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I am writing bot for one rts game.
I am using fuzzy logic to evaluate current position (mine and enemies') and to issue commands.
I have couple fuzzy variables: military_buildings, civilian_building, army_power, enemy_power and distance. I also have couple fuzzy linguistic values like VERY_GOOD, GOOD, NORMAL, BAD, VERY_BAD.
My next task is to make bots to learn, to avoid to all behave on same way. Any advice or idea how to solve this?
To use GA for tuning parameters (but I don't know ratings of players so I don't know if bot wins over a weak player or loses to a strong player).
Does anyone have experience with similar problems (I can change implementation and replace fuzzy logic if there is easier way to learn bots from experience)?
Have a look at reinforcement learning. Here are a quick preview and a book that can help you.
Based on your description, this is what I'd use :)
The idea of using GAs to tune the parameters to Fuzzy Linguistic Variables is a good one (I wish I thought of it!); the fuzzy logic gives you a nice continuous response curve while the GA will search through a large solution space. I think it's definitely a strategy worth pursuing; you should write up your results.
If I were you I would look at the AIIDE annual Starcraft Competition, it is sponsored in part by AAAI so there are some really high quality approaches to this problem. In particular if you are concerned with higher-level reasoning like resource management etc. Starcraft Competition Site Also, the competitors source code is all available open source so if you want to check out some other techniques I recommend it. FYI, most of the top competitors for this type of problem have historically used some variant of a Probabilistic State Machine Paper on Probabilistic FSMs, so this may make a good test bed for parameter tuning. FYI this is also the approach that some of the top Game AI middleware software uses for Game AI, like XAIT.
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I'm a bit confused about artificial intelligence.
I understand it as the capability of a machine to learn new things, or do different things without actually executing code (already written by someone).
In SO I see many threads about A.I. in games, but IMO that is not an A.I. Because if it is every software even a print command should be called A.I. In games there is just code that is executed. I would call it pseudo-AI.
Am I wrong? Should be also this considered as A.I.?
Wikipedia says this:
Artificial intelligence (AI) is the intelligence of machines and the branch of computer science that aims to create it.
AI textbooks define the field as "the study and design of intelligent agents"
[1], where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success.
What you are considering is more specifically referred to as Machine Learning, which is indeed a subbranch of AI. As you can see from the second sentence above, however, the "AI" considered in games also fits perfectly well into this definition.
Of course, the actual line between what is AI, and what not, is quite blurry. This is also due to the fact, that everyone and his mother believes to know what "AI" means.
I suggest you grab yourself a more scientific book (say the classical Russel,Norvig) to get a more thorough grasp on the different fields that are present under the huge roof of what we simply refer to as "AI".
"Minsky and McCarthy, both considered founders of AI, say that artificial intelligence is anything done by a program or a machine that if a human did the same activity, we would say the human had to apply intelligence to accomplish the task."
A more modern definition is to turn this on its head:
Artificial intelligence is anything done by a program or a machine that if a human did the same activity, we would say the human did not need to apply intelligence to accomplish the task.
Intelligence is the ability to do the things that don't require reasoning. Things like understanding and generating language, sequencing your leg muscles as you walk across the floor, or enjoying a symphony. You don't stop to reason for any of those things. You Understand. INTUITIVELY, how to interpret things in your visual field, language, and all other sensory input. And you can do the right thing without reasoning. You can easily prepare all of your breakfast without any reasoning. :-)
Doing things that "require thought" or reasoning, like playing chess or solving integrals are things that computers can already do.
This misunderstanding about what intelligence really is has cost us 60 years and a million man-years of banging our head against the wall.
Deep learning is the currently most popular expression of an alternative path to a "better kind of AI". Artificial Intuition is a special branch of Deep Learning tailored at understanding text.
The easiest way to know whether you are dealing with classical (futile) or modern AI is whether the system requires you to supply any models of the world (MOTW). Any MOTW means the AI is limited to operate in the domain specified by the MOTW and is therefore not a general intelligence. Also, anything with a MOTW is typically not designed to extend that model; this is a very difficult task.
