DMT people, and Conspiracy Theorists, and random people, all in 1 place. And Joe Rogan has a Podcast, where he has Advertisers who pay him money to sell you things. But you do not make any money off of Joe Rogan. To Joe Rogan you are a product that he can sell to Audible, or Snack Boxes or whatever company he is selling things for that day. But if he created a Blockchain for his Forum, the whole community could be creating wealth for him and themselves. Same for any other Youtuber, or Podcaster, or Blogger or anyone with a Forum.

    Then, not just YouTube, a website like BlogTalkRadio could put their website on a Blockchain, and everyone making Podcasts there could earn money straight off the Blockchain.

    Once every website that wants to offer you money is on a Blockchain, you will be able to not only be able to Democratically earn money on a Website like Steemit by liking other people’s Content and getting likes. But you will Democratically be able to choose which website you want to earn on.

    And if anyone is wondering how this all = money. It is through the exchanges. You trade these coins for Bitcoins, then you can cash those out into your Bank, or you can do things like buy Gift Cards on Gyft.

    #391 Reply


    Calling Bitcoin the Internet of Money, which is what the Bitcoin community did, confused people. And my Coin is going to represent a new kind of Coin, created for a reason. For example. Create a Coin for your Religion (which is what this Coin will be, not called Hindu Coin though), or create a Coin for your Town, or a Coin for your State, or a Coin for your Company, or anything.

    This is not going to be the Internet of Money, it is the Hashtags of Money.

    People with Websites should be making Graphene Blockchains to attach to their Websites; but people who do not currently or want to operate some kind of Website that many people can use, like a Forum, or exmples like Steemit and other things that can be put on a Graphene Blockchain. You can make a regular Coin, a Scrypt Coin Clone, or a Cryptonote. Here is information about Cryptonotes.

    The same way Cryptonote created a sytem where anyone can create a Coin, and Forknote created a more streamlined version. I am going to add to that, and I will teach everyone who wants to learn, how to make their own Coin using the page where the Religious Coin files are. So I will take the Cryptonote information, and the Bytecoin information, and everything I have done to figure it out, and the things we went through to make their Instructions work. And I will make it easy for everyone, and since everyone’s first question is probably “What would I make a Coin for?”, my main point will be to Answer that Question for everyone. For your Town, for your Religion, for your Church, for your Charity, for your Government Organization, for your anything. Create a Coin.

    Then I will teach people how to get the Coin used by more people. The Cryptocoin system is no different than the Stock Market, or Currency Exchanging between Countries Currencies. It is the same. And it would make a lot more sense if they had meaning.

    And btw, I want to explain something about how the Market works for everyone, and about how we can make some money once our Coin has Value. A “Bitcoin Whale” is a person who has a ton of Bitcoin, the concept also exists in other things like Stocks. A Bitcoin Whale has so many Bitcoin, that if they sell a percentage of them, they can actually change the entire Bitcoin market. So, they use this leverage to make Money. The worst/best example is a Bear Whale. A Bear Whale drops the prices so low it scares everyone, then everyone Sells. So then the Bear Whale buys all the Coins everyone is frantically Selling Lower and Lower, they get a bunch of cheap Bitcoins, then they raise the Price back up.

    So, the first 100 or so people that are involved in mining, will have that kind of Power over the coin’s market. And we can make a lot of Money with that by itself, or that mixed with Bitshares Assets, or that mixed with another Currency we make later, etc.

    Once the Coin is launched, it will be ideal to start setting up Mining Pools. So anyone who is interested in Mining, and knows a little about Programming, this is a way where once there are Hundreds and Thousands of people Mining this Coin, their best bet to get the most Coins will be to join a Pool, put their Mining power toward the Pool, get a portion of all the Coins the Pool gets from every Block, and the owner of the Pool collects a small amount of fees from everyone. But once the Coin is up and running, people can start setting up Pools.

    And you can set up Pools for various Coins, so you can add new Coins that you want people to mine to your Pool.

    Think about it this way, why do you buy a Stock? Because you heard that Google or Apple or Tesla are launching something new. The US Dollar Represents a County, the British Pound Represents a County, the Chinese Yuan Represents a Country. What does Bitcoin Represent?

    Imagine if when #IceBucketChallenge happened, instead of doing it to get people to Donate, what if you could have mined #IceBucket coin, and given money to Research that way? They could even Premine 50% of the Coin Automatically in the Code (our Coin won’t have premine, but the purpose would usually be fundraising), then launched the #IceBucketChallenge, and you can actually Mine the Hashtag, and make money from the Hashtag while the creator of the Hashtag makes money.

    But you can also do it for your Town, for your Company, for your Temple or Church, for anything. And share it like a Hashtag, and people don’t have to dedicate themselves to 1 Coin, you can mine as many as you want.

