prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: Is there any evidence of heaven and hell?

Read the following text and answer briefly.

Context 1: LOL Anonymous I'm here! & I did answer you back there 
kl el kotb el qadema (tawrat + enjel + 9o7f ibrahem) 7orfat w t'3ayarat   law ma 9ar hal shy chan ma 6ala3 ktab yded ye;3y elly gablah l7ad ma 6ala3 lna el quraan b norah elly allah 7f'6ah don ta7ref w tabdel ela yom el dein    shlon tyeeb Joan shy ma5ooth 5erah?!
1- Imagine you have two individuals  One truly believes in God but he is a bad husband and father who cheats on his wife and neglects his children  The other man is a nonbeliever; he accepts religion in theory and doctrine but simply does not have faith no matter how hard he tried  He is a good father and a good husband as well  When they stand before God at the Day of Judgment, would the nonbeliever be condemned to eternal hell only for that one flaw in him? Don't the other good traits in that person matter at all? Does being a nonbeliever equal being evil?
2-God knows everything  However, why would He have created when He knows exactly what each person is going to do and where they will end up? For example, let us suppose that tomorrow I will murder someone  God knows that I will do that  And He knows that I will suffer in hell when the time comes  So why does He purposely inflict that pain on me? To teach me a lesson? He knows the outcome of everything and everyone He has created 
3-Since you brought up science in the previous comments section, I am seriously curious (I am not trying to be glib) about dinosaurs and prehistoric humans  We have seen the evolution of humans and we have fossils and hard evidence of it  The science world has traced back "Eve" to Africa  The concept of Adam and Eve seems too mythological to be 100% factual  It has the same tone of Greek and Ancient Egyptian legends 
-Note: I am not trying to corner you here, I just want to know your response to my queries and thoughts  Thanks 
_ ولماذا نصلى ولمن نصلى    انى لا ارى لصلاتكم هذه اى حكمه ولماذا كل تلك الحركات اما كان يكفى الخشوع  
اعتقد ان المشكلة ليست كما ذكرت  فما ذكرته يعني ان هناك العديد من المسائل الفقهية المتصلة بالعصر الجديد دون حل  على العكس, فالحلال بين والحرام بين  اعتقد المشكلة تكمن بالمسلمين الذين اصابهم الوهن 
You'll be answerd personaly only if; you comment with your blogger's name/nick 
I answered you before & there was no response from your behalf  Why would you think you're worth an answer from me since you can seek answers from good books?
Your questions are very simple, with clean cut answers  Don't use the word corner; it indicates things you don't have the first alphabet for!
Religion was established to calm our fears from the unknown  Freud did write about it in the father figure argument  Try to read it 
I deleted my last comment because in the end of it I inserted some thing that may offend some people (if you read it, I gauss you know what I mean)  Abraham did not believe in God until he saw the birds come to life  So, all what I want is a single prove similar to Abraham 
I indeed busy doing my homework; I have to solve the energy equation and the continuity equation to come with another, hopefully, exact solution to the problem 
Do you work? I mean do you have a job, it seems that you are always on line!!!???? It is amazing how some one would stick to the internet  Try to go out more often, play sport or find any other hobby  If you could not disattach yourself from the internet try and seek a professional help… :-)  Honestly, I’m not making fun of you it is just an advice… sincere advice 
I measure prosperity of a nation by the degree of its civilization in comparison to others of its time  Islamic civilization was at the peak during the Umayyad and Abbasid reigns, at the time when Moslems conquered other nations and dying civilizations, and mixed with them, and the new comers in Islam started researching for the truth  In their quest they translated Greek philosophy books and dug into ancient ones  Their search for truth led them to the emergence of chemistry, Algebra and other sciences And their findings excelled them in innovative inventions  The maps and astrolabes that Columbus used to sail on his quest to discover the West years later were taken from the Arabs  And in contrary to the scribbles of their holy book, Arabic books were the first that brought the roundedness of earth to world attention  Arabic language romanticized the rigid Latin, poetry and verse advanced  At one time, civilization was dubbed as Arabization in Andalusia and that was evident in Andalusia’s Christian and Jewish ancient books  Islamic Architecture and their aggregation systems portrayed marvels of their time  Islamic libraries were huge and rich for the European knowledge seekers 
And history tells us that those were the times when Moslems were the furthest from the dogma of their religion, there societies were open to controlled prostitution in the name of dancimng Jawari, and alcohol was openly consumed  We also notice in these periods that struggle for power among them mounted, yet they were the most tolerant to other nations and beliefs  There was nothing Islamic about them at that time, only a name  After that time, the Islamic civilization collapsed and kept deteriorating as people clung more to the their religious beliefs that were diverted through different interpretations 
And based on that; I believe that it’s not only Moslems who missread their religion, the problem stemmed from the core of the religion (Quran and tradition) for being vague and subject to different interpretations  If Quran was the true word of God, then it should have contained the miracle of not being subject to different interpretations, at least to keep Moslems united, and then, one would believe that Islam is suitable for every location and time 
Nothing can give a solid proof of the existence of heaven and hell, yet, nothing can disprove it either  Same goes to God  So the chance remain fifty-fifty  You either believe in it for the sake of taking a lesser risk if you don’t believe in blind faith, or discard it all together  Yet, the possibility is there until proved or disproved  As for now; these cases can only be understood through logic  So let’s reiterate your question and narrow it down to make it closer to something that can be measured 
Is there any mention of the stories of prophets outside the holy books? Are there any archeological evidences of each prophet’s reign (and I stress prophet’s)? Are there any mention of each prophet in history books outside its holy book, or the books that were based on it’s holy books? Ok, that was too general, let’s narrow it down a bit since your knowledge of your religion, mashallah, especially in reciting Quran is superb 
Is there any mention of the prophet Mohammad reign in non-Moslem history books other than those referenced to Moslem books? And if there were; did those stories mach? Did archeology of Mohammad’s time match those stories? Did ancient books of other nations confirm those stories? After all Mecca was an open, mid trade center for traders of the South and the North, and supposedly was exposed and open to other nations, and an emergence of a new religion would not have gone unnoticed in the ancient books of those nations 
That's why faith is called "FAITH" to believe in the unseen, you see 
keep in mind that every major religion has gone through various phases from emergence to growth to stagnation to depression and reformation  these are cycles that play out across centuries and i suspect islam is not immune to them 
islam is unique in that it is truly the last 'great' religion and it is here to stay  the future should be one of consilience and consolidation between religions and peoples 
at the end of the day - tolerance of thought will be the salvation of mankind 
I've been asking your first question for as long as I can remember and nobody has given me a convincing answer yet  I also asked the second question verbatim when I was in my high school's Tarbiya Islamiya class, and the teacher had no answer  I kept hounding him until the students yelled at me to shut up and then he kicked me out!
