I have been trying to implement jump-point search to my already implemented A* pathfinding code, but I don't fully understand how it works. The website here (which is all that I could find on it) doesn't really explain much about how to prune or how to determine a jump-point successor. Could anyone explain these two things in detail?
The technical details are in the research paper, which can be found on Alban Grastien's web page.
There is also a tutorial on aigamedev.com.
It appears that an improved paper will appear at ICAPS 2014, but I don't believe the papers are online yet.
If these resources are insufficient, a more directed question (what don't you understand) will be easier to answer. But, one thing important to note is that JPS scans the state space looking for obstacles. In doing so it may look at far more states than A* does. But, its time savings come from not putting most states in the OPEN list.
In addition to the original 2011 paper, I also found this site: https://zerowidth.com/2013/a-visual-explanation-of-jump-point-search.html particularly helpful, as it explains how JPS works in simple terms with diagrams, and also includes a grid where you can add/remove obstacles and visualize how JPS finds and expands jump points.
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Okay here is my problem. I have to implement the Gale-Shapley algorithm for the alignment of two ontologies. I read the first 200 pages of the book "Ontology Matching" by Jerome Euzenat and Pavel Shvaiko and now I understand some of the basics of ontology matching but the problem is that the more I read the more confused I become. I really think that the problem is relatively easy but I really can't get around it and I am not getting clear instructions from my professor as they insist that I should decide everything on my own. The problem is that I don't understand the whole process. I understand the Stable Marriage Problem, I understand the basics of ontology matching but I can't seem to "connect" everything. If somebody could explain to me how the whole thing should look and how to start (the basics of a Design Document) I will be incredibly grateful.
I have a node and I want it's peak point to a specific position (coordinates). How can I do that? So far, I have tried noting because I have no clue how can I do that. Any suggestions?
What you want to SCNConstraint, and more specifically SCNLookAtConstraint.
There are a few similar questions on StackOverflow and examples in both SceneKit WWDC demos.
Next time you ask a question here, make sure you have a specific problem, and do as much research as you can. Show you did all of that, and you won't get downvoted!
If you encounters problems with the constraints, make sure to check if it has been asked here first. Good luck!
I want to implement a conversation system into my RPG (trying to get advanced AI as possible). Conversation as in, the player types:
"Hi, I would like a beer"
and the bartender would respond with
"Coming right up"
and then hand the player a beer.
I've got some ideas and some things I'd like to try, but first I would like to look at what's already been done. But extensive Googling does not turn up anything, so I'm wondering: has this been done or is there research being done in it? (I know this is very complicated, but I'm willing to give it a shot.)
Sure it has. Have a look at the "Eliza" program and its descendants. There's also a Wiki article on chatterbots that might interest you. Have a look at AIML as a way to represent the rules you might use.
For an advanced design, look up the game "Façade". The game's site describes the technologies used and gives links to relevant papers. There was also recently an extensive article in Gamasutra about this, called Beyond Façade: Pattern Matching for Natural Language Applications.
You may also want to look into the Turing Test and it's relevant scientific following/conferences/publications to see what has been done in the humanizing of AI speech.
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I know that it's still an open problem so I don't expect to see complete answers here. I just want to find some approaches to solve the next problem:
I have a model (assume that is's bot's memory), and different words are associated with different objects in the model. Speaking with the bot is like executing sql-queries with a DB. Language is a very hard formalizable protocol. And we can't just write a million lines of code to implement some real language. But I believe that it's absolutely possible to implement some self-learning mechanism. How can it be implemented? Is it possible to implement learning "from scratch" or "from few basic words"? Just want to hear your ideas.
Actually, English is a very strict language and it's one of the easiest languages for experimenting with AI. Many other languages allow you to change the order of words (for example). And in some cases changed order can change the whole meaning or just add some intonation. I really don't have any ideas how to teach a bot for these things.
The first step, in taking this game to the next level, is ...
...to have a very clear view of prior art!
(and pardon me to say, the question doesn't suggest that you have such an extensive insight into the matter [and you're not alone, count me in ;-)])
Even, and maybe in particular, if your intention is to apply completely novel techniques and models, it seems important to review the literature on current and past practices. Aside from possibly identifying elements that may be adapted or reused in a new implementation, a survey of the domain will provide an keen understanding of the nature of the problem[s].
