I'm not sure if this is the right place to ask this, but here goes.
I have been a programmer for about 12 years now with experience in php, java, c#, vb.net and asp. I have always been rather intrigued about Artificial Intelligence. I think it is really the ultimate challenge for any developer.
I have written many simple scripts to play games but nothing compared to what I want to do next. I want to write an AI program that will play a MMORTSG (Massively Multiplayer Online Real Time Strategy Game. I have been searching through many AI techniques but none seem to tackle the problems that I know I will face:
Problems I can foresee:
The game has no "win situation", instead, the best strategy is the one that has the greatest growth in comparison to that of other players. Growth is determined by 3 factors, economy, military and research.
Parts of the game state are unpredictable. Other players can attack me at random.
The game is time based and actions take time. ie. Building a new building make take several hours. While that building is being built, no other buildings can be built.
All the AI systems I have researched, require some sort of "Winning Function" to test if the AI has found an end. Where in my situation it would more likely be something like "I have X, Y, Z options, the best one is X".
ps. Sample code would be awesome. Even Psuedo would be great.
I've seen a few applications of Artificial Intelligence in the Gaming area, but most of this was for FPS, MMORPGs and RTS games. The genre type that you appear to be relating to sounds similar to 'Clash of Clans', where research, military and economy as well as random attacks occur over a random period of time, and runs over an endless period of time.
It seems that a model would be used at key points in the game (building is finished, or research is available, or castle is full) to make strategic decisions for progression. Perhaps a Genetic Algorithm could be applied at key moments determine a suitable sequence of future steps. A modular neural network could be defined to decide the logical Growth factor to take, but training such a network may be difficult as the game rules can change over time (either from previously unknown resources, research options, military and even game updates). Scripts are quite common as well in the MMORPG genre, but defining the manual rules could also be difficult without knowing all of the available options. The fact is that there are so many ways that your challenge can be addressed that it would be difficult to give a clear-cut answer to your problem, let alone the code or psudocode.
Looking briefly over the problem, it appears that the contributing factors to the problem would be current economic state, current military state, current research state, time lost if saving for next upgrade, time required to build next upgrade, cost of upgrade as well as other unknown factors.
Given that the problem has no clear winning objectives, I guess it is a matter of maintaining a healthy balance between the three growth factors. But how does one define the balance? Is research more important? Should you always have money, or just save enough for the next planned upgrade? Should military be as large as possible?
The challenge that you are placing before yourself is quite adventurous, but I would recommend taking on smaller challenges if you are not yet familiar with the models that AI have to provide. There are quite a number of Gaming Applications for AI resources available to inspire your model (including ziggystar's examples noted above).
Related
I need help to chose a project to work on for my master graduation, The project must involve Ai / Machine learning or Business intelegence.. but if there is any other suggestion out of these topics it is Ok, please help me.
One of the most rapid growing areas in AI today is Computer Vision. There are many practical needs where the results of your Master Thesis can be helpful. You can try research something like Emotion Detection, Eye-Tracking, etc.
An appropriate work for a MS in CS in any good University can highlight the current status of research on this field, compare different approaches and algorithms. As a practical part, it makes also a lot of fun when your program recognizes your mood properly :)
Netflix
If you want to work more on non trivial datasets (not google size, but not trivial either and with real application), with an objective measure of success, why not working on the netflix challenge (the first one) ? You can get all the data for free, you have many papers on it, as well as pretty good way to compare your results vs other peoples (since everyone used exactly the same dataset, and it was not so easy to "cheat", contrary to what happens quite often in the academic literature). While not trivial in size, you can work on it with only one computer (assuming it is recent enough), and depending on the type of algorithms you are using, you can implement them in a language which is not C/C++, at least for prototyping (for example, I could get decent results doing things entirely in python).
Bonus point, it passes the "family" test: easy to tell your parents what you are working on, which is always a pain in my experience :)
Music-related tasks
A bit more original: something that is both cool, not trivial but not too complicated in data handling is anything around music, like music genre recognition (classical / electronic / jazz / etc...). You would need to know about signal processing as well, though - I would not advise it if you cannot get easy access to professors who know about the topic.
I can use the same answer I used on a previous, similar question:
Russ Greiner has a great list of project topics for his machine learning course, so that's a great place to start.
