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In computer science, a rule-based system is used to store and manipulate knowledge to interpret information in a useful way. It is often used in artificial intelligence applications and research. Normally, the term rule-based system is applied to systems involving human-crafted or curated rule sets.
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Is it possible to find one rule applied all over the world??
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If your question is when should a rule (or practice) that seems to work (i.e. produce the desired result in terms of human behaviour) in country A be adopted in country B, read Nancy Cartwright’s and Jeremy Hardie’s short book on ‘Evidence-based Policy’. Even the first few pages will illuminate the issue.
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I have created "if then" rules to be used in fuzzy toolbox of MATLAB using two inputs (say, A and B) and one output (C). When giving input in "rule viewer", in some cases, when I keep the input A constant and reduce the value of B, i get a higher value of output C, or if I increase the value of B, I get a lower value of output C.
Is it logical to get higher output even while reducing one of the inputs, or can I assign weights to different rules to address this issue? If yes, how to assign the weights?
Kindly advise.
Regards
Sanchit
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If we have multiple classifiers and we need to know which one is under-fitting, and which one is overfitting based on performance factors (classification accuracy, and model complexity)
Are there any method to select the dominate classifier (optimal fitting) that balance between the above-mentioned two factors?
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Thank you Salah Mortada for your explanation. However, I would like to draw a figure that compares different methods that used in the experiment result and clearly show the overfitting, under-fitting methods. If you have an example of such an experimental result will be grateful.
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What are the major differences between using the Information Gain and Entropy when we use to determine the credibility or the importance in the classification.
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The information gain is the amount of information gained about a random variable or signal from observing another random variable.
Entropy is the average rate at which information is produced by a stochastic source of data, Or, it is a measure of the uncertainty associated with a random variable.
An example at:
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When Constructing a Fuzzy Cognitive Map based on Experts knowledge you need them to specify IF....THEN interrelationships between concepts so that you can aggregate them and get your initial weight matrix for the FCM . I am currently doing a project where I am applying FCM for predicting the risk of a certain disease.
I have gotten a list of factors from a few doctors but am unable to explain how to form the IF...THEN between concepts.
Below are examples how other papers asked the question formulated the IF...THEN and got the relationships from Doctors:
1. IF small change in C1 THEN medium change in C4
infer: Medium Influence
[This doesn't make any sense in medical scenario say,
IF small change in Cholestrol level THEN medium change in Risk of having Obesity - this makes no sense at all]
OR
2. IF C1 is activated THEN it has Weak Influence on C8
infer : Weak Influence
Can someone help me on how to frame the IF...THEN relationships, so I can get the doctors to give me precise answers and proceed with the construction of FCM.
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By using weights that represents the relative importance of each rule
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Can any one help me to aggregate the result of belief rule expert system?
for example
If (Coughing more than 3 weeks is medium) and (Coughing up blood is High) and (Chest pain is High) and (Fatigue is Low) and (Prolonged fever is medium) and (Lack of appetite is Low) and (Weight loss is High) and (Night sweating is Low) THEN TB suspicion is (High, 0.8), (Medium, 0.2), (Low, 0.0)
is there any equation to aggregate the result TB suspicion is (High, 0.8), (Medium, 0.2), (Low, 0.0) ???
see the attached the paper as you can understood my question
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Sorry, your question need to be simplified more to be understood...
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I am trying to use Model Reference Adaptive Control (MRAC) based on Lyapunov's rule on a system. My question is: should the reference model be assumed or is there a systematic procedure for determining its parameters in relation to the dynamism of the system in question.
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Dear Demilade . . .
Adapting/adjusting the controller parameters for such cases (where plant parameters were not accurately known) is called adaptive control. so as it can be infer, you should have an acceptable approximation of your dynamic system to tune the adaptive control parameters.
recently i have read a paper about this subject which i want to suggest it to you to read it. its about how to design the law for your MRAC.
it might be helpful.
best regards
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Dear all,
I am trying to design an FRBS using Matlab fuzzy logic toolbox. The fuzzy system will be used to predict player's type based on inputs (gameplay data) and a set of rules defined by experts.