I've personally tried -on various and multiple occasions!- either the naive approach or the sophomoric approach to tackling broadly-defined problems. With the naive approach, one has but a very slight idea of the true nature and scope of the problem. The sophomoric sees us better equipped with domain knowledge and also with related tools, but this can also be misleading because without a deeper understanding, we tend to mis-read/mis-understand new material offered to us and also misuse some of the tools (a bit like the the fellow who's "good with a hammer" for whom many things look like a nail...)
It is particularly easy to make these mistakes in the field of NLP. That's because
Common sense seems to be all is required: after all a child, who's native tongue is English understands subtleties like
"He's not really an expert"
"He's really not an expert" (small wink at the OP's reference to the ordering of word in the English language)
We live in such exciting times, technology and knowledge wise: Processing power, programming language and tools, mathematical techniques, availability of affordable corpora... to name a few of these things that make this moment in time so special.
Far from me the idea of discouraging you in your chat-bot endeavor, I just hope that this long and generic exposé will encourage to look-before-you-leap, as this will truly save you time in the long run, I think in two ways:
provide you some frames of references (again, even if your intention is to "think outside these boxes")
maybe entice you to redefine the problem, for example by limiting it to particular domains of conversation (sports, or health, or life at a particular university campus...) or by focusing on a particular aspect of the problem (semantic awareness, smooth, natural sounding grammar, use of colloquial forms...)
Good luck ;-)
Check out MegaHAL's implementation for some ideas. We've used a variant of this bot for ages in an IRC channel of ours, and he does on occasion appear to be the intelligent mixture of many of our dominant personalities.
You "train" the bot -
each time the bot answer, you rank (or the tester) the answer - if the answer is good/logical - give high rank, if the answer is bad... low/negative rank.
use the ranking in the future to choose the answer, and this is how the bot learns...
There's a great description of Eliza in Paradigms of AI Programming. You should be able to implement a simple Eliza bot in a few days of work.
This isn't a learning algorithm, but it's surprising how realistic answers can be from something so simple.
You can create your own chat bot on BOT libre, http://www.botlibre.com.
The bots learns, can be trained, can be scripted, and your can program them, or let them program themselves.
Thew site supports embedding your bot on your own site, has REST API access, Android, IRC, Twitter. Free hosting, even for commercial bots.
AIML from the AliceBot project may help you out. It's a whole XML schema (if that doesn't put you off) for the branch of AI its concerned with.
An example from Wikipedia:
<category>
<pattern>WHAT IS YOUR NAME</pattern>
<template>My name is <bot name="name"/>.</template>
</category>
RebbeccaAIML is one quite well documented implementation.
I would like to know if there are any tools that can help me model C applications i.e. Functional programming.
E.g. I'm currently building a shared library.
But to communicate my design visually, I need something like UML. I would like to do this so that the person reviewing my design need not read through 100s of pages of functions, variables and so on.
I have read about UML for C, which I'm considering.
If there is anything better out there, please let me know.
The bottom line is to visualize the design of C applications and modules without reading through 100s of pages of text, because it takes time and is difficult for the reviewers.
Any help in this area from the experts here would be much appreciated.
Thanks.
A well written text documentation brings you a far. Much further than any UML diagram could ever achieve.
You should split this in two parts:
What do you want to say?
What's the best way to saying it?
Whatever formalism you use to answer the second part, you should be sure it's not ambigous.
The good of UML is that a lot of semantic is already defined by the language so you don't have to include a definition of what those boxes, lines and arrows mean in a collaboration diagram.
But most importantly, documenting something means create a path for others to understand the subject you are documenting. A very precise description that offers no clue on how to read it is as good as none. So, use UML, Finite state machines, ER diagrams, plain English, whatever you want but be sure to include a logical path that your "readers" can follow to understand what's going on.
I had a friend that was a fan of "preciseness at all cost" and it would ask us to go through all the details before some sort of meaning would emerge.
I once ask him to do this experiment: on his next trip to an unknown city, he would have to carry the most precise map he could get. Much better, he would have to carry a 1:1 map of the city with every single detail exactly reported in scale. That way he couldn't get lost!
He declined but I would love to see him trying to use that map. Just even folding it! :)
Whatever you like. It's not a standard but many devs use it and understand it. If it does help you to communicate with other people and document your work -> its for you. If it just takes too much time and you think it's not effective, drop it. Also, don't bother with all details, as long as it resembles UML and your team can work with it, it's fine.
It's meant to help you, not waste you time.