    And with Cryptonotes, the big Mining Farms can’t take all of the Coins, because they are made for Computer and Laptop Mining. So these are Coins that can’t really become to hard for anyone to mine, as long as everyone knows where the new ones are.

    #395 Reply


    Think about this for a second. Why does anyone in America want to build a Wall? America thinks itself part of some Alien World. We are not part of Europe, and at our Northern Border is Canada, and our Southern Boarder Mexico. And after NAFTA the Peso to USD exchange rate went from something like 200 Pesos to 1 USD, to now around 20 Pesos per 1 USD. But after NAFTA people began to move to the Factories, and live near Factories, and lost their Culture. And if they were farmers, suddenly their crop had a Global Market value, and not just its value within their local Community. And it destroyed the Individualism of Mexico. This is why the Zapatistas rose up, and the Cartels, and now people want to come to America. And there are Americans who want to build a Wall.

    But once this economic situation is corrected. And America stops pretending it is on an Island with Canada, and not part of a giant land mass called “The Americas” which includes both North and South America. A Wall would be nothing more than a Statue.

    Currencies, both fiat and crypto, don’t actually contain any value they simply represent value. They are called Trade Instruments, meaning, instruments that facilitate trade. Stocks are an example of trade instruments that aren’t money, they have no actual value but they represent a share of a company and the company itself does the work that turns the profits that gives a share its theoretical value. All trade Instruments work along the same lines: Fiat is traded by banks and Foreign Exchange companies, Stock is traded on Stock Exchanges such as the New York Stock Exchange and Cryptocurrencies are traded on various Cryptocurrency Exchanges. All of their values are representations of real things, for example Stocks Represent created and distributed goods and services by a particular company, while fiat currency represents created and distributed goods and services of a nation. Both change based on industrial/technological/scientific/developmental/etc. advancements within those companies or nations, as well as various factors such as trade volume and inflation. It is best to trade your trade instruments at the highest value possible and use them to buy real items, such as: Precious metals, Livestock, Software, Machines, Produce/Seeds, Land, Realestate, etc and then use those to get more trade instruments.

    Trade volume is how many people are buying and selling a particular currency or stock. The more people who are buying it, the higher the value will rise.

    An example of Inflation is when the United States starts printing too much money. When this happens a dollar starts being worth less, which in turn means it will take more money to buy the same materials. For instance, if you go to the store and one day Milk is $3/Gallon but then you go a few months later and notice it is $5/Gallon, this is because of inflation. Inflation also drives things like the minimum wage and social security checks, which are usually based on the cost of living. Cryptocurrencies with no cap will eventually inflate into eternity and lose value, unless they have a high trade volume.

    Supply and Demand is the comparison of how many people want something against how many their are of that thing. For example, when Apple creates a new IPhone the value is higher than it really should be and as the technology slightly or drastically ages, the value goes down.

    A Whale is a person who has a large quantity of a certain trade instrument and uses that to effect the markets. For example, if someone has 51% of a particular stock they could either sell them all quickly which would bring the value of that stock down, or they could hold on to all of them which makes them more rare and makes them more valuable.

    Bubbles are when something is artificially high in value, 2 examples of this are: IPhones as mentioned before, and Gasoline. Gasoline raises in value based simply on the speculation that “one day we might run out”, this creates bubbles which raises prices. But Gasoline will probably be replaced by ethanol before it ever even gets close to being used up.

    Look at different exchanges- Sometimes you can get more on one site than you can on another site, for the same coins. And sometimes you can even buy coins on one site and sell them on another site for more. This works better when you are trading Crypto to Crypto rather than Crypto to fiat.

    Use coins to create goods and services- Don’t just use coins to buy random things, buy software and other goods that you can use to produce things or spend them on things like textbooks. Create a product if you can.

    Promote your favorite coins- If you have a favorite coin and buy some, don’t forget to share it on social media.

    Create a currency- Satoshi gave out the Bitcoin source code so that people could make their own currencies.

    Create an exchange- Transaction fees can earn the owners a lot of coins and you can help fledgling altcoins by offering them on your exchange.

    Don’t buy above spot- If you are trading coins for precious metals, check the current global value of that metal and buy as close to that value as you can.

    Invest in foreign countries- Don’t think America is the be all end all.

    #398 Reply


    The entire Government and Business world runs of Business Rules Engines (BREs) that are stipulated either by Policy or Law.

    Everyone thinks that the next big feature in Bitcoin is going to be some little thing like a security feature. But if there were coins with entire Business Rules Engines attached to Cryptocurrencies, or a tool that allowed you to create your own Business Rules Engines within a wallet. Or it could be part of the coin, but maintained as a website, like Bitshares is. Bitshares and Steemit are also probably a good place to start, they might even have Business Rules Engines.