Both GAs and ANNs are learners/classifiers. So I ask you the question, what is an interesting "thing" to learn? Maybe it's:
Detecting cancer
Predicting the outcome between two sports teams
Filtering spam
Detecting faces
Reading text (OCR)
Playing a game
The sky is the limit, really!
Since it has a business tie in - given some input set determine probable business fraud from the input (something the SEC seems challenged in doing). We now have several examples (Madoff and others). Or a system to estimate investment risk (there are lots of such systems apparently but were any accurate in the case of Lehman for example).
A starting point might be the Chen book Genetic Algorithms and Genetic Programming in Computational Finance.
Here's an AAAI writeup of an award to the National Association of Securities Dealers for a system thatmonitors NASDAQ insider trading.
Many great answers posted already, but I wanted to add my 2 cents.There is one hot topic in which big companies all around are investing lots of resources into, and is still a very challenging topic with lots of potential: Automated detection of fake news.
This is even more relevant nowadays where most of us are connecting though social media and there's a huge crisis looming over.
Fake news, content removal, source reliability... The problem is huge and very exciting. It is as I said challenging as it can be seen from many perspectives (from analising images to detect fakes using adversarial netwotks to detecting fake written news based on text content (NLP) or using graph theory to find sources) and the possbilities for a research proyect are endless.
I suggest you read some general articles (e.g this or this) or have a look at research articles from the last couple of years (a quick google seach will throw you a lot of related stuff).
I wish I had the opportunity of starting over a project based on this topic. I think it's going to be of the upmost relevance in the next few years.
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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.
There are many papers about ranged combat artificial intelligences, like Killzones's (see this paper), or Halo. But I've not been able to find much about a fighting IA except for this work, which uses neural networs to learn how to fight, which is not exactly what I'm looking for.
Occidental AI in games is heavily focused on FPS, it seems! Does anyone know which techniques are used to implement a decent fighting AI? Hierarchical Finite State Machines? Decision Trees? They could end up being pretty predictable.
In our research labs, we are using AI planning technology for games. AI Planning is used by NASA to build semi-autonomous robots. Planning can produce less predictable behavior than state machines, but planning is a highly complex problem, that is, solving planning problems has a huge computational complexity.
AI Planning is an old but interesting field. Particularly for gaming only recently people have started using planning to run their engines. The expressiveness is still limited in the current implementations, but in theory the expressiveness is limited "only by our imagination".
Russel and Norvig have devoted 4 chapters on AI Planning in their book on Artificial Intelligence. Other related terms you might be interested in are: Markov Decision Processes, Bayesian Networks. These topics are also provided sufficient exposure in this book.
If you are looking for some ready-made engine to easily start using, I guess using AI Planning would be a gross overkill. I don't know of any AI Planning engine for games but we are developing one. If you are interested in the long term, we can talk separately about it.
You seem to know already the techniques for planning and executing. Another thing that you need to do is predict the opponent's next move and maximize the expected reward of your response. I wrote a blog article about this: http://www.masterbaboon.com/2009/05/my-ai-reads-your-mind-and-kicks-your-ass-part-2/ and http://www.masterbaboon.com/2009/09/my-ai-reads-your-mind-extensions-part-3/ . The game I consider is very simple, but I think the main ideas from Bayesian decision theory might be useful for your project.
I have reverse engineered the routines related to the AI subsystem within the Street Figher II series of games. It does not incorporate any of the techniques mentioned above. It is entirely reactive and involves no planning, learning or goals. Interestingly, there is no "technique weight" system that you mention, either. They don't use global weights for decisions to decide the frequency of attack versus block, for example. When taking apart the routines related to how "difficulty" is made to seem to increase, I did expect to find something like that. Alas, it relates to a number of smaller decisions that could potentially affect those ratios in an emergent way.
Another route to consider is the so called Ghost AI as described here & here. As the name suggests you basically extract rules from actual game play, first paper does it offline and the second extends the methodology for online real time learning.
Check out also the guy's webpage, there are a number of other papers on fighting games that are interesting.
http://www.ice.ci.ritsumei.ac.jp/~ftgaic/index-R.html
its old but here are some examples
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Our brain consists of billions of neurons which basically work with all the incoming data from our senses, handle our consciousness, emotions and creativity as well as our hormone system, etc.
