#finite state machines

[trialofsacrifice]

In this blog post, our Lead Programmer, Teegan, talks about the creation of the Skeleton AI that Viktor fights in the dungeon.

[rupasriymts]

Best VLSI - Finite State Machines Projects for engineering students

In the dynamic landscape, Finite State Machine Project provide a systematic means of represented the dynamic world of network with a finite number of distinct condition. These states summarize specific design or modes of province that the system may reside at any given moment. Takeoff Edu Group provides this projects with innovative and unique titles. Conversion between states report the growth of the system's behaviour in response to stimuli or events, change the essence of approach dynamics.

Finite State Machines (FSMs) are basic models used in computer science and engineering to understand how systems work. They show the different situations a system can be in, how it changes between these situations, and what actions it takes. Visual diagrams, like state transition diagrams, help us understand FSMs better. These diagrams show how the situations are connected and help us see important points in how the system behaves.

The Example Title of Finite State Machine Project –Takeoff Edu Groups

Trendy:

Fully Automated Traffic Light Controller system for a four-way intersection using Verilog

Experience seamless traffic flow with our Fully Automated Traffic Light Controller system designed for four-way intersections. Built using Verilog, our solution ensures efficient and safe navigation for vehicles and pedestrians alike

Approach for Implementation of Vending Machine through Verilog HDL

Implementing a Vending Machine using Verilog HDL: A robust approach for digital logic design enthusiasts to create efficient vending solutions. Harnessing Verilog HDL's power, this implementation ensures seamless operation and precise functionality

Design and implementation of subway automatic ticketing system based on Verilog HDL

Revolutionize urban commuting with our Verilog HDL-based Subway Automatic Ticketing System. Seamlessly designed and implemented for efficient, hassle-free travel experiences underground. Experience the future of subway ticketing today.

There are two main types of FSMs: deterministic and non-deterministic. Deterministic FSMs have clear rules for how they change between situations. Non-deterministic FSMs have more flexible rules, allowing for different changes from the same situation.

FSMs are useful in many areas, like making software, designing hardware, and building robots. They help us create and understand systems by showing us how they work in different situations. Overall, FSMs are essential tools for engineers to model and understand how systems behave in both theoretical and practical situations.

Finite State Machines projects (FSMs) aren't just models for understanding systems; they're also tools for making new things happen. By using FSMs in Takeoff Edu group and other attempt, we're not just solving problems – we're opening doors to new ideas and progress. As we keep using FSMs to explore and create, we're pushing technology and engineering forward, finding better ways to do things and making exciting discoveries along the way. The Above titles are the examples of Takeoff Edu group Finite State Machines.

[1ucasvb]

Animation I've done for explaining part of my research.

[howdoesone]

How does one use computational methods to analyze and process morphologically complex words in a language?

Computational methods have become increasingly important in the field of linguistics, particularly in the study of morphologically complex languages. These methods allow linguists to analyze and process large amounts of data in a way that would be impossible using traditional methods. In this article, we will explore how computational methods can be used to analyze and process morphologically…

[max1461]

One thing that was hard for me to get used to when I started learning math was what I call "static thinking". Math doesn't have any time evolution; everything either is or it isn't.

When non-mathematicians think about operations like addition, they think of them as "processes" that "occur": you take 2 and 8 and "combine them" to get 10. The expression "2+8" is like a sort of command, telling you to perform this process of addition. People think of math this way because it's basically how math is presented in schools.

To a mathematician, the expression "2+8" is not a command and it does not signify a process. "2+8" is merely another way of writing "10". They are two expressions with identical meaning. That's what "2+8=10" means, it means "these two expressions signify the same thing". There is no "process of addition" which "happens" and "results in 10". "10" and "2+8" are just alternate spellings of the same number.

For a more advanced example, consider the formal definition of a finite state machine. Intuitively, we think of a finite state machine as a network with various nodes and directed edges and so on, into which we input some string in the machine's alphabet. After inputting the string, it travels around the machine according to the transition functions before finally arriving (or not) at a final node, and by this process a computation is performed. Of course, mathematically, this is nonsense. A finite state machine is a network with various nodes and directed edges and so on, but the notion that you can "input a string" and it will "travel around the network via the transition functions" is bullshit. A string is recognized by the machine if and only if there exists a valid path for that string via the transition functions from an initial node to a final node. The string never actually travels the path, because such a notion does not exist in mathematics.