    But the Business Rules Engines should be geared toward the community, not just making the coin work, but it should actually effect the community, and if people don’t like a certain coin and its rules, they could just go to another one (Ex of a Controversial Rules type would be Income Redistribution or something).

    This would actually allow Cryptocurrencies to become Decentralized Democratic Structures, coins could be made where people submit new Rules to a website, and they are voted on by the Community, maybe each wallet gets 1 vote, votes could even be done in the wallet.

    Anyways. Business Rules Engines could really change the way people outside the Bitcointalk forums view coins.

    Bot Development

    Virtual Assistant Demo

    It was like needing to bike across town with a blindfold on — you had a general sense of what direction you needed to go, but the only way to progress was by hitting a wall.

    Expert Systems


    Inference Engine

    Rule1: Human(x) => Mortal(x)

    Bayesian Statistics

    Bayesian Network

    Knowledge Representation

    Knowledge Engineering

    Rule Based Expert Systems

    Expert Systems

    Inline image 1

    Rule Based Expert Systems



    History of Bots and Microsoft Tay

    Dialog System

    Visual Basic could be used for the implementation while Microsoft Access could be used for creating the database. (Others: VB.NET, Jess, C, C++, Lisp, PROLOG)
    A production system may be viewed as consisting of three basic components: a set of rules, a data base, and an interpreter for the rules. In the simplest design a rule is an ordered pair of symbol strings, with a left-hand side and a right-hand side (LHS and RHS). The rule set has a predetermined, total ordering, and the data base is simply a collection of symbols. The interpreter in this simple design operates by scanning the LHS of each rule until one is found that can be successfully matched against the data base. At that point the symbols matched in the data base are replaced with those found in the RHS of the rule and scanning either continues with the next rule or begins again with the first. A rule can also be viewed as a simple conditional statement, and the invocation of rules as a sequence of actions chained by modus ponens.

    Replication of expertise — providing many (electronic) copies of an expert’s knowledge so it can be consulted even when the expert is not personally available. Geographic distance and retirement are two important reasons for unavailability.
    Union of Expertise — providing in one place the union of what several different experts know about different specialties. This has been realized to some extent in PROSPECTOR [Reboh81] and CASNET [Weiss7b>] which show the potential benefits of achieving such a superset of knowledge bases.
    Documentation — providing a clear record of the best knowledge available for handling a specific problem. An important use of this record is for training, although this possibility is just beginning to be exploited. [Brown82, Clancey79].

    Rule-based expert systems evolved from a more general class of computational models known as production systems [Newell73]. Instead of viewing computation as a prespecified sequence of operations, production systems view computation as the process of applying transformation rules in a sequence determined by the data. Where some rule-based systems [McDermott80] employ the production-system formalism very strictly, others such as MYCIN have taken great liberties with it.2 However, the. production system framework provides concepts that are of great use in understanding all rule-based systems. A classical production system has three major components: (1) a global database that contains facts or assertions about the particular problem being solved, (2) a rulebase that contains the general knowledge about the problem domain, and (3) a rule interpreter that carries out the problem solving process.
    The facts in the global database can be represented in any convenient formalism, such as arrays, strings of symbols, or list structures. The rules have the form

    IF the ‘traffic light’ is ‘green’ THEN the action is go
    IF the ‘traffic light’ is ‘red’ THEN the action is stop

    IF IF
    . .
    . .
    The antecedent of a rule incorporates two parts: an object (linguistic object) and its value. The object and its value are linked by an operator. The operator identifies the object and assigns the value. Operators such as is, are, is not, are not are used to assign a symbolic value to a linguistic object. Expert systems can also used mathematical operators to define an object as numerical and assign it to the numerical value.

    facts are associative triples, that is, attribute-object-value triples, with an associated degree of certainty

    The of is with certainty ))

    There are five members of the development team:
    1. domain expert
    2. knowledge engineer
    3. programmer
    4. project manager
    5. end-user

    We can regard the modularity of a program as the degree of separation of its functional units into isolatable pieces. A program is highly modular if any functional unit can be changed (added, deleted, or replaced) with no unanticipated change to other functional units. Thus program modularity is inversely related to the strength of coupling between its functional units.

