Business Rules Engines
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
http://chatbotfriends.altervista.org/Download.html
https://www.chatbots.org/platform/download/
https://docs.botframework.com/en-us/downloads/
https://github.com/Microsoft/BotFramework-Emulator
https://dev.botframework.com/
https://www.nowassistant.com/digital-assistant/bots-and-integrations
https://www.technologyreview.com/s/603383/new-uk-surveillance-law-will-have-worldwide-implications/
https://slack.com/apps/category/At0MQP5BEF-bots
http://www.cleverscript.com/demos/virtual-assistant-demo/
https://www.forbes.com/sites/parmyolson/2016/05/09/could-chat-bots-replace-human-jobs-facebook/#6c447f9f7564
http://www.softwebsolutions.com/resources/5-jobs-where-bots-will-replace-humans.html
http://www.news18.com/news/tech/humans-vs-bots-will-bots-replace-human-labour-soon-1346001.html
http://www.theverge.com/2016/4/7/11380470/amy-personal-digital-assistant-bot-ai-conversational
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
http://ccscjournal.willmitchell.info/Vol7-91/No5/Bin%20Cong.pdf
Inference Engine
https://en.wikipedia.org/wiki/Inference_engine
Rule1: Human(x) => Mortal(x)
Bayesian Statistics
https://en.wikipedia.org/wiki/Bayesian_statistics
Bayesian Network
https://en.wikipedia.org/wiki/Bayesian_network
Knowledge Representation
https://en.wikipedia.org/wiki/Knowledge_representation_and_reasoning
https://www.cse.buffalo.edu/~shapiro/Courses/CSE563/Slides/krrSlides.pdf
https://web.stanford.edu/class/cs227/Lectures/lec01.pdf
http://dai.fmph.uniba.sk/~sefranek/kri/handbook/handbook_of_kr.pdf
http://stpk.cs.rtu.lv/sites/all/files/stpk/lecture_7.pdf
Knowledge Engineering
http://ai.uom.gr/dsklavakis/en/mathesis/journals/The%20MATHESIS%20Meta-Knowledge%20Engineering%20Framework.pdf
https://pdfs.semanticscholar.org/47c9/c4ea22d4d4a286e74ed1f8b8f62d9bea54fb.pdf
http://infolab.stanford.edu/~stefan/paper/2000/ios_2000.pdf
http://icaps07-satellite.icaps-conference.org/ickeps/OWL-ICKEPS07_CamRdy.pdf
http://liris.cnrs.fr/robert.laurini/text/1-s2.0-S1045926X13000669-main.pdf
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.278.7295&rep=rep1&type=pdf
http://ceur-ws.org/Vol-1070/kese9-procs.pdf
http://dai.fmph.uniba.sk/~sefranek/kri/handbook/chapter25.pdf
http://www.aiai.ed.ac.uk/project/ix/project/kingston/phdjkk.pdf
Rule Based Expert Systems
https://github.com/akhilnair95/RuleBasedSystem
https://github.com/Sorosliu1029/Rule-based_Expert_System
https://github.com/dniwrallets/RuleBasedExpertSystem
https://github.com/philipbrown/rule-based-expert-system-ruby
https://github.com/TheRusskiy/ExpertSystem
https://github.com/laimonassutkus/RuleBasedExpertSystemsWithCLIPS
https://github.com/levu48/rulemod
https://github.com/rmoswela/ExpertSystem
https://github.com/ubarkai/rulu
https://github.com/grkpranaykumar/Mobile-Phone-recommendation-expert-system
Expert Systems
http://www.exsys.com/online.html
https://www.diagnose-me.com/
http://www.openlearningworld.com/books/Expert%20Systems/Expert%20Systems/
http://www.openlearningworld.com/innerpages/Expert%20Systems.htm
https://www.quora.com/Is-there-a-website-that-lets-you-create-an-expert-system-online-for-free
http://naimath.sourceforge.net/
https://en.wikipedia.org/wiki/List_of_computer-assisted_organic_synthesis_software
https://pdfs.semanticscholar.org/8705/78736d52b65b00a97b983d8759f4b2a46cd7.pdf
http://www.springer.com/us/book/9783642840500
http://www.springer.com/us/book/9781461284055
https://www.quora.com/What-is-a-good-open-source-expert-system
Mycin
Inline image 1
Rule Based Expert Systems
http://www.softcomputing.net/fuzzy_chapter.pdf
http://i.stanford.edu/pub/cstr/reports/cs/tr/82/926/CS-TR-82-926.pdf
http://ftp.it.murdoch.edu.au/units/ICT219/Lectures/03B219Lect_Week05.pdf
http://www.theimpactinstitute.org/Teaching/CS4725/rbs.pdf
http://staff.informatics.buu.ac.th/~krisana/975352/handout/Lecture02.pdf
https://pdfs.semanticscholar.org/c848/fea059184c3f0edc2e1f3534a34465f9737e.pdf