We're all adults here, so why can't you answer Anon's query? I still find it amazing that you named yourself after Saint Joan  A Christian (gasp!) who will burn in hell according to some of the comments here 
All men are my brothers  I would have liked to have said it then, and I would like to say it now: all men are my brothers  But all men are not my brothers  Why? Because all women are my sisters  And the brother who denies the rights of his sister: that brother is not my brother  At the very best, he is my half-brother - by definition  Osama is not my brother 
Religion is sensitive ground, as well it might be  Here we walk on eggshells  Because religion is itself an eggshell  Today, in the West, there are no good excuses for religious belief - unless we think that ignorance, reaction and sentimentality are good excuses  This is of course not so in the East, where, we acknowledge, almost every living citizen in many huge and populous countries is intimately defined by religious belief  The excuses, here, are very persuasive; and we duly accept that 'faith' - recently and almost endearingly defined as 'the desire for the approval of supernatural beings' - is a world-historical force and a world-historical actor  All religions, unsurprisingly, have their terrorists, Christian, Jewish, Hindu, even Buddhist  But we are not hearing from those religions  We are hearing from Islam 
Let us make the position clear  We can begin by saying, not only that we respect Muhammad, but that no serious person could fail to respect Muhammad - a unique and luminous historical being  Judged by the continuities he was able to set in motion, he remains a titanic figure, and, for Muslims, all-answering: a revolutionary, a warrior, and a sovereign, a Christ and a Caesar, 'with a Koran in one hand', as Bagehot imagined him, 'and a sword in the other'  Muhammad has strong claims to being the most extraordinary man who ever lived  And always a man, as he always maintained, and not a god  Naturally we respect Muhammad  But we do not respect Muhammad Atta 
Until recently it was being said that what we are confronted with, here, is 'a civil war' within Islam  That's what all this was supposed to be: not a clash of civilisations or anything like that, but a civil war within Islam  Well, the civil war appears to be over  And Islamism won it  The loser, moderate Islam, is always deceptively well-represented on the level of the op-ed page and the public debate; elsewhere, it is supine and inaudible  We are not hearing from moderate Islam  Whereas Islamism, as a mover and shaper of world events, is pretty well all there is 
So, to repeat, we respect Islam - the donor of countless benefits to mankind, and the possessor of a thrilling history  But Islamism? No, we can hardly be asked to respect a creedal wave that calls for our own elimination  More, we regard the Great Leap Backwards as a tragic development in Islam's story, and now in ours  Naturally we respect Islam  But we do not respect Islamism, just as we respect Muhammad and do not respect Muhammad Atta 
المؤامرات على الأديان وجميع الانقلابات المخربة والثورات على القيم والمبادئ خرجت من هذا التراث    وان كل معول هدم كان وراءه توجيه يهودي 
•تذكروا أن الشعب الذي لا يهلك غيره يهلك نفسه 
•يجب ان نخلق الجيل الذي لا يخجل من كشف عورته (ألا تفسر لنا هذه الجملة موجة العرى في الافلام والموضات التى تسود العالم الآن) 
 علينا ان نشعل حربا بين الشعوب ونضرب الدول بعضها ببعض فبهذا يصبح جميع المتحاربين في حاجة الى أموالنا فنفرض عليهم شروطنا 
•الجماهير عمياء فاشتروها بالمال وسوقوها كالبهائم الى أهدافكم 
•سيطروا على الانتخابات ووسائل الاعلام والصحافة (وهم قد سيطروا عليها بالمال والجنس والمرأة في الغرب الرأسمالي وبالحزب والسلطة في العالم الاشتراكي) 
•ادفعوا الجماهير العمياء الى الثورة وسلموهم مقاليد الحكم ليحكموا في غوغائية وغباء (وقد فعلوا هذه في الثورة الفرنسية) وحينئذ نأتي نحن ونعدمهم فنكون منقذين للعالم (وقد اعدموهم جميعاً من روبسبير الى ميرابوا) 
•ارفعوا شعار الحرية واهدموا بها الاخلاق والاسرة والقومية والوطنية 
 ارفعوا شعار العلم واهدموا به الدين    وهذا ما فعله كمال أتاتورك (حفيد مزاراحي) حينما اقام الدولة العلمانية في تركيا ووقف يخطب في البرلمان التركي عام 1923 ساخراً من القرآن 
نحن الآن في القرن العشرين لا نستطيع ان نسير وراء كتاب تشريع يبحث عن التين والزيتون 
•الذي يعرقل مؤامراتكم اوقعوه في فضائح ثم هددوه بكشفها (وقد فعلوها في ووترجيت) او في مآزق مالية ثم تقدموا لانقاذه (وقد فعلها دزرائيلي مع الخديو واستولى على القنال)   وإذ تعذر الامر سارعوا الى اغتياله (وقد فعلوها بكنيدي) ثم اقتلوا قاتله لتدفنوا اسرارنا معه الى الأبد (وقد فعلوها بقاتل كنيدي) 
•اقتلوا القوميات والوطنيات بالدعوة الى الاممية والمواطنة العالمية وقد فعلها ماركس في الشيوعية 
•كل ما عدا اليهود حيوانات ناطقة سخرها الله في خدمة اليهود 
واليهودية ترى ان الله واحد ولكنها تحتكره لنفسها فلا عمل لله الا الحفاظ على اسرائيل وتسخير جميع الشعوب لخدمتها 
واللاهوت اليهودي لا يؤمن بآخرة، وقد شطبوا كل ما جاء عن الآخرة في التوراة    والقيامة عندهم هي قيامة دولتهم في فلسطين والبعث بعثها والنشر نشرها    ويوم الحساب هو اليوم الذي يحاسبون فيه كل الأمم يوم يعود المسيح ويباركهم ويختارهم نواباً له في حكم العالم وإقامة ملكوت الله على الأرض    والعجيب انهم كفروا بالمسيح حينما جاء ثم أعلنوا إيمانهم بعودته وشرطوا هذه العودة بانها رجعة من المسيح ليختارهم رؤساء وحكاماً للعالم الى الأبد 
والفكر اليهودي يلقى غلالة من الأسرار والطلاسم والكتمان والغموض على كل شئ    والكبالا والسحر وعلم الأعداد والحروف وتسخير الشياطين من علومهم التى شغفوا بها وروجوها ونشروها 
وكانت وسيلتهم الى هدم الكتب السماوية هى تفسيرها بالتأويل وذلك برفض المعاني الظاهرة واختراع معان باطنة تهدم الغرض الديني وتفسد هدفه 
ونستطيع ان نرى اثر التوجيه الهودي في الفلسفات العبثية والدمية والمادية والفوضية والإباحية    واحيانا نلمح اسماء يهودية خلفها مثل : سارتر – فرويد – ماركس – ماركوز 
واذا فتحنا ملف الديانة البهائية فإننا نجد اثر التوجيه اليهودي واضحا في كتبها 
عبد الهاء تأليف سليم قبعين القاهرة مطبعة العمران 1922 
مفواضات عبد البهاء الطبعة الاولى 1928م  موعود كل الامم 
جورج تاوزنه مطبوع بإذن من المحفل الروحاني لمصر والسودان 
•اكثر فلاسفة اليونان تعلموا الحكمة من بنى اسرائيل 
•رسالة عبد البهاء هي توحيد المسلمين والنصارى واليهود وجمعهم على أصل نواميس موسى 
•عمل موسى لا يس



ground_truth: Unknown.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: What factors control the reliance of artificial organisms on plasticity?

Read the following text and answer briefly.

Context 2: Paper Info

Title: Environmental variability and network structure determine the optimal plasticity mechanisms in embodied agents
Publish Date: Unkown