Better to start from a Model of the Mind (MOTM) or a Model of Learning. These can be derived either from neuroscience (difficult) or from epistemology (much easier). A well done MOTM can then learn anything it needs to know to solve problems in any domain.
The main problem for most is to find what's called "a domain-independent method for determining saliency". In other words, all intelligences, natural or artificial, have to spend most of their time answering the question "what matters".
Google my name and "AI" for more.
Minsky and McCarthy, both considered founders of AI, say that artificial intelligence is anything done by a program or a machine that if a human did the same activity, we would say the human had to apply intelligence to accomplish the task.
Frank and Kirt sum up the academic field of AI pretty well. Any difficulty there is defining AI reflects the more general problem of defining real intelligence. If AI has proved anything, it's that we have precious little idea what intelligence is, how amazing organisms are at solving problems, and how difficult it is to get machines to achieve the same results.
As for the use of the term AI in the video games industry, your confusion justified. The prospect of intelligent characters in games is so compelling, that the term long ago took on a life of its own as marketing jargon. However, AI is really just a poorly chosen name for the solving of problems that computers find hard and people find easy. And in that sense, there is plenty of genuine AI work going on in the games industry.
Take a look at AIGameDev.com for a taste of what is currently considered noteworthy in AI game development.
The most important aspect of AI as I believe is 'curiosity'. Intelligence comes from this very fact that it is a result of curiosity.
There is no precise definition of AI because intelligence itself is relative and hard to define, this is due to the fact that many fields (ancient and modern) such as Philosophy and Neuroscience serve as the foundations of AI. It depends on what your AI is expected to do.
Artificial Intelligence is the attempt to create intelligence from a computer program.
Regardless if its a toy program or neural science, as long as a program is able to mimic human problem-solving skills or even go beyond it, is called Artificial Intelligence.
Of course, the expectation of computer scientists on how capable a program (or machine) is to solve problems in time increases. Playing tic-tac-toe programs before is considered intelligent until chess programs where invented. Then now we are attempting to mimic how human brain through neural networks.
A.I for layman's now a day's is applied in most computer games. It's also used in most machines, like in airplane for autopilot, NASA's explorer on Mars called curiosity (2012), who's able to detect terrain obstacles and move around it.
Very tricky stuff A.I. Its like if you design a mind that replies to all the right questions with all the answers, is it A.I. Or just a talking encyclopedia. What if you can teach the A.I. by simply talking to it, do you then consider that A.I. with a mind, or again just a program. Perhaps the question or answer is if someone someday makes a machine that looks human, acts human, and thinks its human. And then see if others feel the same, that its human, if they dont know its not. And then what if it passes that test. You see its not really about is the machine conscious, or does it have a mind as those will never be truly answered. Its all about does it "seem conscious" act conscious, act like it has a mind, given thats as close as humanity will ever get to understanding that riddle. If a machine acts like it cares, and does helpful things thats all that really matters, not the rest of unseen picture. We just half to get this far in the first place. By the way check out Webo a teachable A.I.Webo a teachable A.I.
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.
I'm working on a project at the moment where it would be really useful to be able to detect when a certain topic/idea is mentioned in a body of text. For instance, if the text contained:
Maybe if you tell me a little more about who Mr Jones is, that would help. It would also be useful if I could have a description of his appearance, or even better a photograph?
It'd be great to be able to detect that the person has asked for a photograph of Mr Jones. I could take a really naïve approach and just look for the word "photo" or "photograph", but this would obviously be no good if they wrote something like:
Please, never send me a photo of Mr Jones.
Does anyone know where to start with this? Is it even possible?
I've looked into things like nltk, but I've yet to find an example of someone doing something similar and am still not entirely sure what this kind of analysis is called. Any help that can get me off the ground would be great.
Thanks!
The best thing out there that might be useful to you is automatic sentiment analysis. This is used, for example, to judge whether, say, a customer review is positive or negative. I cannot give you direct pointers to available tools, but this is what you are looking for.
I must say, though, that this is a current hot topic in natural language processing and I’ve seen a number of papers at conferences. It’s definitely quite a complex matter and if you’re starting from scratch, it might take quite some time before you get the results that you want.
