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.
Related
Yes A.I. field is very vast field. I have gone through the wiki page of A.I. and read about the different fields of it. I think any enthusiastic beginner can choose one of those fields based on his/her interest to get into A.I. But before getting in it is always good to have the per-requisites of the appropriate field.
It is very useful to have those per-requisites listed against each of the A.I. field to any beginner of A.I (like me) so that he can refresh his knowledge in them once and get started in actual field. Do someone list them here please?
Thanks
This question does not have a simple answer. The degree of knowledge required depends on how in depth you want to go into the applicable field.
To use a simple neural network API for example, there are hardly any prerequisites. To know whether a neural network will work for a specific problem or to write your own neural network, you will need to have at least high-school level maths knowledge to understand the internals and thus limitations of a neural network (or you can try to memorize it all). To be able to argue and understand arguments of specific approaches, you will probably need college level maths.
If you want to learn more about AI and the different fields therein (and get some idea of the requirements), I suggest you take an introductory AI course or a course in the sub-field that interests you. Coursera.org is a decent site for such courses (and it's free). Many courses will give you a list of prerequisites before you sign up.
From my experience, the main prerequisites to many of the fields of AI are Statistics, Linear Algebra or Calculus.
A decent understanding of data structures and algorithms is also very important for most fields of AI (and programming in general).
I think rather than having someone put a giant table of all the prerequisites here, just select a sub-field that seems interesting, take an introductory course, and see if you understand it. You can always learn the prerequisites.
If you don't have high-school maths knowledge, it might be a good idea to start with a maths course or two before you think about AI.
I know that Artificial Intelligence field is very vast and there are many books on it. But i just want to know the any resource where i can get the simple inroduction to all Artificail Intelligence techniques like
It would like to have 1 or 2 page introduction of all techniques and their examples of how they can be applied or for what purpose they can be used. I am interested in
Backpropagation Algoritm
Hebbs Law
Bayesian networks
Markov Chain Models
Simulated Annealing
Tabu Search
Genetic Algorithms or Evolutionary Algos
Now there are many variants and more AI techniques. And each one have many books written on them. i am unable to decide which algos i can use unless i know what they are capable of doing.
So can i find 1-2 page inroduction of them with Application examples
Essentials of Metaheuristics covers several of these - I can't promise it'll cover all of them, but I know there's good stuff on simulated annealing and genetic algorithms in there. Probably at least a few of the others, but I'd have to re-download it to check. It's available for free download.
It can be a bit light on the theory, but it'll give you a straightforward description, some explanation of when you'd want to use each, and a lot of useful pseudocode.
Here's an image on local search (= tabu search without tabu) from the Drools Planner manual:
I am working on similar images for Greedy algorithms, brute force, branch and bound and simulated annealing.
As an example of Genetic Algorithms implementation I can give you this.
It's an API I developed for GA, with one implementation for each operator, and one concrete example problem solved ((good) Soccer Team among ~600 players with budget restriction). Its all setup so you run it with mvn exec:java and watch it evolving in the console output. But you can implement your own problem structure, or even other operators (crossing, mutating, selection) methods.
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.
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
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