A finite state machine is not a machine, it never actually does anything. It sits there in the realm of abstractions, unmoving and static. Every string which it "recognizes" it recognizes by dint not of things that it does but of facts that simply are; every string recognized by the machine is so and has been so since the dawn of time, without the machine ever in fact going about the process of recognizing it.

This is philosophically a little bit trippy, but it can also confuse early math students in practice, too. As I mentioned at the top, I was very confused by it. For instance, in the finite state machine example, a perfectly ordinary statement to encounter in a proof might run something like

[Block of reasoning establishing that some string w is recognized by the machine M] [Block of reasoning establishing that all transition functions into a final node F of M have label x] ...since w is recognized by the machine M, there must exist a transition function T whose target is a final node and which sends w to that final node on the last character of w. Thus, since T must have label x, the final character of w is x.

To a mathematician this seems perfectly trivial. To me as a young math student, this kind thing seemed almost miraculous. We don't even know what w is, and yet we can run it through the machine? And from the fact that the machine recognized it, we can conclude things about what w is? We can tell its final character? How is that possible? I felt like this kind of thing involved "reaching into the future", reasoning about processes from the end when we haven't even begun them yet.

But, of course, we can do this, because there is no past or future in mathematics. The machine is simple there, the string is simply recognized or not, its last character simply is x or it isn't x. Nothing has to "happen".

[debian-official]

You ask me what my gender is and I grab pen&paper from my bag and start drawing a finite state machine

[s-lycopersicum]

some of your never modeled ui components using finite-state machines and it shows

[cetaceanhandiwork]

the conversation around generative neural networks is a dumpster fire in a dozen different ways but I think the part that disproportionately frustrates me, like on an irrational pet peeve level, is that nobody in that conversation seems to understand automata theory

back before most of these deep learning techniques were a twinkle in a theorist's eye, back when computing was a lot less engineering and a lot more math, computer scientists had worked out the math of different "classes" of computer system and what kinds of problems they could and couldn't solve

these aren't arbitrary classifications like most taxonomy turns out to be. there's qualitative differences. you can draw hard lines: "it takes class X or above to run programs with Y trait", and "only class X programs or below are guaranteed to have Y trait". and all of those lines have been mathematically proven; if you ever found a counterexample, then we'd be in "math is a lot of bunk" territory and we'd have way bigger things to worry about

this has nothing to do with how fast/slow the computer system goes; it's about "what kinds of program can it run at all". so it includes emulation and such. you can emulate a lower system in a higher one, but not vice versa

at the top of this heap is turing machines, which includes most computers we'd bother to build. there's a lot of programs that it's been mathematically proven require at least a turing machine to run. and this class of programs includes a lot of things that humans can do, too

but with this power comes some inevitable restrictions. for example, if you feed a program to a turing machine, there's no way to guarantee that the program will finish; it might get stuck somewhere and loop forever. in fact there's some programs that you straight up can't predict whether they'll ever finish even if you're looking at the code yourself

these two are intrinsically linked. if your program solves a turing complete problem, it needs a turing machine; nothing less will do. and a turing machine is capable of running all such programs, given enough time.

ok. great. what does any of that jargon have to do with AI?

well... the important thing to know is that the machine learning models we're using right now can't loop forever. if they could loop forever they couldn't be trained. for any given input, they'll produce an output in finite time

which means... well, any program that requires a turing machine to run, or even requires a push-down automaton to run (a weaker type of computer system that can get into infinite loops but that you can at least check ahead of time if a program will get stuck or not), can't be emulated by these systems. they've got to be in the next category down: finite state machines at most - and thus unable to compute, or emulate computation of, programs that inhabit a higher tier

and there is a heck of a lot of stuff we conceptualize as "thinking" that doesn't fit in a finite state machine