Author List: Sina Khajehabdollahi (from Department of Computer Science, University of Tübingen)

Figure

Figure2: An outline of the network controlling the foraging agent The sensor layer receives inputs at each time step (the ingredients of the nearest food), which are processed by the plastic layer in the same way as the static sensory network, Fig 1 The output of that network is given as input to the motor network, along with the distance d and angle α to the nearest food, the current velocity v, and energy E of the agent These signals are processed through two hidden layers to the final output of motor commands as the linear and angular acceleration of the agent
Figure4: The evolved parameters θ = (θ 1 ,      , θ 8 ) of the plasticity rule for the reward prediction (a ) and the decision (b ) tasks, for a variety of parameters (p tr = 0 01, d e ∈ 0, 0 1,      , 1, and σ ∈ 0, 0 1,      , 1 in all 100 combinations) Despite the relatively small difference between the tasks, the evolved learning rules differ considerably For visual guidance, the lines connect θs from the same run 
Figure5: a The trajectory of an agent (blue line) in the 2D environment A well-trained agent will approach and consume food with positive values (green dots) and avoid negative food (red dots) b The learning rate of the plastic sensory network eta p grows with the distance between environments d e c  and decreases with the frequency of environmental change d The fitness of an agent (measured as the total food consumed over its lifetime) increases over generations of the EA for both the scalar and binary readouts in the sensory network e The Pearson correlation coefficient of an evolved agent's weights with the ingredient value vector of the current environment (E 1 -blue, E 2 -red) In this example, the agent's weights are anti-correlated with its environment, which is not an issue for performance since the motor network can interpret the inverted signs of food 

abstract

The evolutionary balance between innate and learned behaviors is highly intricate, and different organisms have found different solutions to this problem  We hypothesize that the emergence and exact form of learning behaviors is naturally connected with the statistics of environmental fluctuations and tasks an organism needs to solve 
Here, we study how different aspects of simulated environments shape an evolved synaptic plasticity rule in static and moving artificial agents  We demonstrate that environmental fluctuation and uncertainty control the reliance of artificial organisms on plasticity  Interestingly, the form of the emerging plasticity rule is additionally determined by the details of the task the artificial organisms are aiming to solve 
Moreover, we show that coevolution between static connectivity and interacting plasticity mechanisms in distinct sub-networks changes the function and form of the emerging plasticity rules in embodied agents performing a foraging task  One of the defining features of living organisms is their ability to adapt to their environment and incorporate new information to modify their behavior 
It is unclear how the ability to learn first evolved , but its utility appears evident  Natural environments are too complex for all the necessary information to be hardcoded genetically and more importantly, they keep changing during an organism's lifetime in ways that cannot be anticipated ;   The link between learning and environmental uncertainty and fluctuation has been extensively demonstrated in both natural ; , and artificial environments  
Nevertheless, the ability to learn does not come without costs  For the capacity to learn to be beneficial in evolutionary terms, a costly nurturing period is often required, a phenomenon observed in both biological , and artificial organisms   Additionally, it has been shown that in some complex environments, hardcoded behaviors may be superior to learned ones given limits in the agent's lifetime and envi-ronmental uncertainty ; ;  
The theoretical investigation of the optimal balance between learned and innate behaviors in natural and artificial systems goes back several decades  However, it has recently found also a wide range of applications in applied AI systems ;   Most AI systems are trained for specific tasks, and have no need for modification after their training has been completed 
Still, technological advances and the necessity to solve broad families of tasks make discussions about life-like AI systems relevant to a wide range of potential application areas  Thus the idea of open-ended AI agents that can continually interact with and adapt to changing environments has become particularly appealing 
Many different approaches for introducing lifelong learning in artificial agents have been proposed  Some of them draw direct inspiration from actual biological systems ;   Among them, the most biologically plausible solution is to equip artificial neural networks with some local neural plasticity , similar to the large variety of synaptic plasticity mechanisms ; ; that performs the bulk of the learning in the brains of living organisms  
The artificial plasticity mechanisms can be optimized to modify the connectivity of the artificial neural networks toward solving a particular task  The optimization can use a variety of approaches, most commonly evolutionary computation  The idea of meta-learning or optimizing synaptic plasticity rules to perform specific functions has been recently established as an engineering tool that can compete with stateof-the-art machine learning algorithms on various complex tasks ; ; Pedersen and Risi (2021);  
Additionally, it can be used to reverse engineer actual plasticity mechanisms found in biological neural networks and uncover their functions ;   Here, we study the effect that different factors (environ-arXiv:2303 06734v1 [q-bio NC] 12 Mar 2023 mental fluctuation and reliability, task complexity) have on the form of evolved functional reward-modulated plasticity rules 
We investigate the evolution of plasticity rules in static, single-layer simple networks  Then we increase the complexity by switching to moving agents performing a complex foraging task  In both cases, we study the impact of different environmental parameters on the form of the evolved plasticity mechanisms and the interaction of learned and static network connectivity 
