Is there anyone here know how to find dataset for route optimisation for decision tree? I am doing my final year project and my topic is logistics scheduling system.
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I'm having some doubts about which system should I use for a new software.
No code has been written yet, I'm just breaking apart all the needs and only then start coding.
This will be implemented in a computer company that provides services for other companies, onsite and remotely.
These are my variables:
Number of technicians
Location of customer
Type of problem
Services already scheduled for the technician
Expertise of the technician about the situation
Customer priority
Maybe some are missing, but these are the most important ones.
This job is being done manually, and has humans, we fail to see the best route to be taken sometimes.
Let's say that a customer calls with a printer problem.
First, check which tech knows about printers.
Then, is the tech available? far from the customer? can it be done remotely (software issues)?
Can it be done by another tech who is closer from the customer location?
Does this customer have more priority than the other where the same tech should be going?
Is the technician schedule full? If yes, pass to another printer/hardware tech.
I know my english is not perfect (not my natural language), but I'll try to provide more details or correct the text as needed.
So, my question is this, what kind of approach would you take? Genetic algorithm seems nice for this kind of job, and I also have some experience with GAF and WatchMaker (Java GA Framework). However, when reading the text above, an expert system seems also appropriate.
Have someone done something like this?!I had search for this kind of software and couldn't find anything alike.
Would another approach be better than the two asked?!
Also, I'm building up a table with all the techs capabilities and expertise, with simple rules like, 1 to 5 about each expertise. This is also a decision factor.
Thanks.
Why not do both? Use an expert system (a rule engine) to define your constraints and use a metaheuristic (such as Local Search or Genetic Algorithms) to solve it. The planning engine OptaPlanner (java, open source) does exactly that (by using the rule engine Drools). The architecture look likes this:
Here's a video demonstrating the constraint flexibility on the vehicle routing problem (VRP). Your problem seems to be an advanced variant on VRP (which is a variant on TSP).
Maybe you can start off with TSP,
here http://en.m.wikipedia.org/wiki/Travelling_salesman_problem
I guess it only deals with the distance.
I need very basic help (hopefully) on an Artificial Intelligence Project. My question is very basic so I won't be getting into many details other than what is required. My question starts with a description of the problem, which is to determine the best route to change the temperature. This sounds very vague but to clear things up it is for developing a thermostat. I want to be able to properly control temperature in the most efficient way by using a variety of sensors including outside temperature, humidity, motion in the home (whether someone is home or not), etc.
An example might help as well: Lets say its summer time and the A/C is on. The setpoint is set to 77, but the temperature is 79. However, nobody is home. But the thermostat knows the user will be back home at exactly 4pm in which it is currently 1 pm. And according to weather websites, the outside temperature is going to drop to 77 at 3 pm. The humidity is very high which also plays a big factor. Should the A/C turn on? If so for how long?
My real question is what AI Technique should I look into to solve this kind of problem. I want to simulate something similar situations in python before deploying it on an Android/Java level. I am a very beginner in the area of Artificial Intelligence and want to know the quickest and best manner to approach this problem while making sure I understand everything there is to know about AI at this project level.
I'm not expert, but for this kind of issues prefer using Fuzzy Logic or Expert Systems.
You can use a neural network to predict the status of the thermostat and control it behavior which is based on several parameters. Thermal neural networks may help.
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I am a Computer Science student. I want to do an AI project for my 4th year with two other students. (It's a 5-year degree in my university so I can pursue the same project for two consecutive years if I want to). Our knowledge in AI is very basic at this moment since we'll be specializing in it these coming two years, so a very advanced idea will probably be hard to accomplish. We're not expected to research new untouched soils either, so the more resources the better.
I'm interested in ideas that can benefit people and not just applying algorithms and techniques. I want to do a masters after graduation, but I'm not sure in what field yet.
I'd love to do a medical application or a project that of some use to the handicapped.
Some projects that were already pursued at the university included a project to recognize breast cancer, and to teach sign language to the deaf.
I'm wondering:
1) what other ideas we can work on in those fields?
2) how much will my choice of graduation project affect my application for a masters degree?
3) Is a stocks price prediction expert system too advanced for us?
Thanks a lot.
1) what other ideas we can work on in those fields?
It's amazing to me how little imagination computer science students seem to have. Stackoverflow.com is rife with questions about first projects from beginners and students.
I think that using statistics and data in novel ways, like Peter Norvig's spell checker, would be most interesting and fruitful.
Dr. Peter Norvig is a well-known computer science professor and AI guru. He's the CTO of Google now. Perhaps you can mine a choice out of his writings.
2) how much will my choice of
graduation project affect my
application for a masters degree?
Depends on too many other factors that you don't mention, like your past record as a student, etc. Probably a minor factor, in my opinion. Nobody is admitted to a masters program on the basis of a graduation project. Neither your undergrad project nor a masters thesis is a doctoral dissertation. Don't get them confused.
3) Is a stocks price prediction expert system too advanced for us?
I think stock price prediction is too advanced for anybody. After years of applying Fourier analysis, statistical models, Monte Carlo simulations, etc. if it were possible to do it would have been done.
2) how much will my choice of graduation project affect my application for a masters degree?
If you are applying for a PhD, the faculty in the prospective department tend to favor students who are interested in the research they are doing, or who have demonstrated the ability to do their own research. For a Masters these are not much of an issue, but they can make a little difference.
3) Is a stocks price prediction expert system too advanced for us?