...I suspect it will some day be possible for a computer program to be a person. I am absolutely certain that when that day comes, the computer program who's a person would require at least a turing machine to run them

what we have right now isn't that. what we have right now is eye spots on moths, bee orchids, mockingbirds. it might be "artificial intelligence", depending on your definition of "intelligence", but prompt it to do things that we've proven only a turing machine can do, and it will fall over

and the reason I consider this an "irrational pet peeve" and not something more severe? is because this information doesn't actually help solve policy questions! if this is a tool, then we still need to decide how we're going to allow such tools to be built, and used. it's not as simple as a blanket ban, and it's not as simple as letting the output of GNNs fully launder the input, because either of those "simple" solutions are rife for abuse

but I can't help but feel like the conversation is in part held back by specious "is a GNN a people" arguments on the one hand, and "can a GNN actually replace writers, or is it just fooling execs into thinking it can" arguments on the other, when the answer to both seems to me like it was solved 40 years ago

[multidimensionalsock]

I HAVE A MOCK INTERVIEW IN 20 MINS so have a summary of every game programming pattern except 6. You can find out more about them here, as that's what I'm summarising from

command

Putting an extra layer between the input and the execution that allows you to swap out what each input actually does at run time, it also means that the input doesn't call the execution directly meaning you can delay it. Most known for its use in undo and redoing actions.

flyweight

Pulling all common data from an object which will be displayed multiple times, loading it into memory once and referencing this data wherever it needs to be displayed on screen. Each instance of the object will hold its own unique parameters but will be far more lightweight. E.g. when displaying trees the model and basic data will be held as common data and only loaded once. but the position will be stored in the instance

Observer

Using events to decouple different systems in the game. A script sends out an event and any class which may need to know about it will add a listener with an attached function to implement if that event is called. Doesn't require classes to explicitly know each other, and the broadcasting class shooting off the event needs no knowledge of its listeners.

prototype

Creating a parent class with base virtual functions with the basic functionality for a given set of objects, then overriding those functions in its child classes for custom behaviour (if necessary). Means that a set of objects always has the same function names for another class to call on and reduces how much code is needed when child Classes do not need to override functions to their own implementation.

singleton

A class which can only have one instance and has global access to it. Is initialized at runtime and only if needed. However they're global, discourage concurrency and encourages coupling

State

Creating a set of states and transitions (finite state machine) with different functionalities and behaviours you can switch between when certain conditions are met. You can only be in one state at a time.

double buffer

Having two frame buffers, one displaying the current frame and another loading the next one. It then swaps the frames one in once it's fully loaded rather than writing over the current frame which can lead to rendering errors.

game loop

Functionality that loops so long as gameplay is active e.g. update or tick (called each frame), fixed update (called at a fixed interval 50 times a second)

update

All a game objects behaviour is kept within its own update rather than the main game loops update. The main game loop calls on these update functions each frame and then the object handles its own behaviour.

event queue

Storing requests in first in first out to prevent them being processed simultaneously. Allows for control over when certain actions happen.

type object

Instead of inheriting from a class directly, have two classes which reference each other, one which stores the objects base data that's similar across all objects (e.g. animal, pathfinding) and one which stores specific information and implementation (e.g. Breed) allows for creation of lots of different breeds just by changing parameters instead of creating new inherited children of animal

dirty flag

When primary data (e.g transform data) changes on an object it sets its flag to dirty which means it and it's child objects needs updating. Rather than redoing and updating calculations every frame when the data is the same as before.

[agatedragongames]

Coding a bouncing slime in Processing

Learn how to code a bouncing slime using forces and the finite state machine pattern. You will learn how to split your code up into an air state and a ground state.

In the ground state the slime will wait, then launch into the air state using an impulse force. In the air state the slime will rise into the air and then start to fall due to gravity until it hits the ground.

[reality-detective]

Biden’s Impeachment Looms as Congress Unleashes a Storm of Revelations!, Conspiracy Cabal on Telegram

The clock is ticking, and the removal of Joe Biden from power has begun! Congress is gearing up to announce the dates for his impeachment, marking a pivotal moment in American history. But that’s not all – House Speaker McCarthy drops a bombshell, hinting at the potential impeachment of Attorney General Merrick Garland for mishandling the Hunter Biden investigation.

These strategic moves are poised to shake the very foundations of the mainstream media. Finally, the impeachment hearings will force their reluctant hand, compelling them to report on the crimes and misdemeanors linked to Biden’s impeachment. Prepare for a seismic awakening as millions of Democratic voters are red-pilled, confronted with the shocking revelations surrounding their beloved president and the notorious Hunter Biden laptop.

