I'm working with PDDL2.1 durative-actions and I'm having difficulty understanding the purpose of over all.
I have a function charge_level which is updated with a value every 10Hz. In a durative-action move, I say condition: (over all (>= (charge_level) 12)).
I interpreted this as "while performing the action, verify that charge_level is greater than or equal to 12, otherwise, move fails and the planner should find a new action with the condition at start (< (charge_level) 12)". However, the planner does not seem to plan that way. I appreciate any clarity on this.
Thanks!
The semantics of the over all condition is indeed as #haz says in his answer (it prevents the planner from scheduling another action in parallel with your move action that would violate the over all condition), but what I think is confusing you is the difference between planning and plan execution. During plan execution, the (charge_level) may drop below 12 at any point unexpectedly due to malfunctioning battery, or faulty sensor, etc.. At such occasion, your plan execution should stop the move action (and therefore the whole plan) and re-plan. At that point, the planner could choose any action, which has a satisfied pre-condition in that new state. So not necessarily at start (< (charge_level) 12).
The PDDL durative action cannot be stopped or paused by the planner, while computing the plan. However, if you tell the planner, how is the (charge_level) changing over time, it could compute the longest possible duration of the move action, and then do something else e.g. recharge battery, before scheduling another instance of the move action into the same plan. In that approach, there are no failures involved, just reasoning about how long a given action could last in order to achieve the goal without violating any constraints, including the over all conditions.
If that is the behavior you want, you will need to model the (charge_level) as a continuously changing function. If you want to see an example, here is the power generator or the coffee machine. Here is a peek from the Generator domain:
The generator must not run out of fuel:
(over all (>= (fuel-level ?g) 0))
The fuel decreases by 1 unit every unit of time #t.
(decrease (fuel-level ?g) (* #t 1))
Given the initial (fuel-level), it is a simple calculation to figure out the maximum duration of the action. For that flexibility, you will need to specify the action duration unconstrained :duration (>= ?duration 0), as in the coffee machine domain.
Now, to be able to process such model including continuous numeric effects, you will need a planner that supports the :continuous-effects requirement, so for example OPTIC, or POPF.
If you just want to prevent the action from happening based on a condition, then you use at start. The over all is for conditions that must hold for the full duration of the action. So you could interpret your condition as, "for the entire duration of moving, never let the battery level go below 12".
Related
We're struggling with some aspects of the following problem:
a public transportation bus timetable consists of shifts (~ track sections) each with fixed start and end times
bus drivers need to be assigned to each of those shifts
[constraint in question] legal regulations demand that each bus driver has a 30 min break after 4 hours of driving (i.e. after driving shifts)
put differently, a driver accrues driving time when driving shifts that must not exceed 4h unless the driver takes a 30 min break in which case the accrued time is "reset to zero"
In summary, we need to track the accrued driving time of each driver in order to suppress shift assignments to enforce the 30 min break.
The underlying problem seems to sit halfway between a job shop and an assignment problem:
Like job shop problems, it has shifts (or tasks, jobs) with many no-overlap and precedence constraints between them...
...BUT our shifts (~tasks/jobs) are not pre-assigned to drivers; in contrast with job shop problems, the tasks (~shifts) need to be executed on specific machines (~drivers) and are therefore pre-assigned, so assigning them is not part of the problem
Like assignment tasks, we need to assign shifts to as few as possible drivers...
...BUT we also need to handle the aforementioned no-overlap and precedence constraints, that are not taken into account in assignment problems
So my question is, how to best model the above constraint in a constraint problem with the or-tools?
Thanks in advance!
One general technique for specifying patterns in constraint programming is the regular constraint (in Gecode, Choco, MiniZinc, among others, unsure of the status for or-tools), where patterns of variables are specified using finite automata (DFAs and NFAs) or regular expressions.
In your case, assuming that you have a sequence of variables representing what a certain driver does at each time-point, it is fairly straight-forward to specify an automaton that accepts any sequence of values that does not contain mora than four consecutive hours of driving. A sketch of such an automaton:
States:
Driving states Dn representing n time units driving (for some resolution of time units), up to n=4 hours.
Break states DnBm for a break of length m after n time units of driving, up to m=30 minutes.
Start state is D0.
Transitions:
Driving: When driving 1 unit of time, move from state Dn to D(n+1), and from a break shorter than 30 minutes from DnBm to D(n+1).
