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Can a rules file have a dependency on other rules?

I'm using drools rules engine for my new service. I'm looking at cleaning up the rules / making it easier to write. I was hoping to use a rules framework style of coding. I.e. I want a rules file who's sole purpose is to validate the input data (i.e. input list isn't null and contains a specific value). Then when I write new rules files I can just say import this and run the validation before all other rules.
Also, I know I can load multiple rules file into the KieSession. Is it possible to tell it which order to run the rules files in, or which files to skip for each use case? The idea behind this is for performance. Let's say I load up an AWS lambda function with the rules service, I want to have all rules loaded already and have it run the specific one for the use case, instead of loading up a rules file for each call.
Thanks for the help.

You asked
Is it possible to tell it which order to run the rules files in, or which files to skip for each use case?
The answer is yes. The method to do this is called salience. The linked article is a good source to learn about this. This is important because salience can make it possible to change the order of execution of rules.
Hypothetically, let say that you have a program that process transactions based on categories. So, if category == deposit, you want to add to current balance. You have another rule that if category == withdrawal you want to subtract from current balance. BUT, you want to process deposits over withdrawals. Using salience you can guarantee that the deposits rule will fire before the withdrawal rule regardless of order of transaction.
Dependency is kind of related, but not the same. In drools, this is know as Forward or Backward Chaining depending on the order. This is all composed based on a series of facts. For example, if I was to ask a system "is my house on planet Earth?" the conclusion is reached if the following facts exist:
My house is in Fort Worth
Fort Worth is a city in Texas
Texas is a state in the United States
United States is a country on Earth
A direct fact linking the house location to this planet is asserted based on the rules that verify the enumerated facts above. This is done using chaining. Forward or backwards is just how these are processed (top-down or bottom-up). This is in a nutshell to the best of my recollection.

Yes, it is possible by using salience and assigning priorities to some rules so that they execute before other rules. Also, we can make use of inference engine to create facts which can make other rules eligible for execution.

How to expand code/description to a complex object?

I want to present a list of the names/basic attributes of some complex objects (i.e. they are comprised of multiple collections of other objects) in a recycler view, then get the full object on user selection. For example, the top level objects are "Play Scripts", and each contains a number of "Spoken Lines" spoken by one of the "Actors" associated with the Play Script.
I'm trying to use the Android Architecture components to do this and have (using Florian # codinginflow.com 's tutorials) successfully used Room to create a simplified Play_Script class, DAO and Repository. I've also created some basic REST web services in ASP.Net which can serve up data from a MySQL db.
It strikes me that the path that I am going down will perform poorly and use excessive network bandwidth getting lots of data that I won't use. I'm getting every Play Script (including its Spoken Lines etc) just so that I have the Play Script "Name" and "Description" attributes to populate the Recycler.
In the olden days, I'd just "SELECT ID, Name, Description FROM Play_Script" and once the user had made their choice, I'd use the ID as the key to get everything else that I needed. I suspect that I'm missing something fundamental in the design of my data entities but can't come up with any keywords that would let me search for examples of this common sort of task being done well (/at all).
Please can you help this SO noob with his 1st question?
Cheers,
Z
Update 15 May:
Though I haven't had a response, from what I've been reading in recent weeks (e.g. re Dependency Injection) I suspect that there is no blanket approach for this sort of thing in Android development. It appears that people generally either retrieve extensive data and then use what they require or else build multiple Web Service APIs to return sparse data that includes keys that the client can use to expand when required. So, for example you might make both a "plays_light" and a "plays_detail" Get API.

