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I'm using Optaplanner to make a schedule for a school. Every works good, except that it sometimes have gaps between the lessons, which I do not want. I have rules for punishing this but I think the search space is so big that it will take quite a while before it "fixes" this.
Is it possible to tell Optaplanner to try out some "selected/calculated" moves first and then continue with the moves it is supposed to do?
I'm using the time grain patterns with this.
It is possible to do, but not recommended. There is good literature on the subject here: https://www.optaplanner.org/docs/optaplanner/latest/move-and-neighborhood-selection/move-and-neighborhood-selection.html
I would start first by experimenting with configuring the generic moves that are provided out-of-the-box in optaplanner, which you can do by editing the XML file included with the distribution.
If a custom move is really what you need, you can refer to the docs above, but it is much harder to avoid bugs this way and you will want to enable full assert to double-check that score corruption is not occurring after implementation.
I'm currently writing a basic platformer game with the idea of learning the basis patterns of a 2D game development. One of them is the amazing A Star Pathfinding. After reading and study a few articles (two of them are the best: Link and http://theory.stanford.edu/~amitp/GameProgramming/Heuristics.html#S7), tutorials and source code, I did a simple A Star Pathfinding using the Manhattan method that works well for my personal learning process.
The next, and logical step, is to do the same on a platform scenario where the NPC must jump or climb ladders to reach the goal target. I've found these screencastings http://www.youtube.com/watch?v=W2xoJfT6sek and https://www.youtube.com/watch?v=Bc7tu-KwfoU that show perfectly what I want to do.
I haven't found for the moment any resource that explains the method to implement it. Can someone give me a starting point I can work with?
Thanks in advance
As far as I can tell, the only difference that jumping/ladders make is that they affect possible movement. So, all your algorithm has to do is take into account where a character can and can't move to, and the costs (if you're implementing that) of moving there.
I haven't any clean A* code around fit for an example but let me give you a few pointers when implementing A*.
First off, the most essential tip that I often find neglected in tutorials is use a priority queue.
Whenever you generate possible move states, put them into the priority queue with priority being the value of your heuristic for that move state. This differentiates A* from DFS (which uses a stack and backtracking) or simple greedy path finding (which simply goes to the best state with respect to the present state).
Next, mind those pointers! I don't know what language will you implement this but even if you do it in some language that abstracts pointers (unlike C), you have to understand the concept of a deep copy versus a plain shallow copy. I remember the first time I did A* (in Python) and I did not pay attention to this. My states were repeatedly overwritten leading to unpredictable behavior. Turns out I did not make deep copies!
That said, depending on how complex the task you have in mind, all those deep copies might clutter up your memory. While good memory management will help, for big problem instances that need real-world performance, I tend to just use iterative-deepening A* so that after every iteration, I get to unclutter my memory.
I need a 3D engine for a very specific task in Artificial Intelligence, and I'd like some input.
The first part is the trivial one - basically, all I need is a FPS engine (3rd person would be good, too), such that it allows me to navigate a room and interact with objects (if you have Java and Windows, I'm looking for something similar to the Give Challenge, but a little more up-to-date). Physics would be nice, but is not a must.
Now, the non-trivial part would be: I need to impose a virtual grid over this room, such that at any moment I can say "the player is located at B5 - now he moved to B6", and so on. I need to redirect this information to another system (namely, one which will give the player instructions about what to do) and, at the same time, send messages to the player, so I must be able to have a single point through which the game logic passes through; also, I'd love not having to write my own collision detection and such.
So far, I've tried:
the Source SDK: it seems a little overkill (since I'm not really planning to shoot anyone, at least half the code base is useless to the task), and since I'm not really a Windows developer, I'm spending too much time with the "easy" stuff (such as getting VS up and running). Plus, cross-platform would be really nice.
Blender game engine: while this worked decently, the interaction model seems a little weird, and some easy stuff (such as making sure the camera stays inside the scene or showing the mouse on screen) gets too weird too soon.
Crystalspace 3D: I've tried their demos, but it looks a little old-fashioned, and since that was one of the problems of previous engines (it's easier to get volunteers when your game looks nice) I'd like to try something else.
Now, maybe I'm asking a little too much for a single software, but I'd love some input. Can anyone suggest me an alternative? Or should I give one of the previous ones a second chance?