So I'm completely new to this topic but doesn't each neuron have a fixed function? E.g.: If a signal of strength x enters, if the last signal was x ms ago, redirect it.
From what I've learned in biology about our nerves system which includes our brain because both consist of simple neurons, it seems to me as our brain is one big, complicated computer.
Maybe so complicated that things such as intelligence and cognition become possible?
As the most complicated things about a neuron pretty much are the chemical aspects on generating an electric singal, keeping itself alive, and eventually segmenting itself, it should be pretty easy emulating some on a computer, or?
You won't have to worry about keeping your virtual neuron alive, or?
If you can emulate a single neuron on a computer, which shouldn't be too hard, could you theoretically emulate more than 1000 billions of them, recreating intelligence, cognition and maybe even creativity?
In my question I'm leaving out the following aspects:
Speed of our current (super) computers
Actually writing a program for emulating neurons
I don't know much about this topic, please tell me if I got anything wrong :)
(My secret goal: Make a copy of my brain and store it on some 10 million TB HDD and make someone start it up in the future)
A neuron-like circuit can be built with a handful of transistors. Let's say it takes about a dozen transistors on average. (See http://diwww.epfl.ch/lami/team/vschaik/eap/neurons.html for an example.)
A brain-sized circuit would require 100 billion such neurons (more or less).
That's 1.2 trillion transistors.
A quad-core Itanium has 2 billion transistors.
You'd need a server rack with 600 quad-core processors to be brain-sized. Think $15M US to purchase the servers. You'll need power management and cooling plus real-estate to support this mess.
One significant issue in simulating the brain is scale. The actual brain only dissipates a few watts. Power consumption is 3 square meals per day. A pint of gin. Maintenance is 8 hours of downtime. Real estate is a 42-foot sailboat (22 Net Tons of volume as ships are measured) and a place to drop the hook.
A server cage with 600 quad-core processors uses a lot more energy, cooling and maintenance. It would require two full-time people to keep this "brain-sized" server farm running.
It seems simpler to just teach the two people what you know and skip the hardware investment.
Roger Penrose presents the argument that human consciousness is non-algorithmic, and thus is not capable of being modeled by a conventional Turing machine-type of digital computer. If it's like that you can forget about building a brain with a computer...
Simulating a neuron is possible and therefore theoretically simulating a brain is possible.
The two things that always stump me as an issue is input and output though.
We have a very large number of nerve endings that all provide input to the brain. Without them the brain is useless. How can we simulate something as complicated as the human brain without also simulating the entire human body!?!
Output, once the brain has "dealt" with all of the inputs that it gets, what is then the output from it? How could you say that the "copy" of your brain was actually you without again hooking it up to a real human body that could speak and tell you?
All in all, a fascinating subject!!!!
The key problem with simulating neural networks (and human brain is a neural network) is that they function continuously, while digital computers function in cycles. So in a neural network different neurons function independently in parallel while in a computer you only simulate discrete system states.
That's why adequately simulating real neural networks is very problematic at the moment and we're very far from it.
Yes, the Blue Brain Project is getting close, and I believe Moore's Law has a $1000 computer getting there by 2049.
The main issue is that our brains are based largely on controlling a human body, which means that our language comprehension and production, the basis of our high-level reasoning and semantic object recognition, is strongly tied to its potential and practiced outputs to a larynx, tongue, and face muscles. Further, our reward systems are tied to signals that indicate sustenance and social approval, which are not the goals we generally want a brain-based AI to have.
An exact simulation of the human brain will be useful in studying the effects of drugs and other chemicals, but I think that the next steps will be in isolating pathways that let us do things that are hard for computers (e.g. visual system, fusiform gyrus, face recognition), and developing new or modifying known structures for representing concepts.
Short: yes we will surely be able to reproduce artificial brains, but no it maybe won't be with our current computers models (Turing machines), because we simply don't know yet enough about the brain to know if we need new computers (super-Turing or biologically engineered brains) or if current computers (with more power/storage space) are enough to simulate a whole brain.
Long:
Disclaimer: I am working in computational neuroscience research and I am interested both by the neurobiological side and the computational (artificial intelligence) side.