    A rule-based system consists of if-then rules, a bunch of facts, and an interpreter controlling the application of the rules, given the facts. These if-then rule statements are used to formulate the conditional statements that comprise the complete knowledge base. A single if-then rule assumes the form ‘if x is A then y is B’ and the if-part of the rule ‘x is A’ is called the antecedent or premise, while the then-part of the rule ‘y is B’ is called the consequent or conclusion. There are two broad kinds of inference engines used in rule-based systems: forward chaining and backward chaining systems. In a forward chaining system, the initial facts are processed first, and keep using the rules to draw new conclusions given those facts. In a backward chaining system, the hypothesis (or solution/goal) we are trying to reach is processed first, and keep looking for rules that would allow to conclude that hypothesis. As the processing progresses, new subgoals are also set for validation. Forward chaining systems are primarily data-driven, while backward chaining systems are goal-driven. Consider an example with the following set of if-then rules
    Rule 1: If A and C then Y
    Rule 2: If A and X then Z
    Rule 3: If B then X
    Rule 4: If Z then D
    If the task is to prove that D is true, given A and B are true. According to forward chaining, start with Rule 1 and go on downward till a rule that fires is found. Rule 3 is the only one that fires in the first iteration. After the first iteration, it can be concluded that A, B, and X are true. The second iteration uses this valuable information. After the second iteration, Rule 2 fires adding Z is true, which in turn helps Rule 4 to fire, proving that D is true. Forward chaining strategy is especially appropriate in situations where data are expensive to collect, but few in quantity. However, special care is to be taken when these rules are constructed, with the preconditions specifying as precisely as possible when different rules should fire. In the backward chaining method, processing starts with the desired goal, and then attempts to find evidence for proving the goal. Returning to the same example, the task to prove that D is true would be initiated by first finding a rule that proves D. Rule 4 does so, which also provides a subgoal to prove that Z is true. Now Rule 2 comes into play, and as it is already known that A is true, the new subgoal is to show that X is true. Rule 3 provides the next subgoal of proving that B is true. But that B is true is one of the given assertions. Therefore, it could be concluded that X is true, which implies that Z is true, which in turn also implies that D is true. Backward chaining is useful in situations where the quantity of data is potentially very large and where some specific characteristic of the system under consideration is of interest. If there is not much knowledge what the conclusion might be, or there is some specific hypothesis to test, forward chaining systems may be inefficient. In principle, we can use the same set of rules for both forward and backward chaining. In the case of backward chaining, since the main concern is with matching the conclusion of a rule against some goal that is to be proved, the ‘then’ (consequent) part of the rule is usually not expressed as an action to take but merely as a state, which will be true if the antecedent part(s) are true (Donald, 1986).

    heuristic — i.e., it reasons with judgmental knowledge as well as with formal knowledge of established theories; 0
    transparent — i.e., it provides explanations of its line of reasoning and answers to queries about its . knowledge; l
    flexible — i.e., it integrates new knowledge incrementally into its existing store of knowledge.‘.

    MYCIN [Davis77b] [Shortliffe, 1976]. analyzes medical data about a patient with a severe infection, PROSPECTOR [Duda79] analyzes geological data to aid in mineral exploration, and PUFF [Kunz78] analyzes the medical condition of a person with respiratory problems. In order to provide such analyses, these systems need very specific rules containing the necessary textbook and judgmental knowledge about their domains.
    The first expert systems, DENDRAL [Lindsay801 and MACSYMA [Moses71], emphasized performance, the former in organic chemistry and the latter in symbolic integration. These systems were built in the mid-1960’s, and were nearly unique in AI because of their focus on real-world problems and on specialized knowledge. In the 1970’s, work on expert systems began to flower, especially in medical problem areas (see, for example [P0ple77, Shortliffc76, Szolovits78, Weiss79bl). The issues of making the system understandable through explanations [Scott77, Swartout811 and of making the system flexible enough to acquire new knowledge [Davis79, Mitchell791 were emphasized in these and later systems.

    Very often people express knowledge as natural language (spoken language), or using letters or symbolic terms. There exist several methods to extract human knowledge. Cognitive Work Analysis (CWA) and the Cognitive Task Analysis (CTA) provide frameworks to extract knowledge. The CWA is a technique to analyze, design, and evaluate the human computer interactive systems (Vicente, 1999). The CTA is a method to identify cognitive skill, mental demands, and needs to perform task proficiency (Militallo and Hutton, 1998). This focuses on describing the representation of the cognitive elements that defines goal generation and decision-making. It is a reliable method for extracting human knowledge because it is based on the observations or an interview.

    A representation is a set of conventions for describing the world. In the parlance of AI, the representation of knowledge is the commitment to a vocabulary, data structures, and programs that allow knowledge of a domain to be acquired and used. This has long been a central research topic in AI (see [Amarel81, Barr81, Brachman80, Cohen82] for reviews of relevant work).

    The interpreter is the source of much of the variation found among different systems, but it may be seen in the simplest terms as a select-execute loop in which one rule applicable to the current state of the data base is chosen and then executed. Its action results in a modified data base, and the select phase begins again. Given that the selection is often a process of choosing the first rule that matches the current data base, it is clear why this cycle is often referred to as a recognize-act, or situation-action, loop.