NLTK is not a bad framework for parsing natural language but beware that this is not a simple matter. Doing stuff like this is really research level programming.
A good thing that makes it much easier is if you have a very limited domain - say your application focuses on information about famous writers, then you can avoid some complexities of natural language like certain types of ambiguities.
Where to start? Good question. I don't know of any tutorials on the topic (and I presume you tried the Google option) but I'd imagine that iTunes U would have a course on the topic. If not I can post a link to a course I've done that mentions the subject and wasn't completely horrible: http://www.inf.ed.ac.uk/teaching/courses/inf2a/lecturematerials/index.html#lecture01
The problem that u tackle is very challenging.
I would start by first identifying the entities in the text (problem referred as Named Entity Recognition, google it), and then a I would try to identify concepts.
If want to roughly identify what is the text about, I suggest that you start by using WordNet and according to the words and their places in the hierarchy to identify the concepts involved.
If you want to produce a system which show real intelligence than you should start researching about resources such as CYC (OpenCYC) which will allow you to convert the sentences into FOL sentences.
This hardcore AI, approach to solving your problem. For simple chat bot, it would be easier to rely on simple statistical methods.
good luck
Out of curiosity, I've been reading up a bit on the field of Machine Learning, and I'm surprised at the amount of computation and mathematics involved. One book I'm reading through uses advanced concepts such as Ring Theory and PDEs (note: the only thing I know about PDEs is that they use that funny looking character). This strikes me as odd considering that mathematics itself is a hard thing to "learn."
Are there any branches of Machine Learning that use different approaches?
I would think that a approaches relying more on logic, memory, construction of unfounded assumptions, and over-generalizations would be a better way to go, since that seems more like the way animals think. Animals don't (explicitly) calculate probabilities and statistics; at least as far as I know.
The behaviour of the neurons in our brains is very complex, and requires some heavy duty math to model. So, yes we do calculate extremely complex math, but it's done in a way that we don't perceive.
I don't know whether the math you typically find in A.I. research is entirely due to the complexity of the natural neural systems being modelled. It may also be due, in part, to heuristic techniques that don't even attempt to model the mind (e.g., using convolution filters to recognise shapes).
If you want to avoid the math but do AI like stuff, you can always stick to simpler models. In 90% of the time, the simpler models will be good enough for real world problems.
I don't know of a track of AI that is completely decoupled from math though. Probability theory is the tool for handling uncertainty which plays a major role in AI. So even if there was not-so-mathematical subfield, math techniques would most be a way to improve on those methods. And thus the mathematics would be back in game. Even simple techniques like the naive Bayes and decision trees can be used without a lot of math, but the real understanding comes only through it.
Machine learning is very math heavy. It is sometimes said to be close to "computational statistics", with a little more focus on the computational side. You might want to check out "Collective Intelligence" by O'Reilly, though. I hear they have a good collection of ML techniques without math too hard.
You might find evolutionary computing approaches to machine learning a little less front-loaded with heavy-duty maths, approaches such as ant-colony optimisation or swarm intelligence.
I think you should put to one side, if you hold it as your question kind of suggests you do, the view that machine learning is trying to simulate what goes on in the brains of animals (including Homo Sapiens). A lot of the maths in modern machine learning arises from its basis in pattern recognition and matching; some of it comes from attempts to represent what is learnt as quasi-mathematical statements; some of it comes from the need to use statistical methods to compare different algorithms and approaches. And some of it comes because some of the leading practitioners come from scientific and mathematical backgrounds rather than computer science backgrounds, and they bring their toolset with them when they come.
And I'm very surprised that you are suprised that machine learning involves a lot of computation since the long history of AI has proven that it is extremely difficult to build machines which (seem to) think.
I've been thinking about this kind of stuff a lot lately.
Unfortunately, most engineer/mathematician types are so tied to their own familiar mathematical/computational worlds, they often forget to consider other paradigms.