I have 6 inputs and 4 outputs (types of players). The given rules do not concern all inputs (specific inputs are used for each player type).
Is it imperative to include all inputs and outputs in a rule? Also is there a min/max of rules that an FRBS should include?
Thank you for your help.
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As far as I know, it is not necessary to have all the inputs and outputs in a rule. For example, in your system a rule can have only 2 inputs which are along with at least one output. This can be applied in the Matlab fuzzy logic toolbox as well.
Hope it could help you.
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Suppose we have two IF rules
(1) If x is IX1 and y is IY1 then z is IZ1.
(2) If x is IX2 and y is IY2 then z is IZ2.
Here, IX1, IX2, IY1, IY2, IZ1, IZ2 are intuitionistic fuzzy sets. How the inferencing will take place?
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Thanks Dr. Azedine Boulmakoul for sharing your research paper. We have seen that and in fact willing to write you to understand the inferencing used in that article. Lot of things you have not disclosed in that paper.
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I have a data set that contains in-game players actions and interactions (stored as metrics). I want to create a recognition system that contains a set of predefined rules and takes as inputs the collected metrics. As an output the system will determine the player type. How to categorize players based on the collected data and the predefined rules? What are the possible algorithms/approaches?
PS: The data set contain only 23 entries (not enough to train the recognition system).
Many thanks.
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Hello Nabila,
First of all, the data set has too much less data points. Hardly any good classifier may work well on this. So, my suggestion is to increase more observations for better classification accuracy.
Thanks,
Sobhan
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Machine learning-based data mining techniques (decision trees, bayesian networks, rule based systems, neural networks,….) aim to build a model from past experience (historical data), that later will be used to predict the output of new cases or to get some insight by using the model as a descriptive tool. Greedy algorithms (hill climbing, gradient descent, …) are perhaps the most commonly approach to the design of machine learning algorithms because their good tradeoff between the quality of the obtained model and the amount of resources (mainly CPU time) they need. However, it is also well known that the use of more complex (in terms of resources) approaches often yields more accurate models. Specially the evolutionary algorithms have been used widely in different tasks of Data Mining: classification, clustering, dependence modelling, regression, time series, discovery of comprehensible and interesting knowledge, scaling up for very large databases, etc. Recently other metaheuristics as ant colony optimization, and tabu search among others, are being used in this area.
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In my opinion GA and NN are similar in term of CPU time. Both take a lot of resources :-)
But currently it is not my subject of interest so maybe new NN algorithms are much better. If you interested, see my old papers:
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Machine learning-based data mining techniques (decision trees, bayesian networks, rule based systems, neural networks,….) aim to build a model from past experience (historical data), that later will be used to predict the output of new cases or to get some insight by using the model as a descriptive tool. Greedy algorithms (hill climbing, gradient descent, …) are perhaps the most commonly approach to the design of machine learning algorithms because their good tradeoff between the quality of the obtained model and the amount of resources (mainly CPU time) they need. However, it is also well known that the use of more complex (in terms of resources) approaches often yields more accurate models. Specially the evolutionary algorithms have been used widely in different tasks of Data Mining: classification, clustering, dependence modelling, regression, time series, discovery of comprehensible and interesting knowledge, scaling up for very large databases, etc. Recently other metaheuristics as ant colony optimization, and tabu search among others, are being used in this area.
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I am working with healthcare data sets. Mostly to identify patient pathways during their treatment journeys. The solutions be accurate, precise and reasonable for clinicians and managers. Tree like methods are more common sense and can have applications with any type of data.
My objective is to get rule based systems using tree algorithms.
Many thanks for your suggestion/ comments in advance.
Tahseen
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Minimum Spanning Trees (MSTs) may be of interest to address overlapped clustering problems:
MSTs may be used in a fuzzy context. Refer to:
  • Vathy-Fogarassy et al., " Minimal Spanning Tree based Fuzzy Clustering ", 2007
Gaussian Mixing Models (GMMs) can then be used for deriving a measure of component overlap. Refer to :
Finally, an overview of existing methods is available from:
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Such that system could be used in real world IT environment. It should not be a dummy system.