    EMYCIN [vanMelle80] [Bennet81a] ROSIE [Fain81], KAS [Reboh81], EXPERT [peiss79a], and OPS [Forgy77] OPS Carnegie-Mellon University [Forgy77] EMYCIN Stanford University [vanMelle80] AL/X University of Edinburgh EXPERT Rutgers University [Weiss79a] KAS SRI International [Reboh81] RAINBOW IBM Scientific Center (Palo Alto) [Hollander79]

    One of the most popular shells widely used throughout the government, industry, and academia is the CLIPS (CLIPS, 2004). CLIPS is an expert system tool that provides a complete environment for the construction of rule- and/or object-based expert systems. CLIPS provides a cohesive tool for handling a wide variety of knowledge with support for three different programming paradigms: rule-based, object-oriented, and procedural. CLIPS is written in C for portability and speed and has been installed on many different operating systems without code changes.

    There are alternatives to representing task-specific knowledge in rules. Naturally, it is sometimes advantageous to build a new system in PASCAL, FORTRAN, APL, BASIC, LISP, or other language, using a variety of data structures and inference procedures, as needed for the problem. Coding a new system from scratch, however, does not allow concentrating primarily on the knowledge required for high performance. Rather, one tends to spend more time on debugging the procedures that access and manipulate the knowledge.

    Evolutionary Computation (EC) is a population based adaptive method, which may be used to solve optimization problems, based on the genetic processes of biological organisms (Michalewicz and Fogel, 1999). Over many generations, natural populations evolve according to the principles of natural selection and ‘survival of the fittest’, first clearly stated by Charles Darwin in ‘On the Origin of Species’. By mimicking this process, EC could ‘evolve’ solutions to real-world problems, if they have been suitably encoded (problem representation is called chromosome). Automatic adaptation of membership functions is popularly known as self tuning and the chromosome encodes parameters of trapezoidal, triangle, logistic, hyperbolic-tangent, Gaussian membership functions, and so on. Evolutionary search of fuzzy rules can be carried out using three approaches. In the first method (Michigan approach), the fuzzy knowledge base is adapted as a result of antagonistic roles of competition and cooperation of fuzzy rules.
    The second method (Pittsburgh approach), evolves a population of knowledge bases rather than individual fuzzy rules. Reproduction operators serve to provide a new combination of rules and new rules.
    The third method (iterative rule learning approach), is very much similar to the first method with each chromosome representing a single rule, but contrary to the Michigan approach, only the best individual is considered to form part of the solution, discarding the remaining chromosomes of the population. The evolutionary learning process builds up the complete rule base through an iterative learning process (Cordon´ et al., 2001).

    Modus ponens is the . primary rule of inference by which a system adds new facts to a growing data base:
    THEN C IS TRUE. ——–

    First, some follow-on research to MYCIN addresses the human engineering problems directly, for example, by integrating high quality graphics with user-oriented forms and charts for input and output [Shortliffe81]. Second, some MYCIN-like programs finesse many human engineering problems by collecting data from on-line instruments rather than from users [Kunz78]. Exportability can be gained by rewriting [Carhart79, Kunz78] or by designing for export initially [Weiss79a].

    Extendability — the data structures and access programs must be flexible enough to allow extensions to the knowledge base without forcing substantial revisions. The knowledge base will contain heuristics that are built out of experts’ experience. Not only do the experts fail to remember all relevant heuristics they use, but their experience gives them new heuristics and forces modifications to the old ones. New cases require new distinctions. Moreover, the most effective way we have found for building a knowledge base is by incremental improvement. Experts cannot define a complete knowledge base all at once for interesting problem areas, but they can define a subset and then refine it over many weeks or months of examining its consequences. All this argues for treating the knowledge base of an expert system asean open-ended set of facts and relations, and keeping the items of knowledge as modular as possible.
    Simplicity — We have all seen data structures that were so baroque as to be incomprehensible, and thus unchangeable. The flexibility WC argued for above requires conceptual simplicity and uniformity so that access routines can be written (and themselves modified occasionally as needed). Once the syntax of the knowledge base is fixed, the access routines can be fixed to a large extent. Knowledge acquisition, for example, can take place with the expert insulated from the data structures by access routines that make the knowledge base appear simple, whether it is or not. However, new reasons will appear for accessing the knowledge base as in explanation of the contents of the knowledge base, analysis of the links among items, display, or tutoring. With each of these reasons, simple data structures pay large benefits. From the designer’s point of vi& there are two ways of maintaining conceptual simplicity: keeping the form of knowledge as homogeneous as possible or writing special access functions for non-uniform representations.
    Explicitness — The point of representing much of an expert’s knowledge is to give the system a rich enough knowledge base for high-performance problem solving. But because a knowledge base must be built incrementally, it is necessary to provide means for inspecting and debugging it easily. With items of knowledge represented explicitly, in relatively simple terms, the experts who are building knowledge bases can determine what items are present and (by inference) which are absent.