CNN, a prominent player in this unfolding drama, will lead the charge in broadcasting the impeachment proceedings. Could this be a sign of their gradual transformation, as they slowly flip their narrative and challenge the established order? And behind the scenes, whispers echo of a monumental interview where Trump returns to CNN, leaving the MSM, CIA, and FBI in a frenzy. CNN holds the key to unveiling information that has long been suppressed.

But let’s dig deeper. Who wields the unseen hand, manipulating the very fabric of reality? Who has captured the attention of Twitter, orchestrated changes within CNN, and exposed classified military operations in Ukraine? This enigmatic force fights against the looming threat of a fake alien invasion, safeguarding the interests of Trump and RFK Jr., and shedding light on the dark underbelly of child trafficking and human exploitation. They are the architects of the Great Awakening, leading humanity towards truth and liberation.

And now, in an unprecedented turn of events, Congress uncovers a treacherous web of deceit surrounding the fake alien invasion. Classified military operations, hidden deep within the underground machinations of the deep state, are exposed. These operations harbor groundbreaking technologies that could revolutionize the world, liberating us from the shackles of oil and gas dependency. Prepare for a seismic shift as the grip of the elite cabal, who profit from these finite resources, is shattered.

But that’s not all. Brace yourself for the next chapter in the fight against human trafficking, as Congress prepares to launch an all-out assault on this heinous industry. Epstein, JP Morgan, the Clintons, and Biden are just a few names that will be dragged into the spotlight. The dark secrets of Pizzagate, once concealed by the CIA’s mockingbird operations, will resurface. The time for truth and justice is at hand, exposing the sinister forces that have preyed upon our society for far too long.

Stay tuned, for timing is everything. The stage is set for a dramatic unraveling, where the powers that be will be held accountable. The truth will prevail, and a new dawn is on the horizon.

- Julian Assange

The Storm is Brewing 🤔

[felixcloud6288]

"The alphabet is the set of all individual characters the machine is able to recognize, while an input is a string of those characters. For example my keyboard contains the alphabet that my computer, the machine, can recognize and this *Smacks keyboard multiple times with an open palm* is an input I can give my machine."

-Me during office hours explaining some terms related to finite state machines.

[stevebattle]

Ants: Anita, Sandra, Kisha, Tracie, Niqi, and Hope (1995) by James McLurkin, MIT AI Lab, Cambridge, MA. There were six robot ants, all with womens’ names since all worker ants are female. We see McLurkin with his ‘Ant Farm’, and before and after images of the ants foraging for ‘food’, then responding to signals from the first ant to find it. “The Ants are programmed with a subset of Brooks' Subsumption Architecture implemented in 6811 assembly language. A program for the Ants would consist of a group of behaviors, arranged in a hierarchy. … A behavior is a small piece of code that acts like a finite state machine. … This is an effective method of providing the robot with a response for every possible sensory input, without having to explicitly program for every condition. Summing the responses of many behaviors is a much easier task that looking at all of the sensory inputs and then trying to decide what to do. As a result, the robots can exhibit surprisingly complex actions with a very small amount of software. … When a robot finds food, she stops and transmits I-found-food through her IR beacon emitter. Other members in the vicinity that detect this signal head towards her, transmitting I-see-a-robot-with-food from their beacon emitters. Any robot that detects this secondary signal heads towards it until they receive the primary signal, then the head towards the first robot. In this manner, many robots can be vectored towards a large food source quickly.” – The Ants: A Community of Microrobots by James McLurkin.

[morsking]

earlier today some comrades and i had a discussion on dialectical materialism and we spent a lot of time talking about the unity of opposites. the unity of opposites argues the way in which concepts are understood is by comparing things that exist in opposition. opposites can only exist in relation to one another and cannot be inherently defined in isolation. the example given in rob sewell's text is light and darkness, cold and heat, life and death, the finite and infinity, thinking and being, among others. we understand that in existence, under the laws of physics as we understand them, all matter must change and move and though things can be opposites and therefore seem irreconcilable, the fact of the matter (heh) is that just like the yin and yang, you will find in everything traces of its opposite. that is why under capitalism, you will inevitably find the seeds of socialism germinating, and once socialism flourishes, within you'll find the seeds of communism. this is because capitalism in itself came from the seeds of feudalism, and that too came from a pre-existing shape of society that kept evolving in an endless chain of cause and effect where conditions necessitated change. and that chain predates and will likely transcend communism into a new form of society, on and on, at least according to how i understood the text.