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While most academics would suggest Prolog I would say that it depends on the IT infrastructure that you are going to deploy such system and the requirement for coupling with existing systems.
I would also recommend that if you are going to do program such a system for large scale production you should optimize as much as possible.A simlple suggestion is to use hashes and use fast string matching to reduce lookup time(some Prolog implementations use these but you must check to see if the selected Prolog engine does so).
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Kindly suggest me some related resources.
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A good article about interpretability indices:
Cannone, R., Alonso, J. M., & Magdalena, L. (2011). An empirical study on interpretability indexes through multi-objective evolutionary algorithms. In Fuzzy Logic and Applications (pp. 131-138). Springer Berlin Heidelberg.
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Many years ago, Tony Arrott showed me an optimization program he had written using a values-based, as opposed to a rules-based algorithm.  It solved the Travelling Salesman problem (with almost perfect optimization) many times faster and was also able to solve the Inventory problem (turning production facilities on an off optimally in response to fluctuations in demand) in real time on an old Mac. 
He explained that the strategy of "scoring" each solution according to an array of values (such as "Having the item in stock is good," and "Shutting down or restarting a factory is bad.") was a lot easier and faster than trying to obey rules about which action should follow which. 
I'm pretty sure this is how our brains work.  It helps to explain our societal obsession with single-valued logic (e.g., "No price is too great to save a single human life!"or "The only good X is a dead X!" ).  As stupid as it may be, it's a very easy way of reaching a decision.  The danger in overutilizing this method is obvious. 
(Caveat: again, I have no credentials to speak of such matters.  :-)
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I´m using Python programming in my project and I need to classify the provided sentences by rule base methods. I want to define rules conditions and actions. How we can  create and execute these rule bases?
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As Reisel González Pérez  has mentioned, Pyke is the packages which give you the ability of developing rule based systems.
Besides the given links above you can see some Pyke examples here : http://cyberdelia.github.io/pricing/ 
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What are languages for representing knowledge in general? I don't mean some implementing languages such as RuleML or CLIPS. I want some like "Rule" "Frame" and "Procedure". Is there any more?
Is there any research that compare them or try to convert them together?
I want to know any idea.
thanks in advance.  
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Hi Saba et al.,
I am very sure that you are aware of it, but hopefully I can point you in a few directions that you might not be familiar with (at the same time as promoting the technology to others who have not heard of it).
Certainly the largest following for knowledge representation "languages" is the Resource Description Framework (RDF), which is the model that powers both the Semantic Web and the Linked Data web. It's a W3C standard, which is a very well respected consortium. It has plenty of ontologies ready for use in different domains. It is also enriched by a large web of open data in the form of the linked open data (LOD) cloud. SPARQL is the most well used query language, that acts a little bit like SQL does to a relational database. The power of RDF really comes when you pair it with cross-domain hyperlinking, and that's where the Linked Open Data cloud comes in.
I'd very highly recommend everyone looking into it. Here are some links....
* Linked Open Data cloud: http://lod-cloud.net/
* The LOD2 project which promoted LOD to public and private organisations in Europe, and provided them with consultancy, tutorials and a bespoke stack of technologies: http://lod2.eu/
* SKOS (Simple Knowledge Organisation System), an RDF ontology for making frame-like data: http://www.w3.org/2004/02/skos/
* Rule Interchange Format (RIF): http://www.w3.org/2005/rules/wiki/Primer
* TRiG is very important for serialisation of Named Entities and RDF datasets: http://wifo5-03.informatik.uni-mannheim.de/bizer/trig/
* I've used TRiG quite a while ago with XLWrap which transforms Excel spreadsheets into RDF: http://xlwrap.sourceforge.net/
I hope that all helps, and is interesting!
Daniel
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Dear all
how can I find medical rule bases(knowledge base)?Is their implemented languages important for dealing with?what are they?