    Semantic Completeness of the knowledge base for a problem area is also desirable. Because of the nature of the knowledge base and the way it is built, however, it will almost certainly fail to cover some interesting (sometimes important) possibilities. In a very narrow problem area, for example, there may be 100 attributes of interest, with an average of 4 important values for each attribute. (Only in extreme cases will all attributes be binary.) Thus there would be 79,800 possible rules relating two facts (400 items taken two at a time), over 10 million possible rules relating three facts, and so on. While most are semantically implausible, e.g., because of mutually exclusive values, the cost of checking all combinations for completeness is prohibitive. Checking the inferences made by a system in the context of carefully chosen test cases is currently the best way to check the completeness of coverage of the rules

    If there is only one applicable rule, the obvious thing to do is to apply it. Its application will enter new facts in the database. While that may either enable or disable previously inapplicable rules, by our assumption it will never disable a previously applicable rule. If there is more than one applicable rule, we have the problem of deciding which one to apply. Procedure 21 Select-Rule has the responsibility for making this decision. Different data-driven strategies differ greatly in the amount of problem-solving effort they devote to rule selection. A simple and inexpensive strategy is to select the first rule that is encountered in the scan for S — “doing the first thing that comes to mind.” Unfortunately, unless the rules are favorably ordered, this can result in many useless steps. Elaborations intended to overcome such shortcomings can make data-driven control arbitrarily complex.

    Methods used for conflict resolution
    1 Use the rule with the highest priority. In simple applications, the priority can be established by placing the rules in an appropriate order in the knowledge base. Usually this strategy works well for expert systems with around 100 rules.
    2 Use the most specific rule. This method is also known as the longest matching strategy. It is based on the assumption that a specific rule processes more information than a general one.
    3 Use the rule that uses the data most recently entered in the database. This method relies on time tags attached to each fact in the database. In the conflict set, the expert system first fires the rule whose antecedent uses the data most recently added to the database.

    Uncertainty can be expressed numerically as certainty/confidence factor (cf) or measure of belief (mb)
    cf usually is a real number in a particular range, eg, 0 to 1 or -1 to 1
    Combining certainties of propositions and rules
    Let P1 and P2 be two propositions and cf(P1) and cf(P2) denote their certainties
    cf(P1 and P2) = min(cf(P1), cf(P2))
    cf(P1 or P2) = max(cf(P1), cf(P2))
    given the rule
    if P1 then P2: cf = C
    then certainty of P2 is given by
    cf(P2) = cf(P1) * C

    place the responsibility on the knowledge engineer to see that the rules are properly structured. Many problems caused by interactions can be solved by employing a hierarchical structure, with several levels of assertions between the direct observations and the final conclusions. The goal is to localize and limit tic interactions, and to have a rclativcly small number of clauses in a condition and a relatively small number of rules sharing a common conclusion. Note that this limitation on the number of rules does not reduce the amount of evidence considered in reaching a conclusion, but rather controls the ways in which the observations are allowed to interact. A hierarchical structure is typically employed by the experts themselves to reduce the complexity of a problem. Wherever the remaining interactions still prevent the assumption of local independence, the rules have to be reformulated to achieve the desired behavior. For example, in the strongly interacting situation where B, suggests A and B, suggests A, but the simultaneous presence of both B, and I33 rules out A one may have to augment the rule set
    { (B1 – – > A with weight L1)
    (B2 – – > A with weight L2) }
    with the rule (B1 & B2 –> A with weight-m). Thus, rather than viewing probability theory as a paradigm that prescribes how information should be processed, the knowledge engineer employs it as a tool to obtain the desired behavior.

    In contrast with the heuristic techniques for reasoning with uncertainty employed in many rule-based expert systems, the theory of belief networks is mathematically sound, based on techniques from probability theory. The formalism of belief networks offers an intuitively appealing approach for expressing inexact causal relationships between domain concepts [7, 20]. A belief network consists of two components [3]:
    • A qualitative representation of the variables and relationships between the variables discerned in the domain, expressed by means of a directed acyclic graph G = (V (G),A(G)), where V (G) = {V1,V2,… ,Vn} is a set of vertices, taken as the variables, and A(G) a set of arcs (Vi,Vj), where Vi,Vj ∈ V (G), taken as the relationships between the variables.
    • A quantitative representation of the ‘strengths’ of the relationships between the variables, expressed by means of assessment functions.