Try the UDK. All of the things you request are present, and it's free for personal/noncommercial projects. Here are some highlights:
Modern looking. The UDK features an intuitive-ish visual material design system, post-processing effects, Scaleform Gfx UIs from Autodesk, and more.
A visual scripting interface called Kismet that can control gameplay elements, the camera, and more.
UnrealScript, a scripting language similar in syntax to C, C++, Java, that gives you the ability to extend existing functionality or create your own.
Comprehensive documentation available on UDN.
Lots of community support outside of Epic, in places such as Polycount, Eat3d, 3dbuzz, and more.
Basically, "and more".
If what you're looking for is a professional, free (as in beer) engine that will allow you to focus primarily or solely on your differentiating gameplay features, Epic has set the bar high.
When doing hallway usability tests do most of you make your apps fully or near fully functional? Or do you just make sure the links or flow chain correctly? Or do you just draw on paper and go with that?
I'm would like to test early on a prototype and am trying to find a good balance. But at the same time am worried that some non functional parts might actually not give representative results.
Thanks.
Usability tests, hallway or otherwise, only need the functionality that you need to test. In most usability tests, you should go in with specific design questions to answer and develop your prototype to the point where it can answer those questions. For example, if you need to test if users understand your indication of the sort order for a table, all you need is a paper picture of the table showing the sort indication (with the table contents blurred) and ask them how the table is sorted. If you need to test the IA, all you need is a bunch of web pages, empty except for a title, that are linked through the navigation menus.
You only need the pages relevant for the tasks you give your users. If you’re just testing the IA, then you only need the pages on the normative path. If you are also testing error recovery, then you need the pages off the normative path along with the full navigation controls. If you are also testing error detection, then you need content on the pages as well.
You can also simulate functionality when that’s easier to do. For example, in testing if users can figure out how to get a desired sort order, when the user clicks on a non-functioning control for sorting the table, you can say, “Okay, doing that will get you this,” and you take the mouse and select a bookmark that shows the table in the new sort order.
In hallway testing, if users breach the fidelity envelope, you can simply say, “I haven’t made that part yet. Let’s go back to A, and continue from there.” Of course, you should note that the user made a wrong turn in the task you intended for them. I haven’t had any problems with users complaining about non-functional features when I tell them up front it’s an incomplete prototype and we’re only testing the UI for features x, y, and z at the moment.
For low fidelity prototypes, I often call them “mockups” or “drawings” to users rather than “prototypes” to indicate the low functionality. You can put obvious placeholders in for missing content (e.g., “Blah, blah, blah…”, “TODO: Picture of product about here.”). If a user comments on something outside the fidelity envelope (e.g., “This symbol should be red to stand out more”), simply note it, and say that topic is under development (e.g., “Thanks. We haven’t started work on the colors yet. We’re just trying to figure out how to organize the site right now.”).
Usability testing with limited-fidelity prototypes is really necessary for iterative design to be feasible for most projects. Otherwise, you waste too much work developing things that have to be redone.
A couple things to remember:
Test early and often.
The goal of usability testing is to find problems with the UI, not Q/A your code.
Therefore, if users can see the parts of your UI you are interested in testing and interact with them in a realistic way (e.g., click on buttons and links), you should be able to collect useful data. If some links are dead-ends, that's okay, as long as there's some way for users to recover and continue on. Basically, with prototypes, the "correct" path should work, but it's okay if incorrect paths don't (as long as there's a reasonably quick way to get back on the correct path). Even static storyboards (non-functioning drawings of a UI) can provide you with some information if you ask the right questions, e.g., "What would you do on this screen if you wanted to view your shopping cart?").
I would suggest a couple rounds of usability testing. First on paper, perhaps later on screen, generally throughout the application lifecycle (take an Agile approach to it).
There is a good argument to be made for paper prototypes. When users see a screen, even limited functionality, they may be hesitant to suggest changes since it looks "done."
Make no mistake, it's not trivial to get it all down on paper, but that's where I would start. Probably start with just a section or two of the application. And make sure somebody with good people skills and/or explaining skills is there to walk the user through it. Have a second person on-hand to take notes. Try to ask open-ended questions, etc.
For a hallway test, I would test with NONE of the functionality implemented.