Interestingly, we find that different environmental conditions and different combinations of static and plastic connectivity have a very large impact on the resulting plasticity rules  We imagine an agent who must forage to survive in an environment presenting various types of complex food particles  Each food particle is composed of various amounts and combinations of N ingredients that can have positive (food) or negative (poison) values 
The value of a food particle is a weighted sum of its ingredients  To predict the reward value of a given resource, the agent must learn the values of these ingredients by interacting with the environment  The priors could be generated by genetic memory, but the exact values are subject to change  To introduce environmental variability, we stochastically change the values of the ingredients 
More precisely, we define two ingredient-value distributions E 1 and E 2 and switch between them, with probability p tr for every time step  We control how (dis)similar the environments are by parametrically setting E 2 = (1 − 2d e )E 1 , with d e ∈ [0, 1] serving as a distance proxy for the environments; when d e = 0, the environment remains unchanged, and when d e = 1 the value of each ingredient fully reverses when the environmental transition happens 
For simplicity, we take values of the ingredients in E 1 equally spaced between -1 and 1 (for the visualization, see Fig  )  The static agent receives passively presented food as a vector of ingredients and can assess its compound value using the linear summation of its sensors with the (learned or evolved) weights, see Fig   
The network consists of N sensory neurons that are projecting to a single post-synaptic neuron  At each time step, an input X t = (x 1 ,       , x N ) is presented, were the value x i , i ∈ {1,       , N } represents the quantity of the ingredient i  We draw x i independently form a uniform distribution on the [0, 1] interval (x i ∼ U (0, 1)) 
The value of each ingredient w c i is determined by the environment (E 1 or E 2 )  The postsynaptic neuron outputs a prediction of the food X t value as y t = g(W X T t )  Throughout the paper, g will be either the identity function, in which case the prediction neuron is linear, or a step-function; however, it could be any other nonlinearity, such as a sigmoid or ReLU 
After outputting the prediction, the neuron receives feedback in the form of the real value of the input R t   The real value is computed as R t = W c X T t + ξ, where W c = (w c 1 ,       , w c N ) is the actual value of the ingredients, and ξ is a term summarizing the noise of reward and sensing system ξ ∼ N (0, σ) 
Figure : An outline of the static agent's network  The sensor layer receives inputs representing the quantity of each ingredient of a given food at each time step  The agent computes the prediction of the food's value y t and is then given the true value R t ; it finally uses this information in the plasticity rule to update the weight matrix 
For the evolutionary adjustment of the agent's parameters, the loss of the static agent is the sum of the mean squared errors (MSE) between its prediction y t and the reward R t over the lifetime of the agent  The agent's initial weights are set to the average of the two ingredient value distributions, which is the optimal initial value for the case of symmetric switching of environments that we consider here 
As a next step, we incorporate the sensory network of static agents into embodied agents that can move around in an environment scattered with food  To this end, we merge the static agent's network with a second, non-plastic motor network that is responsible for controlling the motion of the agent in the environment 
Specifically, the original plastic network now provides the agent with information about the value of the nearest food  The embodied agent has additional sensors for the distance from the nearest food, the angle between the current velocity and the nearest food direction, its own velocity, and its own energy level (sum of consumed food values) 
These inputs are processed by two hidden layers (of 30 and 15 neurons) with tanh activation  The network's outputs are angular and linear acceleration, Fig    The embodied agents spawn in a 2D space with periodic boundary conditions along with a number of food particles that are selected such that the mean of the food value distribution is ∼ 0  An agent can eat food by approaching it sufficiently closely, and each time a food particle is eaten, it is The sensor layer receives inputs at each time step (the ingredients of the nearest food), which are processed by the plastic layer in the same way as the static sensory network, Fig   
The output of that network is given as input to the motor network, along with the distance d and angle α to the nearest food, the current velocity v, and energy E of the agent  These signals are processed through two hidden layers to the final output of motor commands as the linear and angular acceleration of the agent re-spawned with the same value somewhere randomly on the grid (following the setup of ) 
After 5000 time steps, the cumulative reward of the agent (the sum of the values of all the food it consumed) is taken as its fitness  During the evolutionary optimization, the parameters for both the motor network (connections) and plastic network (learning rule parameters) are co-evolved, and so agents must simultaneously learn to move and discriminate good/bad food 
Reward-modulated plasticity is one of the most promising explanations for biological credit assignment   In our network, the plasticity rule that updates the weights of the linear sensor network is a rewardmodulated rule which is parameterized as a linear combination of the input, the output, and the reward at each time step:
Additionally, after each plasticity step, the weights are normalized by mean subtraction, an important step for the stabilization of Hebbian-like plasticity rules   We use a genetic algorithm to optimize the learning rate η p and amplitudes of different terms θ = (θ 1 ,       , θ 8 )  The successful plasticity rule after many food presentations must converge to a weight vector that predicts the correct food values (or allows the agent to correctly decide whether to eat a food or avoid it) 