    Narrow scope — The task for the system must be carefully chosen to be narrow enough that the relevant expcrtisc can be encoded, and yet complex enough that expertise is required. This limitation is more because of the time it takes to engineer the knowlcdgc into a system including rcfmemcnt and debugging, than because space required for the knowledge base.
    Existence of an expert — Thcie are problems so new or so complex that no one rBnks as an expert in the problem area. Generally speaking, it is unwise to expect to be able to construct an expert system in areas where there are no experts.
    Agreement among experts — If current problem solving expertise in a task area leaves room for frequent and substantial disagreements among experts, then the task is not appropriate for an expert system.
    Data available — Not only must the expertise be available, but test data must be available (preferably online). Since an expert system is built incrementally, with knowledge added in response to observed difficulties, it is necessary to have several test cases to help explore the boundaries of what the system knows.
    Milestones definable — A task that can be broken into subtasks, with measurable milestones, is better than one that cannot be demonstrated until all the parts are working
    Separation of task-specific knowledge from the rest of the program — This separation is essential to maintain the flexibility and understandability required in expert systems.
    Attention to detail — Inclusion of very specific items of knowledge about the domain, as well as general facts, is the only way to capture the expertise that experience adds to textbook knowledge.
    Uniform data structures– A homogeneous representation of knowledge makes it much easier for the system builder to develop acquisition and explanation packages.
    Symbolic reasoning – It is commonplace in AI, but not elsewhere, to regard symbolic, non-numeric reasoning as a powerful method for problem solving by computers. In applications areas where mathematical methods are absent or computationally intractable, symbolic reasoning offers an attractive alternative.
    Combination of deductive logic and plausible reasoning — Although deductive reasoning is the standard by which we measure correctness, not all reasoning — even in science and mathematics — is accomplished by deductive logic. Much of the world’s expertise is in heuristics, and programs that attempt to capture expert level knowledge need to combine methods for deductive and plausible reasoning.
    Explicit problem solving strategy — Just as it is useful to separate the domain-specific knowledge from the inference method, it is also useful to separate the problem solving strategy from both. In debugging the system it helps to remember that the same knowledge base and inference method can produce radically different behaviors with different strategies. For example, consider the difference between “find the best” and “find the first over threshold”.
    Interactive user interfaces — Drawing the user into the problem solving process is important for tasks in which the user is responsible for the actions recommended by the expert system, as in medicine. For such tasks, the inference method must support an interactive style in which the user contributes specific facts of the case and the program combines them in a coherent analysis.
    Static queries of the knowledge base — The process of constructing a large knowledge base requires understanding what is (and is not) in it at any moment. Similarly, using a system effectively depends on assessing what it does and does not know.
    Dynamic queries about the line of reasoning — As an expert system gathers data and makes intermediate conclusions, users (as well as system builders) need to be able to ask enough questions to follow the line of reasoning. Otherwise the system’s advice appears as an oracle from a black box and is less likely to be acceptable.
    Bandwidth — An expert’s ability to communicate his/her expertise within the framework of an expert system is limited by the restrictions of the framework, the degree to which the knowledge is already well-codified, and the speed with which the expert can create and modify data structures in the knowledge base.
    Knowledge engineer — One way of providing help to experts during construction of the knowledge base is to let the expert communicate with someone who understands the syntax of the framework, the rule interpreter, the process of knowledge base construction, and the practical psychology of interacting with world-class experts. This person is called a “knowledge engineer”.
    Level of performance — Empirical measures of adequacy are still the best indicators of performance, even though they are not sufficient for complete validation by any means. As with testing new drugs by the pharmaceutical industry, testing expert systems may. best bc accomplished by randomized studies and double blind experiments.
    Static evaluation — Because the knowledge base may contain judgmental rules as well as axiomatic truths, logical analysis of its completeness and consistency will be inadequate. However, static checks can reveal potential problems, such as one rule subsuming another and one rule possibly contradicting another. Areas of weakness in a knowledge base can sometimes be found by analysis as well.
    Many applications programs that have the characteristics of expert systems have been developed for analysis problems in a diversity of areas including: chemistry [Buchanan78, Carhart79]; genetics [Stefik78]; protein crystallography [Engelmore79]; physics [Bundy79, Larkin80, Novak80,]; interpretation of oil well logs [Barstow79b, Davis81]; electronics troubleshooting [Addis80, Bennett81b, Brown82, Davis82b, Genesereth81b, Kandt81, Stallman77]; materials engineering [Basden82, Ishizuka81]; mathematics [Brown78, Moses71]; medical diagnosis [Chandrasekaran80, Fagan80, Goriy78, Heisdr78, Horn81, Kaihara78, Lindberg81, Pati181, Pople77, Reggia78, Shortliffe76, Shortliffe81, Swartout77, Szolovits78, Tsotsos81, Weiss79bl; mineral exploration [Duda79]; aircraft identification and mission planning [Engelman79]; military situation assessment [McCo1179, Nii82]; and process control [wamdani82].