My solution has been exactly as my May update - i.e. to extend the web API and offer a number of similar calls that return varying granularities of information. It's not particularly elegant and I suspect there may be better ways but it works. In general, I'm finding that the user tends to need less detail in the parent entities and more as we get to individual children/grandchildren.
I do now realise why some apps are so slow though: It's easy to be lazy in the web service design and just return loads of data - only a fragment of which will be used by the client - and justify this by convincing yourself that single API will be universally applicable and thus easier for whoever picks up my code down the line to understand.
Again, it could be my inexperience but I find the local caching of relational data on the Android side retrieved through the API calls quite clunky - lots of storing foreign keys and then re-parsing json to get data into the SQLite tables. I'd hoped Dagger would have been more useful in simplifying this than it has turned out to be so far. I actually unravelled a whole load of Dagger-related code just to preserve my sanity. Not sure I was entirely successful!
Better answers are still very much welcome.
Z

How to make career guidance system intelligent

Well at-last I am working on my final year project which is Intelligent web based career guidance system the core functionality of my system is
Recommendation System
Basically our recommendation system will carefully examine user preferences by taking Interest tests and user’s academic record and on the basis of this examined information it will give user the best career options i.e the course like BS Computer Science etc. .
Input of the recommendation system will be the student credentials and Interest test and in interest test the questions will be given according to user academic history and the answers that he is giving in the test, so basically test will not be asking same questions from everyone it will decide on real time about what to ask from which user according to rules defined by the system.
Its output will be the option of fields which will be decided on the basis of Interest test.
Problem
When I was defending my scope infront of committee they said "this is simple if-else" this system is not intelligent.
My question is which AI technique or Algorithm could be use to make this system intelligent. I have searched a lot but papers related to my system are much more superficial they are just emphasizing on idea not on methodology.
I want to do all my work in Java. It is great if answer is technology specific.
You people can transfer my question to any other stackexchange site if it is not related to SO Q&A criteria.
Edit
After getting some idea from answers I want to implement expert system with rule based and inference engine. Now I want to be more clear on technology aspect to implement rule based engine. After searching I have found Drools to be best but Is it also compatible with web applications? And I also found Tohu to be best dynamic form generator (as this is also need of my project). can I use tohu with drools to make my web application? Is it easy to implement this type of system or not?

If you have a large amount of question, each of them can represent a feature. Assuming you are going to have a LOT of features, finding the series of if-else statements that fulfills the criteria is hard (Recall that a full tree with n questions is going to have 2^n "leaves" - representing 2^n possible answers for these questions, assuming each question is yes/no question).
Since hard programming the above is not possible for a large enough (and probably a realistic size n - there is a place for heuristical solutions one of those is Machine Learning, and specifically - the classification problem. You can have a sample of people answering your survey, with an "expert" saying what is the best career for them, and let an algorithm find a classifier for the general problem (If you want to convert it into a series of yes-no questions automatically, it can be done with a decision tree, and an algorithm like C4.5 to create the tree).
It could also be important to determine - which questions are actually relevant? Is a gender relevant? Is height relevant? These questions as well can be answered using ML algorithms with feature selection algorithms for example (one of these is PCA)
Regarding the "technology" aspect - there is a nice library in java - called Weka which implement many of the classification algorithms out there.
One question you could ask (and try to find out in your project) which classification algorithm will be best for this problem? Some possibilities are The above mentioned C4.5, Naive Bayes, Linear Regression, Neural Networks, KNN or SVM (which usually turned out best for me). You can try and back your decision which algorithm to use with a statistical research and a statistical proof which is better. Wilcoxon test is the standard for this.
EDIT: more details on point 2:
In here an "expert" can be a human classifier from the field of HR
that reads the features and classifies the answers. Obtaining this
data (usually called the "training data") is hard and expansive
sometimes, if your university has an IE or HR faculty, maybe they
will be willing to help.
The idea is: Gather a bunch of people who first answer your survey. Then, give it to a human classifier ("expert") which will chose what is the best career for this person, based on his answers. The data with the classification given by the expert is the input of the learning algorithm, its output will be a classifier.
A classifier is a function itself, that given answers to a surveys - predicts what is the "classification" (suggested career) for the person who did this survey.
Note that once you have a classifier - you do not need to maintain the training data any more, the classifier alone is enough. However, you should have your list of questions and the answers for these questions will be the features provided to the classifier.