To have comparable results, we divide θ = (θ 1 ,       , θ 8 ) by We then multiply the learning rate η p with θ max to maintain the rule's evolved form unchanged, η norm p = η p • θ max   In the following, we always use normalized η p and θ, omitting norm   To evolve the plasticity rule and the moving agents' motor networks, we use a simple genetic algorithm with elitism  
The agents' parameters are initialized at random (drawn from a Gaussian distribution), then the sensory network is trained by the plasticity rule and finally, the agents are evaluated  After each generation, the bestperforming agents (top 10 % of the population size) are selected and copied into the next generation 
The remaining 90 % of the generation is repopulated with mutated copies of the best-performing agents  We mutate agents by adding independent Gaussian noise (σ = 0 1) to its parameters  To start with, we consider a static agent whose goal is to identify the value of presented food correctly  The static reward-prediction network quickly evolves the parameters of the learning rule, successfully solving the prediction task 
We first look at the evolved learning rate η p , which determines how fast (if at all) the network's weight vector is updated during the lifetime of the agents  We identify three factors that control the learning rate parameter the EA converges to: the distance between the environments, the noisiness of the reward, and the rate of environmental transition 
The first natural factor is the distance d e between the two environments, with a larger distance requiring a higher learning rate, Fig    This is an expected result since the convergence time to the "correct" weights is highly dependent on the initial conditions  If an agent is born at a point very close to optimality, which naturally happens if the environments are similar, the distance it needs to traverse on the fitness landscape is small 
Therefore it can afford to have a small learning rate, which leads to a more stable convergence and is not affected by noise  A second parameter that impacts the learning rate is the variance of the rewards  The reward an agent receives for the plasticity step contains a noise term ξ that is drawn from a zero mean Gaussian distribution with standard deviation σ 
This parameter controls the unreliability of the agent's sensory system, i e , higher σ means that the information the agent gets about the value of the foods it consumes cannot be fully trusted to reflect the actual value of the foods  As σ increases, the learning rate η p decreases, which means that the more unreliable an environment becomes, the less an agent relies on plasticity to update its weights, Fig   
Indeed for some combinations of relatively small distance d e and high reward variance σ, the EA converges to a learning rate of η p ≈ 0  This means that the agent opts to have no adaptation during its lifetime and remain at the mean of the two environments  It is an optimal solution when the expected loss due to ignoring the environmental transitions is, on average, lower than the loss the plastic network will incur by learning via the (often misleading because of the high σ) environmental cues 
A final factor that affects the learning rate the EA will converge to is the frequency of environmental change during an agent's lifetime  Since the environmental change is modeled as a simple, two-state Markov process (Fig  ), the control parameter is the transition probability p tr   When keeping everything else the same, the learning rate rapidly rises as we increase the transition probability from 0, and after reaching a peak, it begins to decline slowly, eventually reaching zero (Fig  ) 
This means that when environmental transition is very rare, agents opt for a very low learning rate, allowing a slow and stable convergence to an environment-appropriate weight vector that leads to very low losses while the agent remains in that environment  As the rate of environmental transition increases, faster learning is required to speed up convergence in order to exploit the (comparatively shorter) stays in each environment 
Finally, as the environmental transition becomes too fast, the agents opt for slower or even no learning, which keeps them ) and the decision (b ) tasks, for a variety of parameters (p tr = 0 01, d e ∈ 0, 0 1,       , 1, and σ ∈ 0, 0 1,       , 1 in all 100 combinations)  Despite the relatively small difference between the tasks, the evolved learning rules differ considerably 
For visual guidance, the lines connect θs from the same run  near the middle of the two environments, ensuring that the average loss of the two environments is minimal (Fig  )  The form of the evolved learning rule depends on the task: Decision vs  Prediction The plasticity parameters θ = (θ 1 ,       , θ 8 ) for the rewardprediction task converge on approximately the same point, regardless of the environmental parameters (Fig  ) 
In particular, θ 3 → 1, θ 5 → −1, θ i → 0 for all other i, and thus the learning rule converges to: Since by definition y t = g(W t X T t ) = W t X T t (g(x) = x in this experiment) and R t = W c X T t + ξ we get: Thus the distribution of ∆W t converges to a distribution with mean 0 and variance depending on η p and σ and W converges to W c  
So this learning rule will match the agent's weight vector with the vector of ingredient values in the environment  We examine the robustness of the learning rule the EA discovers by considering a slight modification of our task  Instead of predicting the expected food value, the agent now needs to decide whether to eat the presented food or not 
This is done by introducing a step-function nonlinearity (g(x) = 1 if x ≥ 1 and 0 otherwise)  Then the output y(t) is computed as: Instead of the MSE loss between prediction and actual value, the fitness of the agent is now defined as the sum of the food values it chose to consume (by giving y t = 1)  Besides these two changes, the setup of the experiments remains exactly the same 
The qualitative relation between η p and parameters of environment d e , σ and p tr is preserved in the changed experiment  However, the resulting learning rule is significantly different (Fig  )  The evolution converges to the following learning rule: In both cases, the rule has the form ∆W t = η p X t [α y R t + β y ] 
Thus, the ∆W t is positive or negative depending on whether the reward R t is above or below a threshold (γ = −β y /α y ) that depends on the output decision of the network (y t = 0 or 1)  Both learning rules (for the reward-prediction and decision tasks) have a clear Hebbian form (coordination of preand post-synaptic activity) and use the incoming reward signal as a threshold 