Break of 1 unit of time, move from DnBm to DnB(m+1), unless the 30 minutes break time has been reached, for which the transition goes back to D0.
Other actions handled mostly as self-loops, depending on desired semantics.
Of course, details will vary for your specific use-case.
I want to approximate the Worst Case Execution Time (WCET) for a set of tasks on linux. Most professional tools are either expensive (1000s $), or don't support my processor architecture.
Since, I don't need a tight bound, my line of thought is that I :
disable frequency scaling
disbale unnecesary background services and tasks
set the program affinity to run on a specified core
run the program for 50,000 times with various inputs
Profiling it and storing the total number of cycles it had completed to
execute.
Given the largest clock cycle count and knowing the core frequency, I can get an estimate
Is this is a sound Practical approach?
Secondly, to account for interference from other tasks, I will run the whole task set (40) tasks in parallel with each randomly assigned a core and do the same thing for 50,000 times.
Once I get the estimate, a 10% safe margin will be added to account for unforseeble interference and untested path. This 10% margin has been suggested in the paper "Approximation of Worst Case Execution time in Preepmtive Multitasking Systems" by Corti, Brega and Gross
Some comments:
1) Even attempting to compute worst case bounds in this way means making assumptions that there aren't uncommon inputs that cause tasks to take much more or even much less time. An extreme example would be a bug that causes one of the tasks to go into an infinite loop, or that causes the whole thing to deadlock. You need something like a code review to establish that the time taken will always be pretty much the same, regardless of input.
2) It is possible that the input data does influence the time taken to some extent. Even if this isn't apparent to you, it could happen because of the details of the implementation of some library function that you call. So you need to run your tests on a representative selection of real life data.
3) When you have got your 50K test results, I would draw some sort of probability plot - see e.g. http://www.itl.nist.gov/div898/handbook/eda/section3/normprpl.htm and links off it. I would be looking for isolated points that show that in a few cases some runs were suspiciously slow or suspiciously fast, because the code review from (1) said there shouldn't be runs like this. I would also want to check that adding 10% to the maximum seen takes me a good distance away from the points I have plotted. You could also plot time taken against different parameters from the input data to check that there wasn't any pattern there.
4) If you want to try a very sophisticated approach, you could try fitting a statistical distribution to the values you have found - see e.g. https://en.wikipedia.org/wiki/Generalized_Pareto_distribution. But plotting the data and looking at it is probably the most important thing to do.
We are working on a Algorithmic trading software in C#. We monitor Market Price and then based on certain conditions, we want to buy the stock.
User input can be taken from GUI (WPF) and send to back-end for monitoring.
Back - end receives data continuously from Stock Exchange and checks if user entered price is met with certain limits and conditions. If all are satisfied, then we will buy / sell the stock (in Futures FUT).
Now, I want to design my Back end service.
I need Task Parallel Library or Custom Thread Pool where I want to create my tasks / threads / pool when application starts (may be incremental or fixed say 5000).
All will be in waiting state.
Once user creates an algorithm, we will activate one thread from the pool and monitors price for each incoming string. If it matches, then buy / sell and then go into waiting state again. (I don't want to create and destroy the threads / tasks as it is time consuming).
So please can you guys help me in this regard? If the above approach is good or do we have any other approach?
I am struck with this idea and not able to go out of box to think on this.
The above approach is definitely not "good"
Given the idea above, the architecture is wrong in many cardinal aspects. If your Project aspires to survive in 2017+ markets, try to learn from mistakes already taken in 2007-2016 years.
The percentages demonstrate the NBBO flutter for all U.S. Stocks from 2007-01 ~ 2012-01. ( Lower values means better NBBO stability. Higher values: Instability ) ( courtesy NANEX )
Financial Markets operate on nanosecond scales
Yes, a few inches of glass-fibre signal propagation transport delay decide on PROFIT or LOSS.
If planning to trading in Stock Markets, your system will observe the HFT crowd, doing dirty practice of Quote Stuffing and Vacuum-Cleaning 'em right in front of your nose at such scales, that your single-machine multi-threaded execution will just move through thin-air of fall in gap already created many microseconds before your decision took place on your localhost CPU.
The rise of HFT from 2007-01 ~ 2012-01 ( courtesy NANEX ).
May read more about an illusion of liquidity here.
See the expansion of Quotes against the level of Trades:
( courtesy NANEX )
Even if one decides to trade in a single instrument, on FX, the times are prohibitively short ( more than 20% of the ToB Bids are changed in time less than 2 ms and do not arrive to your localhost before your trading algorithm may react accordingly ).