understanding this gave me a greater appreciation of the politics of the film snowpiercer, which i just watched with my roommate tonight, purely on a whim. the engine of the titular train is touted as eternal. it'll never stop and it'll never die, and as such humanity can keep existing long after the earth was frozen. the inherent contradiction of this statement is the engine itself needs constant maintenance. it is only as eternal as its creator, wilford, can keep throwing parts (and eventually, people) into it to keep it running. no matter what, the engine, though being described as infinite and mechanical, still exists within the constraint of that which is human to keep moving. and that contradiction is underscored by the fact the seemingly eternal (infinite) engine can only exist in the constrained (finite) space of the train which is stressed to have a head (front, beginning) and a tail (back, end).

thought and therefore people exist in a state that is markedly limited. an alternative form of existence that does not split people into the binary of front (bourgeois) and tail (proletariat) passengers is violently discouraged, because the existence of the train and dominion over it by wilford can only justify themselves through that dialectic, that life can only happen inside and that death is the only thing that waits outside. there is an extended contradiction in that message in the form of wilford culling the population at the back of the train periodically so the train can sustain itself by not straining its stockpiled resources. meaning that even inside the supposed sanctuary of the train, people will die arbitrarily. wilford and his cronies all stress this is natural, that there is a preordained relationship of sacrifices and beneficiaries, and those at the tail are the intended sacrificed who must not rise above their station (heh). furthermore, this highlights the contradiction that though humanity survives on the train, everyone is, in some form or another, objectified and dehumanized within it to justify the absurd inequality and abuses that occur within as people are thought as parts of a machinery that need to be in place for it to work. this metaphor is made literal by how children from the tail younger than 5 become unwilling replacements for extinct parts of the engine, meaning the system, the machine, only works by breaking people so profoundly they cease to think of themselves as human and can only think of themselves as machinery.

this is all so wilford can enjoy the luxuries of the train unimpeded and undisturbed, far away from the squalor of the tail. he has built the train, yet has prevented himself from running its whole length, of seeing the end to his beginning. this is in opposition to protagonist curtis, who has seen the train end to end and thus can see it for what it is in a way wilford thinks is identical to his yet could not be more different. wilford doesn't have a high opinion of humanity, as he tells curtis that people are naturally selfish and destructive despite the fact he built the train in such a way that deliberately fostered conditions that turned people desperate for food at the tail to resort to murder and cannibalism and people at the front to be selfish and destructively hedonistic. he should have no reason to want humanity to survive if he's that convinced humanity is inherently unworthy, and yet he knows he can only enjoy leadership, recognition, and comfort by having people to exist below him and shape their behavior to conform to his need of preserving a stagnant hierarchy he is aware he can only enjoy so long as he is alive. he wants the train to be his legacy, for it to be eternal in a way he cannot be as a mortal. humanity in the train will stay the same and he has no hope of the world outside being different, and if the world outside cannot be different then humanity is also forbidden from being different. within the train you cannot think, you can only be, yet you can only be because one the thought of how you can exist has been created by someone else who operates under a self-serving and restricted view of what humanity is. wilford's definition of humanity in nature only works in an artificial space.

but dialectical materialism is about change and evolution, about events that build up over time and burst into a force that sets things in motion once more for the next major event to happen. though wilford had thought he created a societal structure that was permanent, he unwittingly set the conditions for individuals who wanted to overturn it and discard it to rise and succeed. that is why namgoong, the train's security specialist, wants to cause a literal explosion that will blow the train apart and free him and his daughter yona from existence within the train. he has noticed that for years, the ice covering the earth has melted gradually, meaning that heat is slowly restoring in the frozen world and it just needs a push to keep falling apart and uncover what remains. within a frozen world, there was the undeniable and real potential for a world that thawed. sooner or later, there would not be a need for the train as the world changes, and people would have to (re-)learn to live in this changed world, themselves needing to change the relationship they have to it and themselves. which is why neither yona nor timmy, a boy kidnapped to be a replacement part in the engine, have surviving parents or comrades at the end of the movie. they too must learn to survive in a world with traces of life without their predecessors to guide them. and that is why the message of the movie is to embrace change and transform throughout the trajectory of socioeconomic change from one structure to another rather than limit humanity, a species that constantly needs to overcome its limits collectively, rather than individually, to survive as a whole.