These similarities indicate some common organizing principles of reward-modulated learning rules, but their significant differences highlight the sensitivity of the optimization process to task details  We now turn to the moving embodied agents in the 2D environment  To optimize these agents, both the motor network's connections and the sensory network's plasticity parameters evolve simultaneously 
Since the motor network is initially random and the agent has to move to find food, the number of interactions an agent experiences in its lifetime can be small, slowing down the learning  However, having the larger motor network also has benefits for evolution because it allows the output of the plastic network to be read out and transformed in different ways, resulting in a broad set of solutions 
The fitness of an agent (measured as the total food consumed over its lifetime) increases over generations of the EA for both the scalar and binary readouts in the sensory network  e  The Pearson correlation coefficient of an evolved agent's weights with the ingredient value vector of the current environment (E 1 -blue, E 2 -red) 
In this example, the agent's weights are anti-correlated with its environment, which is not an issue for performance since the motor network can interpret the inverted signs of food  The agents can solve the task effectively by evolving a functional motor network and a plasticity rule that converges to interpretable weights (Fig  ) 
After ∼ 100 evolutionary steps (Fig  ), the agents can learn the ingredient value distribution using the plastic network and reliably move towards foods with positive values while avoiding the ones with negative values  We compare the dependence of the moving and the static agents on the parameters of the environment: d e and the state transition probability p tr  
At first, in order to simplify the experiment, we set the transition probability to 0, but fixed the initial weights to be the average of E 1 and E 2 , while the real state is E 2   In this experiment, the distance between states d e indicates twice the distance between the agent's initial weights and the optimal weights (the environment's ingredient values) since the agent is initialized at the mean of the two environment distributions 
Same as for the static agent, the learning rate increases with the distance d e (Fig  )  Then, we examine the effect of the environmental transition probability p tr on the evolved learning rate η p   In order for an agent to get sufficient exposure to each environment, we scale down the probability p tr from the equivalent experiment for the static agents 
We find that as the probability of transition increases, the evolved learning rate η p decreases (Fig  )  This fits with the larger trend for the static agent, although there is a clear difference when it comes to the increase for very small transition probabil-ities that were clearly identifiable in the static but not the moving agents 
This could be due to much sparser data and possibly the insufficiently long lifetime of the moving agent (the necessity of scaling makes direct comparisons difficult)  Nevertheless, overall we see that the associations observed in the static agents between environmental distance d e and transition probability p tr and the evolved learning rate η p are largely maintained in the moving agents 
Still, more data would be needed to make any conclusive assertions about the exact effect of these environmental parameters on the emerging plasticity mechanisms  A crucial difference between the static and the moving agents is the function the plasticity has to perform  While in the static agents, the plasticity has to effectively identify the exact value distribution of the environment in order to produce accurate predictions, in the embodied agents, the plasticity has to merely produce a representation of the environment that the motor network can evolve to interpret adequately enough to make decisions about which food to consume 
To illustrate the difference, we plot the Pearson correlation coefficient between an agent's weights and the ingredient values of the environment it is moving in (Fig  )  We use the correlation instead of the MSE loss (which we used for the static agents in Fig  ) because the amplitude of the vector varies a lot for different agents and meaningful The evolved parameters of moving agents' plasticity rule for the g(s) = x, identity (a ) and the step function (Eq 
4) (b ) sensory networks (the environmental parameters here are d e ∈ [0, 1], σ = 0 and p tr = 0 001)  The step function (binary output) network evolved a more structured plasticity rule (e g , θ 3 > 0 for all realizations) than the linear network  Moreover, the learned weights for the identity network (c ) have higher variance and correlate significantly less with the environment's ingredient distribution compared to the learned weights for the thresholded network (d )
conclusions cannot be drawn from the MSE loss  For many agents, the learned weights are consistently anti-correlated with the actual ingredient values (an example of such an agent is shown in Fig  )  This means that the output of the sensory network will have the opposite sign from the actual food value 
While in the static network, this would lead to very bad predictions and high loss, in the foraging task, these agents perform exactly as well as the ones where the weights and ingredients values are positively correlated, since the motor network can simply learn to move towards food for which it gets a negative instead of a positive sensory input 
This additional step of the output of the plastic network going through the motor network before producing any behavior has a strong effect on the plasticity rules that the embodied agents evolve  Specifically, if we look at the emerging rules the top performing agents have evolved (Fig  ), it becomes clear that, unlike the very well-structured rules of the static agents (Fig  ), there is now virtually no discernible pattern or structure 
The difference becomes even clearer if we look at the learned weights (at the end of a simulation) of the best-performing agents (Fig  )  While there is some correlation with the environment's ingredient value distribution, the variance is very large, and they do not seem to converge on the "correct" values in any way 