If your TAMARA-measurements are similar to this, at your localhost, simply forget to trade in any HF/MF/LF-HFT instruments -- you simply do not see the real market ( the tip of the iceberg ) -- as the +20% price-events happen in the very first column ( 1 .. 2 ms ), where you do not see any single event at all!
5000 threads is bad, don't do that ever, you'll degrade the performance with context switch loss much more than parallel execution timing improvement. Traditionally the number of threads for your application should be equal to the number of cores in your system, by default. There are other possible variants, but probably they aren't the best option for your.
So you can use a ThreadPool with some working item method there with infinite loop, which is very low level, but you have control on what is going on in your system. Callback function could update the UI so the user will be notified about the trading results.
However, if you are saying that you can use the TPL, I suggest to consider these two options for your case:
Use a collection of tasks running forever for checking the new trading request. You still should tune up the number of simultaneously running tasks because you probably don't want them to fight each other for a CPU time. As the LongRunning tasks are created with dedicated background thread, many of them will degrade your application performance as well. Maybe in this approach you should introduce a strategy pattern implementation for a algorithm being run inside the task.
Setup a TPL Dataflow process within your application. For such approach your should encapsulate the info about the algorithm inside a DTO-object, and introduce a pipeline:
BufferBlock for storing all the incoming requests. Maybe you can use here a BroadcastBlock, if you want to check the sell or buy options in parallel. You can link the block with a boolean predicate here so the different block will process different types of requests.
ActionBlock (maybe one block for each algorithm from user) for processing the algorithmic check for a pattern based on which you are providing the decision.
ActionBlock for storing all the buy / sell requests for a data successfully passed by the algorithm.
BufferBlock for UI reaction with a Reactive Extensions (Introductory book for Rx, if you aren't familiar with it)
This solution still has to be tuned up with a block creation options, and more informative for you how exactly your data flow across the trading algorithm, the speed of the decision making and overall performance. You should properly examine for a defaults for TPL Dataflow blocks, you can find them into the official documentation. Other good place to start is Stephen Cleary's introductory blog posts (Part 1, Part 2, Part 3) and the chapter #4 about this library in his book.
With C# 5.0, the natural approach is to use async methods running on top of the default thread pool.
This way, you are creating Tasks quite often, but the most prominent cost of that is in GC. And unless you have very high performance requirements, that cost should be acceptable.
I think you would be better with an event loop, and if you need to scale, you can always shard by stock.
Greetings SO denizens!
I'm trying to architect an overhaul of an existing NodeJS application that has outgrown its original design. The solutions I'm working towards are well beyond my experience.
The system has ~50 unique async tasks defined as various finite state machines which it knows how to perform. Each task has a required set of parameters to begin execution which may be supplied by interactive prompts, a database or from the results of a previously completed async task.
I have a UI where the user may define a directed graph ("the flow"), specifying which tasks they want to run and the order they want to execute them in with additional properties associated with both the vertices and edges such as extra conditionals to evaluate before calling a child task(s). This information is stored in a third normal form PostgreSQL database as a "parent + child + property value" configuration which seems to work fairly well.
Because of the sheer number of permutations, conditionals and absurd number of possible points of failure I'm leaning towards expressing "the flow" as a state machine. I merely have just enough knowledge of graph theory and state machines to implement them but practically zero background.
I think what I'm trying to accomplish is at the flow run time after user input for the root services have been received, is somehow compile the database representation of the graph + properties into a state machine of some variety.
To further complicate the matter in the near future I would like to be able to "pause" a flow, save its state to memory, load it on another worker some time in the future and resume execution.
I think I'm close to a viable solution but if one of you kind souls would take mercy on a blind fool and point me in the right direction I'd be forever in your debt.
I solved similar problem few years ago as my bachelor and diploma thesis. I designed a Cascade, an executable structure which forms growing acyclic oriented graph. You can read about it in my paper "Self-generating Programs – Cascade of the Blocks".
The basic idea is, that each block has inputs and outputs. Initially some blocks are inserted into the cascade and inputs are connected to outputs of other blocks to form an acyclic graph. When a block is executed, it reads its inputs (cascade will pass values from connected outputs) and then the block sets its outputs. It can also insert additional blocks into the cascade and connect its inputs to outputs of already present blocks. This should be equal to your task starting another task and passing some parameters to it. Alternative to setting output to an value is forwarding a value from another output (in your case waiting for a result of some other task, so it is possible to launch helper sub-tasks).
