I am taking part in a programming competition where the objective is writing a bot that can play a specific game.
The objective of the game is to earn a certain amount of points. You control multiple airships, that you move around, capture islands and navigate drones that carry treasure. You play against one opponent, turns happen simultaneously, and there is a time limit. You can move multiple ships and drones in one turn. You can program your bot in Python, Java or C#.
The exact details don‘t matter, just that each ship has around 15 options each turn (moving and shooting) and overall you have around 10000 different options for each turn (different configurations of airship movements and shooting)
Up until now I approached this competition naively, and haven‘t done anything exceptionally clever (for example, if near enemy, shoot). I have read about minimax algorithms, and I would really like to apply it here (or something similar), you can assume that I can tell the value of a state. My problem is the mass of options for each turn - which create an enourmous branching factor that doesnt let me get very deep.
Question 1: Is there a better, applicable approach to this problem? Perhaps deep-learning or something similar?
Question 2: Is there a way to minimize the branching factor? I`ve read about alpha-beta and similar algorithms, but nothing seems to do the job.
Any help would be much appreciated
The minimax algorithm seems to be natural for these kinds of problems. At first, the game will be modelled in a abstract way and then a solver is used to find the path from current situation to a gamestate which maximize the amount of points. A similar approach to minimax is GOAP, which was implemented in the 1970'er for Shakey the robot under the name STRIPS. But, GOAP and minimax has two problems: first, a abstract model of the game is needed (perhaps in PDDL or in Game Description Language) and second the state-space is to big.
An better alternative to planning is to use a Behavior Tree. Thats a static program which describes the behavior of an agent. No solver is needed and no complete modelling of the game is needed. Instead, a bottom up approach is used with multiple edit-compile-run iterations for finding the optimal behavior tree (Test-driven-development). To implement such programming approach a so called "reactive planner" has to be implemented first which is another word for a realtime scheduler. Thats a module whichs maps a behavior tree onto a gantt-chart for executing an action at a specific moment in time. As introduction, the unity3d Engine is a good starting point, which has a full behaviortree implementation out-of-the-box.
Related
I'd like to start off by saying that I'm new to StackOverflow and to Modelica.
My goal is to simulate the injector system of a Rotating Detonation Engine. Essentially this is a piping system from a tank to a rocket engine. This system will change depending on the experimental setup, so I chose Modelica (specifically OpenModelica) because of the re-usability of components. The flows encountered will be at high pressures and high flow rates (sustaining a detonation requires this), and choked flow will occur.
My question is this: does the standard "Fluid" library in Modelica allow for choked flow? I understand that a few valves model this, but will the current library be able to capture "choking" in a long rough pipe, or the small end of a converging pipe (basically anywhere choking can happen, despite it not being the design location for a choke)?
If yes, excellent. If not, is there a non-standard library available? Should I be looking at something other than Modelica? I am happy to work on making a new library, but before going through that work I thought I would check to see if anything already existed.
I have read through most of the "Media" and the basics of the "Fluid" libraries and I get the feeling that compressible flow is modeled as a means of increasing accuracy over in-compressible flow, but not to actually handle choked flow.
Thank you for your time. I hope everyone is keeping safe!
The pipe model in the Modelica library does not handle choked flows.
Adding a standard orifice in series with the pipe should help provided the 'zeta' value is adjusted to make the velocity at the orifice match with the speed of sound in the gas. In other words Modelica library does not provide a valid mean of modeling choked flows in pipes.
However, I found a very interesting library called FreeFluids (https://github.com/CarlosTrujilloGonzalez/FreeFluidsModelica) who does have a very good model for choked pipes. An example is provided with the library for a choked air flow in a 10m long diam. 50mm circular pipe. The model returns correct values for air.
I am trying to work out the best way to structure an application that in essence is a peak detection program. In my line of work I have been given charge of developing a system that essentially is looking at pulses in a stream of data and doing calculations on the peak data.
At the moment the software is implemented in LabVIEW. I'm sure many of you on here would understand why I'd love to see the end of that environment. I would like to redesign this in Scala (and possibly use Play if I was to make it use a web frontend) but I am not sure how best to approach the initial peak-detection component.
I've seen many tutorials for peak detection in various languages and I understand from a theoretical perspective many of the algorithms. What I am not sure is how would I approach this from the most Scala/Play idiomatic way?