Test against designs done on a whiteboard or on paper. You'll be surprised at how much you find out in these minimal mockups. And they are very inexpensive to make!
Functional prototypes are for later. If you give your usability subject a functional interface, they are much less likely to question whether you've implemented the right set of features in the first place.
I would make the UI functional, so that the user can really play with it, it will be much better than a static image. People can tell you whether they feel comfortable on the UI.
I would make sure everything in the UI works, or at least takes you to a clear, unambiguous message pointing out that the feature isn't implemented yet.
Showing prototypes to clients with a disclaimer up front about how feature X doesn't work yet will usually be ignored. They'll try out the prototype, click on featuree X and indignantly reply "Feature X doesn't work! This really needs to work in the final version! Why doesn't it work?". The client is confused and unhappy about the product, and it's frustrating for yourself because it overshadows the positive feedback. Besides, you told them it didn't work, why can't they use their imagination to envision how it would work in the final version?
Make it work, be it with a rough version, dummy data, or even a simple message saying "would show results sorted alphabetically now".
Erm - what the question said. It's something I keep hearing about, but I've not got round to looking into it yet.
(updated) I could look up the definition... but why not (as pointed out by #erikson) get insight into your real experiences and anecdotes. Community Wiki'd incase that helps folks vote up the most insightful answer. Interesting reading so far, thanks!
Short answer, it is a technique that you can use to express systems with concrete states (as opposed to quantum states / probability distributions).
Quoting the Wikipedia article:
A finite state machine (FSM) or finite
state automaton (plural: automata) or
simply a state machine, is a model of
behavior composed of a finite number
of states, transitions between those
states, and actions. A finite state
machine is an abstract model of a
machine with a primitive internal
memory.
So, what does that mean to you? Put simply, it is an effective way to represent the path(s) from a starting state to the end state(s) of the system that you care about. Using regular expressions as a fairly easy to understand example, let's look at the pattern AB+C (imagine that that plus is a superscript). I would expect to this pattern to accept strings such as "ABC", "ABBC", "ABBBC", etc. A at the start, C at the end, some number of B's in the middle (greater than or equal to one).
If you think about it, it's almost easier to think about this in terms of a picture. Faking it with text (and that my parentheses are a loopback arc), you can see that A (on the left), is the starting state and C (on the right) is the end state on the right.
_
( )
A --> B --> C
From FSAs, you can continue your journey into computational complexity by heading over to the land of Turing Machines.
However, you can also use state machines to represent real behaviors and systems. In my world, we use them to model certain workflow of actual people working with components that are extremely intolerant of mistakes in state order. As in, "A had better happen before C or there will be a very serious problem. Make that be not possible right now."
You could look it up, but what the hell. Intuitively, a finite state automaton is an abstraction of something that has some finite number of states, and rules by which you can go from state to state. A state is something for which a true or false statement can be made, and a rule is a way that you change from one state to another. So, you could have, say, two states: "I'm at home" and "I'm at work" and two rules, "go to work" and "go home."
It turns out that you can look at machines like this mathematically, and find there are things they can and cannot do. Regular expressions are basically a way of describing a finite state machine in which the states are a set of different strings, and the rules move you from state to state based on the next character read. You can prove that. But you can also prove that no finite state machine can tell whether or not the parentheses in an expression are matched (via the pumping lemma for FSAs.)
The reason you should learn about FSAs is that they can be used to solve many problems: string matching, control of systems, business process descriptions, digital circuit design. They're also inherently pretty.
Formally, an FSA is a algebraic structure F = 〈Σ, S, s0, F, δ〉 where Σ is the input alphabet, S is a set of states, s0 ∈ S is a particular start state, F ⊆ S is a set of accepting states, and δ:S×Σ → S is the state transition function.
in OOP terms: if you have an object with methods that you call on certain events, and some (other) methods that have different behaviour depending on the previous calls.... surprise! you have a state machine!
now, if you know the theory, you don't have to rethink it all. you simply say: "piece of cake, it's just a state machine" and go on to implement it.
if you don't know the theory you'll think about it for a while, write some clever hacks, and get something that's difficult to explain and document... because you don't have the words to describe it
Good answers above. I would only add that FSA are primarily a thinking tool, not a programming technique. What makes them useful is they have nice properties, and anything that acts like one has those properties. If you can think of something as an FSA, there are many ways you can build it:
as a regular expression
as a state-transition table
as a while-switch-on-state loop
as a goto-net (horrors!)
as simple structured program code
etc. etc.