Artists, for example, often think of the world in a very fluid way, usually untethered by mathematical models. Much of what happens in art is archetypal or symbolic, and often doesn't follow any seemingly conventional logical arrangement. There are, of course, very strong exceptions to this. Music, for instance, especially in music theory, often requires strong left brained processes and so forth. In truth, I would argue that even the most right brained activities are not devoid of left logic, but rather are more complex mathematical paradigms, like chaos theory is to the beauty of fractals. So the cross-over from left to right and back again is not a schism, but a symbiotic coupling. Humans utilize both sides of the brain.
Lately I've been thinking about a more artful representational approach to math and machine language -- even in a banal world of ones and zeroes. The world has been thinking about machine language in terms of familiar mathematical, numeric, and alphabetic conventions for a fairly long time now, and it's not exactly easy to realign the cosmos. Yet in a way, it happens naturally. Wikis, wysisygs, drafting tools, photo and sound editors, blogging tools, and so forth, all these tools do the heavy mathematical and machine code lifting behind the scenes to make for a more artful end experience for the user.
But we rarely think of doing the same lifting for coders themselves. To be sure, code is symbolic, by its very nature, lingual. But I think it is possible to turn the whole thing on its head, and adopt a visual approach. What this would look like is anyone's guess, but in a way we see it everywhere as the whole world of machine learning is abstracted more and more over time. As machines become more and more complex and can do more and more sophisticated things, there is a basic necessity to abstract and simplify those very processes, for ease of use, design, architecture, development, and...you name it.
That all said, I do not believe machines will ever learn anything on their own without human input. But that is another debate, as to the character of religion, God, science, and the universe.
I attended a course in machine-learning last semester. The cognitive science chair at our university is very interested in symbolic machine learning (That's the stuff without mathematics or statistics ;o)). I can recommend two outstanding textbooks:
Machine Learning (Thomas Mitchell)
Artificial Intelligence: A Modern Approach (Russel and Norvig)
The first one is more focused on machine learning, but its very compact has got a very gentle learning curve. The second one is a very interesting read with many historical informations.
These two titles should give you a good overview (All aspects of machine learning not just symbolic approaches), so that you can decide for yourself which aspect you want to focus on.
Basically there is always mathematics involved but I find symbolic machine learning easier to start with because the ideas behind most approaches are often amazingly simple.
Mathematics is simply a tool in machine learning. Knowing the maths enables one to efficiently approach the modelled problems at hand. Of course it might be possible to brute force one's way through, but usually this would come with the expense of lessened understanding of the basic principles involved.
So, pick up a maths book, study the topics until it you're familiar with the concepts. No mechanical engineer is going to design a bridge without understanding the basic maths behind the system behaviour; why should this be any different in the area of machine learning?
There is a lot of stuff in Machine Learning, outside just the math..
You can build the most amazing probabilistic model using a ton of math, but fail because you aren't extracting the right features from the data (which might often require domain insight) or are having trouble figuring out what your model is failing on a particular dataset (which requires you to have a high-level understanding of what the data is giving, and what the model needs).
Without the math, you cannot build new complicated ML models by yourself, but you sure can play with existing tried-and-tested ones to analyze information and do cool things.
You still need some math knowledge to interpret the results the model gives you, but this is usually a lot easier than having to build these models on your own.
Try playing with http://www.cs.waikato.ac.nz/ml/weka/ and http://mallet.cs.umass.edu/ .. The former comes with all the standard ML algorithms along with a lot of amazing features that enable you to visualize your data and pre/post-process it to get good results.
Yes, machine learning research is now dominated by researchers trying to solve the classification problem: given positive/negative examples in an n-dimensional space, what is the best n-dimensional shape that captures the positive ones.
Another approach is taken by case-based reasoning (or case-based learning) where deduction is used alongside induction. The idea is that your program starts with a lot of knowledge about the world (say, it understands Newtonian physics) and then you show it some positive examples of the desired behavior (say, here is how the robot should kick the ball under these circumstances) then the program uses these together to extrapolate the desired behavior to all circumstances. Sort of...
firstly cased based AI, symbolic AI are all theories.. There are very few projects that have employed them in a sucessfull manner. Nowadays AI is Machine Learning. And even neural nets are also a core element in ML, which uses a gradient based optimization. U wanna do Machine learning, Linear Algebra, Optimization, etc is a must..