Most of the answers assume as true OP's postulate that simulating neurons is enough to save the whole brain state and thus simulate a whole brain.
That's not true.
The brain is more than just neurons.
First, there is the connectivity, the synapses, that is of paramount importance, even maybe more than neurons.
Secondly, there are glial cells such as astrocytes and oligodendrocytes that also possess their own connectivity and communication system.
Thirdly, neurons are heterogenous, which means that there is not just one template model of a neuron that we could just scale up to the required amount to simulate a brain, we also have to define multiple types of neurons and place them pertinently at the right places. Plus, the types can be continuous, so in fact you can have neurons that are half way between 3 different types...
Fourthly, we don't know much about the rules of brain's information processing and management. Sure, we discovered that the cerebellum works pretty much like an artificial neural network using stochastic gradient descent, and that the dopaminergic system works like TD-learning, but then we have no clue about the rest of the brain, even memory is out of reach (although we guess it's something close to a Hopfield network, but there's no precise model yet).
Fifthly, there are so many other examples from current research in neurobiology and computational neuroscience showing the complexity of brain's objects and networks dynamics that this list can go on and on.
So in the end, your question cannot be answered, because we simply do not know yet enough about the brain to know if our current computers (Turing machines) are enough to reproduce the complexity of biological brains to give rise to the full spectrum of cognitive functions.
However, biology field is getting closer and closer to computer science field, as you can see with biologically engineered viruses and cells that are programmed pretty much like you develop a computer program, and genetical therapies that basically re-engineer a living system based on its "class" template (the genome). So I dare to say that once we know enough about the brain's architecture and dynamics, the in-silico reproduction won't be an issue: if our current computers cannot reproduce the brain because of theoretical constraints, we will devise new computers. And if only biological systems can reproduce the brain, we will be able to program an artificial biological brain (we can already 3D-print functional bladders and skin and veins and hearts etc.).
So I would dare say (even if it can be controversial, this is here my own claim) that yes, artificial brains will surely be possible someday, but whether it will be as a Turing machine computer, a super-Turing computer or a biologically engineered brain remain to be seen depending on our progress in the knowledge of brain's mechanisms.
I don't think they are remotely close enough to understanding the human brain to even begin thinking about replicating it.
Scientists would have you think we are nearly there, but with regards to the brain we're not much further along than Dr. Frankenstein.
What is your goal? Do you want a program that can make intelligent decisions or a program that provides a realistic model of how the human brain actually works? Artificial intelligence can be approached from the perspective of psychology, where the goal is to simulate the brain and thereby get a better understanding of how humans think, or from the perspective of mathematics, optimization theory, decision theory, information theory, and computer science, in which case the goal is to create a program that is capable of making intelligent decisions in a computationally efficient manner. The latter, I would say is pretty much solved, although advances are definitely still being made. When it comes to a realistic simulation of the brain, I think we were only recently able to simulate a brain of cat semi-realistically; when it comes to humans, it would not be very computationally feasible at present.
Researchers far smarter than most recon so, see Blue Brain from IBM and others.
The Blue Brain Project is the first
comprehensive attempt to
reverse-engineer the mammalian brain,
in order to understand brain function
and dysfunction through detailed
simulations.
Theoretically the brain can be modeled using a computer (as software and hard/wetware are compatible or mutually expressible). The question isn't a theoretical one as far as computer science goes, but a philosophical one:
Can we model the (chaotic) way in which a brain develops. Is a brains power it's hardware or the environment that shapes the development and emergent properties of that hardware as it learns
Even more mental:
If I, with 100% accuracy modeled my own brain, then started the simulation. And that brain had my memories (as it has my brain's physical form) ... is it me? If not, what do I have that it doesn't?
I think that if we are ever in a position to emulate the brain, we should have been working on logical system based on biological principles with better applications than the brain itself.
We all have a brain, and we all have access to it's amazing power already ;)
A word of caution. Current projects on brain simulation work on a model of a human brain. Your idea about storing your mind on a hard-disk is crazy: if you want a replica of your mind you'll need two things. First, another "blank" brain. Second, devise a method to perfectly transfer all the information contained in your brain: down to the quantum states of every atom in it.
Good luck with that :)
EDIT: The dog ate part of my text.
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)