Well, if you did then you would start using it to make money, others would see what you are doing an imitate you so that pretty soon your arbitrage opportunity would be gone.
Still, these type of systems are often built by students in machine learning classes, mostly due to the fact that there is a lot of data freely available and well formatted data on stock prices, so its easy to get starting writing the program. It is a good way to get insight into machine learning algorithms.
1) What other ideas we can work on in those fields?
Find some problem that you are passionate about, will learn something from by tackling it, and is within the scope of your time, effort, and ability. Projects like this are relevant not only for grad school but also when applying for entry-level jobs (even if a few years off still after doing a masters degree)l. It helps to pick something you can put on a resume that shows your level of accomplishment and ability to complete a task.
2) How much will my choice of graduation project affect my application for a masters degree?
The topic choice probably won't matter significantly except perhaps for top-tier programs or if you have notable weaknesses in other admissions criteria. If the latter is true, then a good project may help, but even the latter is uncertain. Masters program admissions I think is generally handled by administrative staff, so they are probably more interested in whether or not you did a project than what the topic is.
3) Is a stocks price prediction expert system too advanced for us?
Yes, a stock price prediction system is far too difficult if you want a system that actually can work reasonably well over anything other than a small training data set.
The market is neither a natural system, a machine, nor even a system of rational collective behavior. Its pricing mechanism is in general irrational: investors/traders may make transactions at prices that are reasonable for them relative to their own decision criteria, but the market as a whole is generally not rational. The market is more an aggregation of behavior rather than collective behavior.
The above alone would make for an intensively difficult problem to solve with AI methods, but beyond that there are issues of problem scale, the amount of training data which is needed, etc.
There are of course a large number of Wall Street trading firms using quantitative methods for high-frequency trading, etc. They are effective, however, because they are focused on narrow problems (price trends over the next few seconds-to-minutes in highly-liquid stocks, S&P index futures, etc.), they put a lot of work into their models and generally are constantly rebuilding the latter on a daily/weekly basis, and they understand the market's nature, i.e., it's largely irrational as a whole and is a competitive, shifting landscape of exploiting the pricing inefficiencies inherent to large money flows.
I would only recommend this problem domain if you have an intense personal interest in financial markets and have already spent a lot of time studying them, are prepared to fail, and are interested in learning a lot. Trying to work on this problem is certainly a good learning opportunity, but it will be hard to achieve any real success except for small problems unless you have many years to devote.
1) what other ideas we can work on in
those fields?
Dr. Russel Greiner has a nice list of possible student projects in machine learning, several of which are related to medicine.
2) how much will my choice of
graduation project affect my
application for a masters degree?
It probably won't matter very much. However, choosing a ridiculously easy project probably won't help. I'm sure that you'll be vetting whatever you choose with your prof, so don't worry about that so much. Find a topic you're passionate about first and foremost.
3) Is a stocks price prediction expert
system too advanced for us?
Yes. Don't bother with that nonsense. The game of Go will be solved before anyone figures out the stock market.
1) what other ideas we can work on in
those fields?
Are there any faculty members at your university that work in the field of bioinformatics? If so, talk to them and see if they give you a suitable project idea that gets you excited. If you decide to take this path, try to enroll in an Intro to Bioinformatics course as it will help you get familiar with the field and generally make things easier.
I'm going into my third year of studies as an AI student and am planning my third year project. I have been considering a recommendation system of some sort. The motivation for this is to gain an understanding of how people evaluate products (what makes the products desirable) and consequently attempt to build a system that would understand this. Currently my thinking is along the lines of a system that would be able to differentiate between different priorities in peoples' likes and dislikes. For instance a person who is environmentally very aware probably wouldn't want to buy products that are not.
So the question is
- What things are most in need of repair/development in the modern web AI systems (Google, Amazon, Last.fm and so on).
My project is limited to about 6 months but I would be interested to hear any thought on the subject.
Some of the things that you might want to look at are Facebook OpenSocial Graph and Google Prediction API.
Long time listener, first time caller.
I'm a full time SE during the day and a full time data mining student at night. I've taken the courses, and heard what our professors think. Now, I come to you - the stackoverflowers, to bring out the real truth.
What is your favorite data mining algorithm and why? Are there any special techniques you've used that have helped you to be successful in the past?
Thanks!
Most of my professional experience involved last-minute feature additions like, "Hey, we should add a recommendation system to this e-Commerce site." The solution was usually a quick and dirty nearest neighbor search - brute force, euclidean distance, doomed to fail if the site ever became popular. But hey, premature optimization and all that...
I do enjoy the idea that data mining can be elegant and wonderful. I've followed the Netflix Prize and played with its dataset. In particular, I like the fact the imagination and experimentation have played such a large part in developing the top ten entries:
Acmehill blog
Acmehill New York Times article
Just a guy in a garage blog
Just a guy in a garage Wired article
So mostly, like a lot of software dev, I think the best algorithm is an open mind and some creativity.
There is a lot of data mining algorithms for different tasks so I found it a little bit hard to choose.
It would say that my favorite data mining algorithm is Apriori because it has inspired hundred of other algorithms and it has several applications. The Apriori algorithm in itself is quite simple. But it has laid the basis for many other algorithms (FPGrowth, PrefixSpan, etc.) that use the so called "Apriori property".
If you can be more specific about the task the data mining algorithm will perform, sure we can help you (classification, clustering, association rules detection, etc)