I want to do some automatic story generation demonstration and the approach I am taking is using AI planning. I have been reading several relevant papers and have figured out that PDDL is perhaps the most widely used language to form the planning problem. I have been looking at the syntax and several example codes to learn how to use it.
The part where I am stuck is how to get the planner to work. I have found out some popular planners (fast-forward, MBP, IPP) but am not being able to make them work, using the instructions even from the sources itself.
I am using Gnome Terminal on Ubuntu 13.04.
I am very new to planning and this may be a very naive question but I assure that I have been searching for more than 3-4 days without any luck. Also, suggestions on using some other planning system are welcome.
If you are using Linux then I strongly suggest to use Fast Downward (it has its own web page - just google it). First of all, it is currently one of the best-known planning systems in the AI planning community and, further, it is really easy to get it to run. Well, you still need half an hour or so, but there is an easy-to-follow step-by-step description telling you where to check out the code and which commands you need to run.
It has also implemented most of the known planning heuristics that are required to solve problems fast or even optimal (planning requires search and heuristics make the search "goal-oriented" rather than blind and, if the heuristic is admissible and/or monotone (depending on the kind of search algorithm that is chosen -- see fast forward and pddl: is the computed solution the best?), it guarantees to find optimal solutions).
Concerning literature, I suggest to read/skip through the following two journal articles:
Porteous, J.; Cavazza, M.; and Charles, F. 2010. Applying planning to interactive storytelling: Narrative control using state constraints. ACM Trans. Intell. Syst. Tech. 10:1-10:21.
http://dl.acm.org/citation.cfm?id=1869399
Patrik Haslum. "Narrative Planning: Compilations to Classical Planning". Journal of AI Research, vol. 44, p. 383-395, 2012
http://www.jair.org/papers/paper3602.html
Well, both MBP and IPP are really, really old systems. If you're just looking for a ready-made planner to use in an off-the-shelf manner, I'd suggest you to follow the pointers leading to the authors (and software) that took part in the last International Planning Competition (2011):
http://www.plg.inf.uc3m.es/ipc2011-deterministic/ParticipatingPlanners.html
Related
I have built several domains and problems in PDDL and now I'm looking for a possibilty to visualize my domains, problems and solutions. My questions are as follows:
Are there any tools for a rather simple graphical representation of plans (e.g. nodes and connections)?
Are there tools that can display the current world state of my domain at any time (i.e. the value of all variables in my domain, after each step in my plan) in plain text?
How do I integrate PDDL in the system architecture? If I want to use a 2D or 3D representation of my world (e.g. Gazebo) how can i "connect" Gazebo and PDDL?
Thanks!
Going over your questions one by one:
None made publicly available, but I am aware of several students of members of the Planning community working in tools like that. I'd suggest you to make the question on this mailing list: planning-list#googlegroups.com
For that you'd need to modify one of the many existing planners and work it out yourself from their data structures or extend whatever debugging methods planners' authors have built into their systems. If you have the time and good C++ skills I'd suggest you to look into either Fast Downward (http://www.fast-downward.org/) or the less mature and simpler lwaptk (https://github.com/miquelramirez/lwaptk). If you are after some fast prototyping, and you are happy to compute the plans off-line, then I'd recommend you to look at Pyperplan (https://bitbucket.org/malte/pyperplan) a native Python planner which should quite easy to interface with pretty much anything (it just won't be very efficient when it comes to compute plans).
You'll have to work out yourself an interface between the model of your "Gazebo" world and the planning back-end, and the details depend on what a) what are you modeling in the "front-end" and b) what do you exactly want the planner to do for you. For an example of how to expose a relatively complex object model I'd suggest you to look at this little demo I put together a while back (https://github.com/miquelramirez/pr-as-planning-demo).
Read this link, it might be useful for you
https://www.ida.liu.se/~TDDD48/labs/2015/planners.en.shtml
I am looking for good academic references on how to benchmark programs. There seems to be a lot of lore in benchmarking, but I haven't seen many references that explain what a good benchmark is, what a bad one is, and how to write one.
Thanks.
Academically speaking, a relevant article is "Statistically rigorous Java performance evaluation" from OOPSLA 2007 (which you can find from Google Scholar); while focused on Java, it contains general lessons on benchmarking, and the content about Java generalizes nicely to most languages running on some virtual machine and simply using garbage collection. Finally, they summarize the statistics knowledge needed for analyzing the results.
Additionally, here is a framework from Google:
http://code.google.com/p/caliper/
And here their Wiki discusses some criteria for a good benchmark:
http://code.google.com/p/caliper/wiki/JavaMicrobenchmarkReviewCriteria
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