Obviously I don't expect someone to write the code for me but I would really appreciate any pointers as to the direction I should take that makes the most sense. Since I cannot be too specific on the use case I'll try to give an overview of what I'm trying to do below:
Interfacing with data acquisition hardware to send out control voltages and read back "streams" of data.
I should be able to work the hardware side out, but is there a specific structure that would be best for the returned stream? I don't necessarily know ahead of time how much data I'll be reading so a stream that can be buffered and chunked would probably be appropriate.
Scan through the stream to find peaks and measure their height and trigger an event.
Peaks are usually about 20 samples wide or so but that depends on sample rate so I don't want to hard-code anything like that. I assume a sliding window would be necessary so peaks don't get "cut off" on the edge of a buffer. As a peak arrives I need to record and act on it. I think reactive streams and so on may be appropriate but I'm not sure. I will be making live graphs etc with the data so however it is done I need a way to send an event immediately on a successful detection.
The streams can be quite long and are at high sample-rates (minimum of 250ksamples per second) so I'd prefer not to have to buffer the entire stream to memory. The only information that needs to be permanent is the peak voltage data. I will need a way to visualise the raw stream for calibration purposes but I imagine that should be pretty simple.
The full application is much more complex and I'll need to do some initial filtering of noise and drift but I believe I should be able to work that out once I know what kind of implementation I should build on.
I've tried to look into Play's Iteratees and such but they are a little hard to follow. If they are an appropriate fit then I'm happy to work on learning them but since I'm not sure if that is the best way to approach the problem I'd love to know where I should look.
Reactive frameworks and the like certainly look interesting and I can see how I could really easily build the rest of the application around them but I'm just not sure how best to implement a streaming peak detection function on top of them beyond something simple like triggering when a value is over a threshold (as mentioned previously a "peak" can be quite wide and the signal is noisy).
Any advice would be greatly appreciated!
This is not a solution to this question but I'm writing this as an answer because of space/formatting limitations in the comments section.
Since you are exploring options I would suggest the following:
Assuming you have a large enough buffer to keep a window of data in memory (W=tXw) you can calculate the peak for the buffer using your existing algorithm. Next you can collect the next few samples data in a delta buffer (d) (a much smaller window). The delta buffer is the size of your increment. Assuming this is time series data you can easily create the new sliding window by removing the first delta (dXt) values from the buffer W and adding d values to the buffer. This is how Spark-streaming implements reduceByWindow function on a DStream. Iteratee can also help here.
If your system is distributed then you can use stream processing systems (Storm, Spark-streaming) to get better latency and throughput at the cost of distributing the system.
If you are really resource constrained and can live approximate results that bounded I would suggest you look at implementing a combination of probabilistic data structures such as count-min-sketch, hyperloglog and bloom filter.
background
I'm working on a group project to simulate some consensus algorithms used by a group of independent robots to form an arbitrary shape on a 2D plane. The robots are modeled as unit disks, and all run the same algorithm. Basically, each robot can move, wait, or observe its local environment at any moment, but cannot communicate explicitly with an other robots. We'd like to find a simulation or even 2d graphics library to help us without writing too much from scratch.
Question
Can anyone recommend a simulation library meeting the requirements below, which could be used for a multi-robot 2D simulation?
I've never coded a simulation before, so it's possible some of my concerns are readily addressed by many existing libraries. However, the Mason project is the only resource I've found that seems promising so far. Unfortunately, a few of our team members are not very proficient in Java, so I'd like to find something suitable in a different language, if possible.
Requirements
* language preference (descending order): python, c++, (maybe) java
* open source/FOSS recommendations only
* Options/flags to disable simulation: We plan on running several thousand trials of randomly generated shapes against each algorithm, so for the bulk of trials we don't care about any visual representation, just data. So the simulation logic has to be decoupled from the graphics components if this makes sense.
* collision detection
* Customizable visual representations: Within a simulation, we'd like to have several views (or toggles for a single view) that present additional information about each robot like current state, the area it's currently observing etc.
For such simple graphics you can surely get away with either pyqt or wxpython.
The simulation itself should be its own python module; the GUI should just load the module, then call its "timestep" function at regular intervals (timer, GUI idle callback, etc); the step function should evolve the robot system by one small time step.