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The largest and most comprehensive hierarchical vocabulary of medical terms is SNOMED Clinical Terms (SNOMED CT). It is more a knowledge taxonomy than an ontology, but it is currently the best knowledge base of general medical terms there is. 
References:
There are several other medical ontologies under development but they are significantly less expressive (i.e. smaller) than SNOMED. Some are academic and open/free, some are commercial. Additionally, a great deal of these projects is unfinished and the work on them stopped a while back.
  • Ontology for General Medical Science (OGMS), https://code.google.com/p/ogms/
  • GALEN and the "Galen-Core" high-level ontology for medicine.
  • GuideLine Interchange Format (GLIF), a computer-interpretable language for modeling and executing clinical practice guidelines that can be easily integrated into Protege ontology builder.
  • Collaborative Open Ontology Development Environment project (CO-ODE), Medical Informatics Group at the University of Manchester
  • LinKBase, knowledge base of over 1 million language-independent medical concepts featuring an ontology with a formal conceptual description of the medical domain, Language and Computing N.V., Belgium
  • The Medical Ontology Research program, Lister Hill National Center for Biomedical Communications. The aim was to develop a sound medical ontology to enable various knowledge processing applications to communicate with one another.
  • The ONIONS methodology, designed to build the ON9 medical ontology.
  • MedO, a bio-medical ontology developed at the Institute of Formal Ontology and Medical Information Systems, Germany.
My advice would be to first take a look at SNOMED, OGMS, GALEN and - by all means - GLIF.
Kind regards,
Marko
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Please introduce languages and environment for implement a frame base.(same for rule base)
Is there any accessible implemented frame base?(rule base?)
Is there any tools that produce frame base from database?( I mean some thing like WEKA for Tree)
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Dear Saba Ghashghaee madam first read the book on Artificial Intelligence by D W Patterson and another by Rich and Knight. you will understand the basics of frames and rule based system. then you will be able to implement them in any tool.
All the best.
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I am looking for methods that reduce the number of rules in rulebase. methods that fuse the rules and make a new one (not integrating results at the end). I'm searching for papers or anything that could be helpful. Thanks
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Ok. Merging rules can combine either conditions or results.  Combining results would reduce the rule set, e.g., A*B->C and A*D->C becoming A*(B+D)->C, if your allow or-function as well as and conditions.  BUT, combining conditions creates a new rule and the original rules maybe less restrictive, e.g., A->C, B->C becomes A+B->C.  If you can't use or on the left, I'd have to think more about what's possible. . .
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I am looking for methods that reduce the number of rules in a rulebase. methods that fuse the rules and make a new one( not integrating results at the end). I'm searching paper or any thing that could be helpful.
what if rules are not same but similar or related?
thanks  
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You may want to take a look at pruning algorithms, e.g. used in RIPPER. These algorithms reduce rule complexity (similar to penalization in regression analysis)
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I am doing a project to integrate two similar ontologies, and in order to do this I want to use RuleML. The structure of RuleML is in many sites but no code snippets are given. Does anyone have any sample code snippets of RuleML in java?
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Hi Ivy,
Depending on what you want to do, there are two solutions:
(1) If you just want to syntactically process RuleML rule bases from within a Java program, then you can simply use a Java XML parser (I personally prefer DOM4J).
(2) If you want to use a RuleML-compliant rule engine, then I would take a look at the links posted by Nacéra - but note that not all of these rule engines can be integrated in custom applications.
Hope this helps,
- Stratos
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I worked with one process of extracting rules (global) from neural networks. I need some suggestion regarding this process. The weights generated from NN are normalized. From the output layer to hidden layer rules are identified based on the sum of the weights generated at each node(compared with threshold). From the hidden layer pruning is done. From the hidden layer(pruned nodes) nodes rules are identified to the input layer based of the sum of the weights generated.
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@Ahmad : Crisp rules from neural networks can be extracted. There has been many papers on that topic in the last 20 years. Just write on Google Scholar "Rule Extraction" and you will obtain a long list. You can also have a look to my papers on that topic in my ResearchGate publications' page.