https://pdfs.semanticscholar.org/d0ea/2a37ebfb1323e11e809cf407904db0d4680a.pdf
https://pdfs.semanticscholar.org/85c3/7c3cd9322cf388d6b79bb9717ef1e219a39f.pdf
http://csd.ijs.si/papa/courses/08_LopezPartitioning.pdf
https://www.cs.ru.nl/P.Lucas/hep2bel.pdf
http://file.scirp.org/pdf/TI20120200008_45341734.pdf
http://williamdurand.fr/papers/Inferring%20models%20with%20rule-based%20expert%20systems.pdf
http://nptel.ac.in/courses/106105078/pdf/Lesson%2018.pdf
BRE
https://github.com/ddossot/NxBRE
https://github.com/nara/RulesEngine
https://github.com/runxc1/MicroRuleEngine
https://github.com/rsamec/business-rules-engine
https://github.com/azach/rules
https://github.com/kislayverma/Rulette
https://github.com/mallond/rules
https://github.com/CacheControl/json-rules-engine
https://github.com/hoaproject/Ruler
https://github.com/jruizgit/rules
http://it.nv.gov/uploadedFiles/ITnvgov/Content/Sections/IT-Investments/Lifecycle/BABOKV1_6.pdf
http://agilityconsulting.com/resources/Strategic%20Agility%20Institute/OracleBusiness%20Rules.pdf
http://www.equifax.com/pdfs/corp/Celent_Case_Study_0.pdf
http://www.jaqm.ro/issues/volume-4,issue-3/pdfs/mircea_andreescu.pdf
http://ipma-wa.com/prof_dev/2011/Gladys_Lam_Ten_Mistakes.pdf
http://subs.emis.de/LNI/Proceedings/Proceedings91/GI-Proceedings-91-3.pdf
http://www.bcs.org/upload/pdf/business-analysis-techniques.pdf
http://www.kathleenhass.com/Whitepapers/The_Business_Analyst.pdf
http://www.hau.gr/resources/toolip/doc/2016/02/03/business-analysis_2016.pdf
http://docs.sbs.co.za/F1_Larson_Wood.pdf
http://www.buildingbusinesscapability.com/presentations/2014/1601.pdf
Diagrams
http://epf.eclipse.org/wikis/abrd/practice.tech.abrd.base/guidances/practices/resources/legacy2bre.JPG
https://i-msdn.sec.s-msft.com/dynimg/IC19023.jpeg
http://docs.oracle.com/cd/E36909_01/user.1111/e10228/img/rulesession.gif
https://wiki.kuali.org/download/attachments/307464583/krms-architecture.png?version=16&modificationDate=1321303971000&api=v2
https://wiki.kuali.org/download/attachments/307464583/KRMS%20-%20Architecture%20-%20Draft%201.png?version=16&modificationDate=1289547751000&api=v2
http://openrules.com/Site/images/RuleSOA.jpg
http://support.sas.com/documentation/cdl/en/brsag/67259/HTML/default/images/architectversion21brmanddcm.png
http://openrules.com/images/RuleSolver2.jpg
http://www.ibm.com/support/knowledgecenter/de/SS6MTS_7.1.1/com.ibm.websphere.ilog.jrules.doc/Content/Business_Rules/Documentation/_diagrams/JRules_Product_overview/_media/architecture_default.png
History of Bots and Microsoft Tay
http://politicalbots.org/wp-content/uploads/2016/10/NeffNagy.pdf
Dialog System
https://en.wikipedia.org/wiki/Dialog_system
http://www.cs.cmu.edu/~stef/thesis/thesis.pdf
https://www.speech.kth.se/~gabriel/thesis/chapter2.pdf
https://www.cis.upenn.edu/~mkearns/papers/cobotDS.pdf
https://en.wikipedia.org/wiki/Verbot
https://en.wikipedia.org/wiki/ELIZA
https://en.wikipedia.org/wiki/AIML
https://en.wikipedia.org/wiki/Artificial_Linguistic_Internet_Computer_Entity
https://en.wikipedia.org/wiki/Jabberwacky
https://en.wikipedia.org/wiki/Evolutionary_algorithm
https://en.wikipedia.org/wiki/Automated_online_assistant
https://en.wikipedia.org/wiki/Markov_chain
https://en.wikipedia.org/wiki/SitePal
https://en.wikipedia.org/wiki/Xiaoice
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 <condition> THEN <action>
IF the ‘traffic light’ is ‘green’ THEN the action is go
IF the ‘traffic light’ is ‘red’ THEN the action is stop
IF <antecedent 1> IF <antecedent 1>
AND <antecedent 2> OR <antecedent 2>
. .
. .
AND <antecedent n> OR <antecedent n>
THEN <consequent> THEN <consequent>
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 <attribute> of <object> is <value> with certainty <CD
The basic EMYCIN syntax for a rule is:
PREMISE: ($AND (<clause1>…<clause-n>))
ACTION: (CONCLUDE <new-fact> <CF>)
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.‘.
Information is not knowledge.
- Albert Einstein