This is to some extent expected since, unlike the static agents where the network's output has to be exactly correct, driving the evolution of rules that converge to the precise environmental distribution, in the embodied networks, the bulk of the processing is done by the motor network which can evolve to interpret the scalar value of the sensory network's output in a variety of ways 
Thus, as long as the sensory network's plasticity rule co-evolves with the motor network, any plasticity rule that learns to produce consistent information about the value of encountered food can potentially be selected  To further test this assumption, we introduce a bottleneck of information propagation between the sensory and motor networks by using a step-function nonlinearity on the output of the sensory network (Eq 
4)  Similarly to the decision task of the static network, the output of the sensory network now becomes binary  This effectively reduces the flow of information from the sensory to the motor network, forcing the sensory network to consistently decide whether food should be consumed (with the caveat that the motor network can still interpret the binary sign in either of two ways, either consuming food marked with 1 or the ones marked with 0 by the sensory network) 
The agents perform equally well in this variation of the task as before (Fig  ), but now, the evolved plasticity rules seem to be more structured (Fig  )  Moreover, the variance of the learned weights in the bestperforming agents is significantly reduced (Fig  ), which indicates that the bottleneck in the sensory network is in-creasing selection pressure for rules that learn the environment's food distribution accurately 
We find that different sources of variability have a strong impact on the extent to which evolving agents will develop neuronal plasticity mechanisms for adapting to their environment  A diverse environment, a reliable sensory system, and a rate of environmental change that is neither too large nor too small are necessary conditions for an agent to be able to effectively adapt via synaptic plasticity 
Additionally, we find that minor variations of the task an agent has to solve or the parametrization of the network can give rise to significantly different plasticity rules  Our results partially extend to embodied artificial agents performing a foraging task  We show that environmental variability also pushes the development of plasticity in such agents 
Still, in contrast to the static agents, we find that the interaction of a static motor network with a plastic sensory network gives rise to a much greater variety of wellfunctioning learning rules  We propose a potential cause of this degeneracy; as the relatively complex motor network is allowed to read out and process the outputs from the plastic network, any consistent information coming out of these outputs can be potentially interpreted in a behaviorally useful way 
Reducing the information the motor network can extract from the sensory system significantly limits learning rule variability  Our findings on the effect of environmental variability concur with the findings of previous studies that have identified the constraints that environmental variability places on the evolutionary viability of learning behaviors 
We extend these findings in a mechanistic model which uses a biologically plausible learning mechanism (synaptic plasticity)  We show how a simple evolutionary algorithm can optimize the different parameters of a simple reward-modulated plasticity rule for solving simple prediction and decision tasks 
Reward-modulated plasticity has been extensively studied as a plausible mechanism for credit assignment in the brain ; ; and has found several applications in artificial intelligence and robotics tasks ;   Here, we demonstrate how such rules can be very well-tuned to take into account different environmental parameters and produce optimal behavior in simple systems 
Additionally, we demonstrate how the co-evolution of plasticity and static functional connectivity in different subnetworks fundamentally changes the evolutionary pressures on the resulting plasticity rules, allowing for greater diversity in the form of the learning rule and the resulting learned connectivity 
Several studies have demonstrated how, in biological networks, synaptic plasticity heavily interacts with and is driven by network topology   Moreover, it has been recently demonstrated that biological plasticity mechanisms are highly redundant in the sense that any observed neural connectivity or recorded activity can be achieved with a variety of distinct, unrelated learning rules  
This observed redundancy of learning rules in biological settings complements our results and suggests that the function of plasticity rules cannot be studied independently of the connectivity and topology of the networks they are acting on  The optimization of functional plasticity in neural networks is a promising research direction both as a means to understand biological learning processes and as a tool for building more autonomous artificial systems 
Our results suggest that reward-modulated plasticity is highly adaptable to different environments and can be incorporated into larger systems that solve complex tasks  This work studies a simplified toy model of neural network learning in stochastic environments  Future work could be built on this basic framework to examine more complex reward distributions and sources of environmental variability 
Moreover, a greater degree of biological realism could be added by studying more plausible network architectures (multiple plastic layers, recurrent and feedback connections) and more sophisticated plasticity rule parametrizations  Additionally, our foraging simulations were constrained by limited computational resources and were far from exhaustive 
Further experiments can investigate environments with different constraints, food distributions, multiple seasons, more complex motor control systems and interactions of those systems with different sensory networks as well as the inclusion of plasticity on the motor parts of the artificial organisms 



ground_truth: Environmental fluctuation and uncertainty control the reliance of artificial organisms on plasticity.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: How can you level up in the early levels?