[ranidspace]

The Millennium Prize Problems are a set of 7 mathematical problems that each have a one million dollar prize if you are the first to find a correct solution of the problem. (simplified, very bad) Explanations for all of them in the bottom, but feel free to reblog as to why your favourite is the best

The Riemann Hypothesis: For the following equation,

The Hypothesis states that all non-trivial zeroes lie on the n=1/2+ti line, where t is a real number, and i is the imaginary unit

Yang-Mills Theory and the Mass Gap Hypothesis: The Yang-Mills Theory explains the "Standard Model". Which is the behaviour and interactions between elementary particles, such as quarks, gluons, and leptons. It however does not explain the interactions in the nucleus. To prove it you must prove that the Yang-Mills theory holds true for any "compound simple gauge group", and the mass of all particles predicted by it must be greater than 0(mass gap)

The P vs. NP Problem: In computer programming, P problems are anything can be solved by a turing machine(computer) in a "polynomial"(ie: ax^2+bx+c) amount of computation time (considered to be easily solvable, though not all P problems have a known solution etc etc). NP problems are problems, that while not solvable in a polynomial amount of time, a solution is able to be verified in a polynomial amount of time. The definitions of these are a bit fuzzy, and P vs NP asks if there's a fundamental difference between the two at all.

Navier–Stokes equations: These equations predict the movement of viscous newtonian fluids (almost all fluids you can think of that dont have debate over "is it a solid or a liquid" so not like, jello, jam, ketchup, shaving cream whatever). This asks if for any fluid in three dimensions, a "smooth"(differentiable at all points) solution to the equations can always be found

The Poincaré conjecture: In topology, the conjecture states that for any finite 3d topological space, where every loop in the space can be tightened to a point, it's necessarily topologically identical to a 4 dimensional sphere (A normal sphere is a 3d sphere, however it's a 2 dimensional topological space, as loops can only be placed on the "flat" surface, see photo below)

The Birch and Swinnerton-Dyer conjecture: Elliptic curves(see photo below) are defined by y^2=x^3+ax+b. Some of them have an infinite amount of rational solutions, and some only have a finite amount, and this conjecture states that there is an associated function, that when it equals 0, there's infinite rational solutions, and when it is not 0, it's finite.

The Hodge Conjecture: "The answer to this conjecture determines how much of the topology of the solution set of a system of algebraic equations can be defined in terms of further algebraic equations." (im gonna be honest i have no idea, i lifted this definition straight from the clay mathematics institute)

The pointcaré conjecture has actually been solved, it's the only one on the list that has, but dont let that influence your decision

[askagamedev]

When fighting AI controlled enemies, every now in then it happens that said enemies just stop acting. They don't react to anything, they don't act, they just stand there and take hits from me. What's the technological background of this kind of bug?

Most of the time, game AI enemies follow some kind of preset decision-making process. It's usually either a series of conditions that get evaluated until some decision is made, or it's a [finite state machine] that transitions from state to state based on timing and conditions being met. Regardless, decisions are made whenever the proper conditions are met.

Sometimes (usually through uncaught errors on the part of designers like me), the AI gets into a bad state where there's no valid actions to take. In the case of a finite state machine, this could be an orphan state with no exit condition, or a series of states that infinitely loop over and over. In the case of a condition series, it could be an edge case of some weird condition being met that has the AI brain bail out before it reaches any valid action to take. Decision-making that requires certain timing, for example, can also result in this kind of state if the timing conditions for the next step doesn't get met. Regardless, AI that don't do anything are usually stuck in a bad situation that they have no valid means of handling.

Most of the time, we build in some form of debugging visualization to help us figure out what step of the AI decision-making process each AI is currently up to. This way we can catch the various errors that crop up for unhandled edge cases. Unfortunately, the more obscure edge cases can sneak through our testing and make it out to the live game.

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