If somebody says something is a FSA, you can immediately know what they are talking about, no matter how it is built.
You need state machines whenever you have to release your thread before you have completed your operation.
Since web services are often not statefull, you don't usually see this in web services--you re-arrange your URL so that each URL corresponds to a single path through the code.
I guess another way to think about it could be that every web server is a FSM where the state information is kept in the URL.
You often see it when processing input. You have to release your thread before the input has all been completed, so you set a flag saying "input in progress" or something like that. When done you set the flag to "awaiting input". That flag is your state monitor.
More often than not, a FSM is implemented as a switch statement that switches on a variable. Each case is a different state. At the end of the case, you may set the state to a new value. You've almost certainly seen this somewhere.
The nice thing about a FSM is that you can make the state a part of your data rather than your code. Imagine that you need to fill out 1000 items in the database. The incoming data will address one of the 1000 items, but you generally don't have enough data to complete the operation.
Without an FSM you might have hundreds of threads waiting around for the rest of the data so they can complete processing and write the results to the DB. With a FSM, you write the state to the DB, then exit your thread. Next time you can check the incoming data, read the state from the thread and that should give you enough info to determine what code to run.
Nearly every FSM operation COULD be done by dedicating a thread to it, but probably not as well (The complexity multiplies based on number of states, whereas with a state machine the rise in complexity is more linear). Also, there are some conceptual design issues--examining your code at the state level is in some cases much easier than examining it at the line of code level.
Every programmer should know about them because they are an excellent tool for certain kinds of problems, where the usual 'iterative-thinking' approach would yield nasty, complex code.
A typical example is game AI, where NPCs have different states that change according to where the player is, something like:
NPC_STATE_IDLE
NPC_STATE_ALERT (player at less than 100 meters)
NPC_STATE_ENGAGE (player attacked NPC)
NPC_STATE_FLEE (low on health)
where a FSM can describe easily the transitions and help perform complex reasoning about the system the FSM is describing.
Important: If you are a "visual" style learner, stop everything you are doing and go to this link ... Right Now.
If you are a "visual" learner, here is an excellent link that gives a very accessible introduction.
Reanimator by Oliver Steele
It looks like you've already approved an answer, but if you appreciate "visual" introduction to new concepts, as is common, you really should check out the link. It is simply outstanding.
(Note: the link points to a discussion of DFA and NDFA in the context of regular expressions -- with animated interactive diagrams)
Yes! You could look it up!
http://en.wikipedia.org/wiki/Finite_state_machine
What it is is better answered on other sites (such as Wikipedia), because there are pretty extensive answers out there already.
Why you should know them: Because you probably implemented them already.
Any time your code has a limited number of possible states (that's the "finite state" part) and switches to another one once some input/event happend (that's the "machine" part) you've written a finite state machine.
It is a very common tool and knowing the theoretical basics for that, being able to reason about it and knowing how to combine two FSMs into a single one that does the same work can be a great help.
FSAs are great data structures to understand because any chance you have to implement them, you're working at the lowest level of computational complexity on the Chomsky hierarchy. A great example is in word morphology (how parts of words come together). A lot of work has been done to show that even the most severe cases can be analyzed in this extremely fast analytical framework. Take a look at Karttunnen and Beesley's work out of PARC.
FSAs are also a great place to start learning about machine learning concepts like hidden markov models, because in many ways, the problem can be broken down using the same ideas and vocabulary.
One item that hasn't been mentioned so far is the semantic equivalence of finite state automata and regular expressions. A regular expression can be compiled to a finite state automaton (this is how regex libraries work) and vice-versa.
FSA (including DFA and NFA) are very important for computer science and they are use in many fields including many fields. For instance hidden markov fields for speech recognition also regular expressions are converted to the FSA's before they are interpreted by the software and NLP (Natural Language Processing), AI (game programming), Robot Programming etc.
One of the disadvantage of FSA's are they are usually slow and usually hard to implement and hard to understand or visualize while reading the code, but they are good because they usually provide generic solutions to the problems and they are well-known with a lot of studies on FSA's.