    analysis problems are described using many different terms, including:
    l Data Interpretation
    l Explanation of Empirical Data
    l Understanding a Complex of Data (c.g., signal understanding)
    l Classification
    l Situation Assessment
    l Diagnosis (of diseases, equipment failures, etc.)
    l Troubleshooting
    l Fault Isolation
    l Debugging
    l Crisis Management (diagnosis half)

    Synthesis problems arise in many fields including: planning experiments in molecular genetics [Fricdland79, Stefik801, configuring the components of a computer system [McDcrmott80, McDcrrnott81]; scheduling [Fox82, Goldstein79, Lauriere78]; automatic programming [Barstow79a, McCune77]; electronics design [deKleer80, Dincbas80, Sussman78], and chemical synthesis [Gelernter77, Wipke77]. These problems have been called:
    l Planning (or Constructing a Plan of Action)
    l Fault Repair
    l Process Specification
    l Design (of complex devices or of experiments)
    l Configuration
    l Therapy (or therapy planning)
    l Automatic Programming
    l Computer-Aided Chemical Synthesis Planning

    In addition to analysis and synthesis problems, expert systems have been built to provide advice on how to USC a complex system [Anderson76, Bennett79, Gencscreth78, Hewitt75, Krueger81, Rivlin80, Waterman79] or to tutor a novice in the use or understanding of a body of knowledge [Brown82, Clancey79, O’Shea79]. These problems arc partly analytic, since the advice or tutorial must be guided by an analysis of the context, and partly synthetic since the advice must be tailored to the user and the problem at hand.

    The proficiency of an expert system is dependent on the amount of domain-specific expertise it contains. But expertise about interesting problems is not always neatly codified and waiting for transliteration into a program’s internal representation. Expertise exists in many forms and in many places, and the task’ of knowledge engineering includes bringing together what is known about a problem as well as transforming (not merely transcribing) it into the system.

    Note that because it is often easier to design large rule systems as a sequence of independent rulesets to be executed in some order, rule engines sometimes extend the notion of rule execution with mechanisms to orchestrate rulesets – typically called “ruleflows”.

    Another approach is to deploy rulesets in a continuous, event-driven rule engine or agent for tasks such as CEP (Complex Event Processing). Other UML constructs such as state models might be used to provide context for rule execution. Modeling the state of entities over time, and the continuous processing of events, usually requires stateful operation of the rule engine so that information is retained in the rule engine between events

    For business processes represented in a BPMS (Business Process Management System), detailing decision logic within the process diagram often obfuscates the core business processes. Business processes can represent manual (workflow) or automated tasks, with the commonest form of process representation being BPMN (Business Process Modeling Notation).

    The most common format2 for BPM users to represent business rules is the decision table. This provides a common set of condition and action statements, with the table providing different values representing different rules. Some systems map decision tables to a specific algorithm; others will map them to component production rules. Similar models are decision trees and decision graphs.

    Note that decision models output from Predictive Analytics tools may or may not be usefully mapped to production rules. One example might be a segmentation model representing a decision tree segmenting customers for marketing offers, which maps to a decision tree and thence production rules. Alternatively a model type such as a neural net representing a face-recognition feature will not usefully map to production rules. Often such analytics tools generate models in a language called PMML (Predictive Model Markup Language)

    the “why” column in fact drives all the other ones. Why is your data the way it is? Why do you need to know certain “facts” and “terms” (entities and relationships)? Why do you process this way and no the other? Why isn’t this or that allowed? In fact all these questions have always been done. They just weren’t recorded appropriately in our models.

    These tools are for the recording and organizing of the BR.
    • QSS DOORs (a requirements management tool actually) (
    • Rational’s Requisite PRO (idem) (
    • Riverton’s HOW (
    • Usoft’s Teamwork ( • Business Rules Solutions’ BRS Ruletrack (

    #415 Reply


    Now that we have the ShaiviteTemple and TempleCoin Forum together on the Shaivite Temple Website, I can further explain my Vision for the Future of Cryptocurrencies for you, using my website as an example. A very very simple example (not like Google or anything), but an example.

    ANY other Coin that exists right now, is not part of something else. There are not Coins that are part of something larger than themselves. Every single Coin that exists, is on a Website about itself.

    For example, even BiblePay, you can probably find info about it at or, because that Coin (BiblePay) is the most important thing to the people there, because they are Programmers. They did not have an Organization based in the real World until they created a Coin, it was all based in Code. Saved in Notepad++ files.

    TempleCoin is not on, it is on, Temple Coin is not the Mission, it is part of something larger than itself. And that thing larger than itself is not called Bitcoin.

    #429 Reply


    #196715 Reply



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