I'm looking for a design pattern that would fit my application design.
My application processes large amounts of data and produces some graphs.
Data processing (fetching from files, CPU intensive calculations) and graph operations (drawing, updating) are done in seperate threads.
Graph can be scrolled - in this case new data portions need to be processed.
Because there can be several series on a graph, multiple threads can be spawned (two threads per serie, one for dataset update and one for graph update).
I don't want to create multiple progress bars. Instead, I'd like to have single progress bar that inform about global progress. At the moment I can think of MVC and Observer/Observable, but it's a little bit blurry :) Maybe somebody could point me in a right direction, thanks.
I once spent the best part of a week trying to make a smooth, non-hiccupy progress bar over a very complex algorithm.
The algorithm had 6 different steps. Each step had timing characteristics that were seriously dependent on A) the underlying data being processed, not just the "amount" of data but also the "type" of data and B) 2 of the steps scaled extremely well with increasing number of cpus, 2 steps ran in 2 threads and 2 steps were effectively single-threaded.
The mix of data effectively had a much larger impact on execution time of each step than number of cores.
The solution that finally cracked it was really quite simple. I made 6 functions that analyzed the data set and tried to predict the actual run-time of each analysis step. The heuristic in each function analyzed both the data sets under analysis and the number of cpus. Based on run-time data from my own 4 core machine, each function basically returned the number of milliseconds it was expected to take, on my machine.
f1(..) + f2(..) + f3(..) + f4(..) + f5(..) + f6(..) = total runtime in milliseconds
Now given this information, you can effectively know what percentage of the total execution time each step is supposed to take. Now if you say step1 is supposed to take 40% of the execution time, you basically need to find out how to emit 40 1% events from that algorithm. Say the for-loop is processing 100,000 items, you could probably do:
for (int i = 0; i < numItems; i++){
if (i % (numItems / percentageOfTotalForThisStep) == 0) emitProgressEvent();
.. do the actual processing ..
}
This algorithm gave us a silky smooth progress bar that performed flawlessly. Your implementation technology can have different forms of scaling and features available in the progress bar, but the basic way of thinking about the problem is the same.
And yes, it did not really matter that the heuristic reference numbers were worked out on my machine - the only real problem is if you want to change the numbers when running on a different machine. But you still know the ratio (which is the only really important thing here), so you can see how your local hardware runs differently from the one I had.
Now the average SO reader may wonder why on earth someone would spend a week making a smooth progress bar. The feature was requested by the head salesman, and I believe he used it in sales meetings to get contracts. Money talks ;)
In situations with threads or asynchronous processes/tasks like this, I find it helpful to have an abstract type or object in the main thread that represents (and ideally encapsulates) each process. So, for each worker thread, there will presumably be an object (let's call it Operation) in the main thread to manage that worker, and obviously there will be some kind of list-like data structure to hold these Operations.
Where applicable, each Operation provides the start/stop methods for its worker, and in some cases - such as yours - numeric properties representing the progress and expected total time or work of that particular Operation's task. The units don't necessarily need to be time-based, if you know you'll be performing 6,230 calculations, you can just think of these properties as calculation counts. Furthermore, each task will need to have some way of updating its owning Operation of its current progress in whatever mechanism is appropriate (callbacks, closures, event dispatching, or whatever mechanism your programming language/threading framework provides).
So while your actual work is being performed off in separate threads, a corresponding Operation object in the "main" thread is continually being updated/notified of its worker's progress. The progress bar can update itself accordingly, mapping the total of the Operations' "expected" times to its total, and the total of the Operations' "progress" times to its current progress, in whatever way makes sense for your progress bar framework.
Obviously there's a ton of other considerations/work that needs be done in actually implementing this, but I hope this gives you the gist of it.
Multiple progress bars aren't such a bad idea, mind you. Or maybe a complex progress bar that shows several threads running (like download manager programs sometimes have). As long as the UI is intuitive, your users will appreciate the extra data.
When I try to answer such design questions I first try to look at similar or analogous problems in other application, and how they're solved. So I would suggest you do some research by considering other applications that display complex progress (like the download manager example) and try to adapt an existing solution to your application.
Sorry I can't offer more specific design, this is just general advice. :)
Stick with Observer/Observable for this kind of thing. Some object observes the various series processing threads and reports status by updating the summary bar.