All you have to do to satisfy them is create a simple learning system:
Change your thesis terminology so it is described as "learning the best career" instead of using the word "intelligent". Learning is a form of artificial intelligence.
Create a training regime. Do this by giving the questionnaire to people that already have careers and also ask questions to find out how satisfied they are with their career. That way your system can train on what makes a good career match and what makes a bad one.
Choose a learning system to absorb the data from (2). For example, one source of ideas might be this recent paper: http://journals.cluteonline.com/index.php/RBIS/article/download/4405/4493. Product sum networks are cutting edge in AI and apply well to expert-system-like problems.
Finally, try to give a twist to whatever your technology is to make it specific to your problem.

In my final project, I had some experience with Jena RDF inference engine. Basically, what you do with it is create a sort of knowledge base with rules like "if user chose this answer, he has that quality" and "if user has those qualities, he might be good for that job". Adding answers into the system will let you query his current status and adjust questions accordingly. It's pretty easy to create a proof of concept with it, it's easier to do than a bunch of if-else, and if your professors worship prolog-ish style things, they'll like it.

As #amit suggested, Bayesian analysis can provide you guidance on the next question to ask. Another pitfall of dynamic tests is artificial thresholds ("if your score is 28, you are in this category, if your score is 27, you are not"), a problem which fuzzy logic can help address. Another benefit of fuzzy logic is that adding a new category is relatively easy, since the domain expert is only asked to contribute qualitative assessments, not quantitative thresholds.

A program is never more intelligent than the person who wrote it. So, I would first use the collective intelligence that has been built and open sourced already.
Pass your set of known data points as an input to Apache Mahout's PearsonCorrelationSimilarity and use the output to predict which course is the best match. In addition to being open source and scalable, you can also record the outcome and feed it back to the system to improve the accuracy over time. It is very hard to match this level of performance because it is a lot easier to tweak an out of the box algorithm or replace it with your own than it is to deal with a bunch of if else conditions.
I would suggest reading this book . It contains an example of how to use PearsonCorrelationSimilarity.
Mahout also has built in recommender algorithms like NearestNeighborClusterSimilarity
that can simplify your solution further.
There's a good starter code in the book. You can build on it.
Student credentials, Interest Test Questions and answers are inputs. Career choice is the output that you can co-relate to the input. Now that's a very simplistic approach but it might be ok to start with. Eventually, you will have to apply the classifier techniques that Amit has suggested and Mahout can help you with that as well.

Drools can be used via the web, but watch out; it can be a bit of beast to configure and is likely serious overkill for your application. It is an 'enterprise' type of solution focused around rule management, rather than rule execution.
Drools is an "IF-THEN" system, and pretty much all rules engines use the Rete algorithm. http://en.wikipedia.org/wiki/Rete_algorithm - so if your original question is about how not to use an IF-THEN system, Drools is not the right choice. Now, there is a Solver and Planner part of Drools that are not IF-THEN algorithms, but this is not the main Drools algorithm.
That said, it seems like a reasonable choice for your application. Just don't expect it to be considered an 'intelligent' system by those who deem themselves as experts. Rules engines are typically used to codify (that is, make software of) the rules and regulations of business, such as 'should you be approved for a mortgage' or 'how much is your car insurance' and so on. 'what job you should do' is a reasonable application of the same.
If you want to add more AI like intelligence here are a few ideas
Use machine learning to get feedback from the user about earlier recommendations. So, if someone likes or hates a suggestion, add that back in as a feature of the person. You are now doing some basic feedback/reinforcement learning (bayes, neural nets) to try to better classify the person to the career.
Consider the questions you ask the person. Do you need to ask all of the questions? If you can alter the flow of questions based on their responses (by estimating what kind of person they are) then you are trying to learn the series of questions that gives the most useful knowledge for a recommendation.
If you want specific software, look at Weka http://www.cs.waikato.ac.nz/ml/weka/ - it has many great algorithms for classifying. And it is a Java library, so you can easily use it within a web application.
Good luck.