The GUI should just display the simulation state. Avoid mixing everything (display and simulation) in one module, it'll get pretty messy, plus if your simulation engine is a separate module you can then also run it directly from the command line and look at the output file.
It would be pretty easy to write a python script that reads such output file and generates commands to represent it graphically in either excel or powerpoint using win32com, in which case you don't even need pyqt or wxpython.
For the collision detection, look at pybox2d.
Yes, I'm aware that speech recognition is fairly complicated (as an understatement). What I'm looking for is a method for distinguishing between maybe 20-30 phrases. An ability to split words (discrete speech is fine) would be nice, but isn't required. The software will be user-dependent(i.e. for use by me). I'm not looking for existing software, but for a good way of going about doing this myself. I've looked into various existing methods and it seems like splitting the sound into phonemes, while common, is somewhat excessive for my needs.
For some context, I'm just looking for a way to control some aspects of my computer with a few simple voice commands. I'm aware that Windows already has speech recognition software, but I'd like to go about this one myself as a learning exercise. Commands would be simple like "Open Google", or "Mute". What I had in mind (not sure if this is a good idea) is that some commands would be compound. So "Mute" would just be "Mute". Whereas the "Open" command could be recognized individually, and then have its suffixes (Google, Photoshop, etc). recognized with another network/model/whatever. But I'm not sure if looking for prefixes/word breaks in this way would produce better results than having to deal with an increased number of individual commands.
I've been looking into perceptrons, hopfield networks (though they're somewhat obsolete from what I understand) and HMMs, and while I understand the ideas behind these (I've implemented the ANNs before) I don't really know which is best suited to this task. I'm assuming that linear vector quantization models would also be appropriate, but I can't really find much literature to this end. Any guidance/resources would be greatly appreciated.
There are some open source project in speech recognition:
HTK (Hidden Markov Models Toolkit)
Sphinx
Both have decoder, training, language model toolkits. Eveything to build a complete and robust speech recognizer.
Voxforge has acoustic and language models for both open source speech recognition toolkits.
Some time ago, I read a whitepaper about a limited vocabulary system, which used a simple recognition process. The system divided each utterance into a small number of bins (6 in time, and 4 in magnitude, if I remember correctly, for 24 total), and all it did was count the number of sample audio measurements in each bin. There was a fuzzy logic rule base which then interpreted each utterances 24 bin counts, and generated an interpretation.
I imagine that (for some applications) a simple matching process might work just as well, in which the 24 bin counts of the current utterance are simple matched against those of each of your stored prototypes, and the one with the least overall difference is the winner.
What's the best way to "see what is happening" in an algorithm/data structure? If it's something like a binary search I just imagine a bunch of boxes in a row, and throwing half of them out each time. Is there something more powerful that will let us grok something as abstract as an algorithm/data structure?
Clarification: I'm looking for something a little more general. Example: in order to visualize time - some people use a clock in there head but thats slow, whereas a more natural feel would be a globe and if you are trying to get a 'feel' for how an algorithm works you can imagine two objects moving in different directions on that globe.
In general, animations are excellent for visualizing processes that occur over time, such as the execution of algorithms.
For example, check out these animations: Animated Sort Algorthms
Here's an animation that shows how the data structures work on MergeSort.
Now, whether you want to spend time hooking up your algorithm to some kind of animated visualization is a different question!
Algorithm animation was a big research area in the 1990s. Marc H. Brown, who was then at the Digital Systems Research Center, did a large amount of interesting work. The source code to his Zeus animation system is still available, and it would not be hard to get it set up. I played with Zeus years ago and if I remember correctly it ships with dozens of animations.
They had a couple of 'festivals' where non-experts got together to animate new algorithms. You can see one of the reports (with still images) on the 1993 festival. YouTube has one of their videos Visualizing Combinatorial Structures.
Describing something in terms of another thing is called analogy. You just did it with the binary search being a bunch of boxes. Just play with the student's prior knowledge.
For instance, trees can be thought of linked-lists, with multiple "next" nodes, or they could be explained to the uninitiated as something similar to a hierarchy.
As for concrete methods of explanation, graphs and state machines can be easily visualized with Graphviz. A very basic, directed graph can be expressed very simply:
digraph G {
A->B;
B->C;
C->D;
D->B;
}