I'm looking for a graph-database for a Java/Python centric organization [closed]

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At work we want our next generation product to be based on a graph database. I'm looking for suggestions as to what database engine might be appropriate for our new project:
Out product is intended to keep track of a large number of prices for goods. Here's a simplistic example of what it does - supposing you wanted to estimate the price of gasoline in the UK - you know that Gasoline is refined from crude-oil. If you new the price of crude oil in the UK you could estimate the price of anything simply by adding the cost of refining, transporting (etc). Actually things are more complex because there are a number of sources of crude-oil and hundreds of refined oil products. The prices of oil products can be affected by the availability of other energy sources (e.g. nuclear, wind, natural gas) and the demand. It's kind of complex!
The idea is that we want to model the various inter-related goods and their costs of refining, transportation (etc) as an asyclic directed graph. The idea being, when an event causes a price to change then we want to be quickly able to determine what kinds of things are affected and re-calculate those prices ASAP.
Essentially we need a database which can represent the individual commodities as nodes in the graph. Each node will store a number of curves and surfaces of information pertaining to the product.
We want to represent the various costs & transformations (e.g. refining, transportation) as labels on the edges. As with the nodes, the information we want to store could be quite complex - not just single values but curves and surfaces.
The calculations we do are all linear with respect to the size of the objects, however since the graph could be very big we need to be able to traverse the graph very quickly.
We are Java and Python centric - ideally we are after a product that runs on the JVM but has really good APIs for both Python and Java. We don't care so much about other languages... but .Net would be nice to have (even though it might be years before we get round to doing something with it).
We'd definitely like something which was high-performance - but more importantly the system needs to have a degree of hardware fault tolerance. For example, we'd like to distribute the database across a number of physical servers. In the event that any of the servers go down we'd like to be able to continue without an interruption.
Oh, and we are really lazy. We dont want to spend much time writing infrastructure - so if the database came with tools that allow us to do as much as possible of this kind of thing with very little work that's fine by us. It would also be a real bonus if there was a grid technology associated with the graph DB, that way we could push a sequence of re-calculate jobs onto a compute grid and have much of our calculation done in paralell.
So, that's a description of the kind of thing we want to build. What I want to know is whether there are any mature technologies which will help us achieve this. As I mentioned before, we have a preference for Python & JVM, however if the technology is really good and comes with great bindings for Python + Java we'd consider almost anything.

Neo4J is the most mature graphDB I know of - and is java, with bindings for python too, or REST

Design for a Debate club assignment application

For my university's debate club, I was asked to create an application to assign debate sessions and I'm having some difficulties as to come up with a good design for it. I will do it in Java. Here's what's needed:
What you need to know about BP debates: There are four teams of 2 debaters each and a judge. The four groups are assigned a specific position: gov1, gov2, op1, op2. There is no significance to the order within a team.
The goal of the application is to get as input the debaters who are present (for example, if there are 20 people, we will hold 2 debates) and assign them to teams and roles with regards to the history of each debater so that:
Each debater should debate with (be on the same team) as many people as possible.
Each debater should uniformly debate in different positions.
The debate should be fair - debaters have different levels of experience and this should be as even as possible - i.e., there shouldn't be a team of two very experienced debaters and a team of junior debaters.
There should be an option for the user to restrict the assignment in various ways, such as:
Specifying that two people should debate together, in a specific position or not.
Specifying that a single debater should be in a specific position, regardless of the partner.
If anyone can try to give me some pointers for a design for this application, I'll be so thankful!
Also, I've never implemented a GUI before, so I'd appreciate some pointers on that as well, but it's not the major issue right now.
Also, there is the issue of keeping Debater information in file, which I also never implemented in Java, and would like some tips on that as well.

This seems like a textbook constraint problem. GUI notwithstanding, it'd be perfect for a technology like Prolog (ECLiPSe prolog has a couple of different Java integration libraries that ship with it).
But, since you want this in Java why not store the debaters' history in a sql database, and use the SQL language to structure the constraints. You can then wrap those SQL queries as Java methods.

There are two parts (three if you count entering and/or saving the data), the underlying algorithm and the UI.
For the UI, I'm weird. I use this technique (there is a link to my sourceforge project). A Java version would have to be done, which would not be too hard. It's weird because very few people have ever used it, but it saves an order of magnitude coding effort.
For the algorithm, the problem looks small enough that I would approach it with a simple tree search. I would have a scoring algorithm and just report the schedule with the best score.
That's a bird's-eye overview of how I would approach it.