Read the following text and answer briefly.

Context 3: What is this game all about? (short version) Do you like the board game RISK®? Then chances are you’ll like QONQR  Your job is to join a faction and help your faction (team) take over the world  QONQR is an artificial intelligence that appeared on the Internet  We don’t know where it came from, or its purpose, but we know it is powerful  The Legion want to destroy QONQR because they believe it will enslave and exterminate humanity  The Swarm believe QONQR will advance the human race, and we should protect it  The Faceless don’t care, and want to steal the technology for their own uses  Pick a side, recruit your friends, and help your faction capture the towns and cities in which you live, work, and play  What is this game all about? (long version) Right now an invisible war is raging all around you  At stake: the Past, Present, and Future  A rogue Artificial Intelligence has been detected infiltrating the world’s networked infrastructure  Initial hacking of the source code has revealed incredible new technology  It is not certain whether this AI seeks to enhance or destroy humanity  It is only certain that it is here, and that is has a name: QONQR Those who first detected the presence of QONQR on the global networks have argued fiercely over its intentions  They have split into viciously rival Factions, each co-opting the known QONQR technologies for their own ends  Even now the Factions battle over the entire globe seeking to gather resources and prepare for the coming power struggle  Whether you accept it or not, the war is here  Your survival, prosperity, and even glory depend on the choices you make and the skill you demonstrate from this point forward  You will be asked to join one of three Factions  The choice should not be made lightly  Your Faction Alignment will define your place in the war over QONQR  THE LEGION unite under the shared goals of destroying QONQR and saving humanity by crushing the nascent AI before it can mature  They are led by AGENT SUNDAY, a former commander of the NSA's Turing Task Force which has been valiantly stamping out dangerous AIs for years  THE SWARM are convinced that QONQR promises an era of unprecedented technological advancement and human prosperity  Nanobot weaponry expert KIMYO NAGUMO leads this faction in the battle to defend QONQR and assemble its future tech, accelerating humanity's path into the future  THE FACELESS are a loosely organized faction of militant hackers who want QONQR's technology for their own ends, but want to prevent the unavoidable nightmare of human slavery they believe it portends  When they choose to communicate, they do so through an anonymous vigilante who goes by the name PROMETHEUS    What do I do first? Create a base, then launch  Launch nanobots until your fingers hurt  How do I create a base? On iOS there is a Base icon in the menu bar  For Windows Phone, you will find a bases button on the home screen  These take you to the Base Screen where you can see how many bases you have available to you  Once you create a base, be sure harvest often by returning to the list of your bases  You can see how full each base is by checking the fill percentage icon  Bases stop collecting once they are full  What is the point of creating bases and harvesting resources? You need bases to earn money  Bases collect rare elements over time which you can then harvest for your faction in exchange for qredits  Qredits can be used to purchase ordinance (like nanomissiles) and upgrades which will help you capture and hold battle zones more easily  What do you mean, “launch nanobots?” Nanobots are the invisible soldiers generated by your device (which has been transformed into “Scope” by advanced QONQR technology)  Nanobots fight for control of the battle zones around you   From the home screen click “Current Zone”  Once you have selected your zone, you will be able to deploy nanobots there   Initially, you are just a level 1 recruit  You are only going to get a small attack formation with a limited range   Other solders have to practice with rifles before they get tanks; you are no different  Once you prove your mettle, you’ll get access to bigger weapons  Soon you’ll be lobbing missiles hundreds of miles  How do I capture a zone? If you play for the Legion and are launching nanobots into a zone controlled by the Swarm, you will capture the zone for the Legion as soon as you have destroyed enough of the enemy that your nonbots outnumber theirs  If you are the person who causes the zone to change control to the Legion, you will be listed as the Capturer of the Zone  The Person with the most nanobots in the zone is the zone leader  What is my current zone? How do you know? Your current zone is determined by your proximity to the nearest zone center  So, while you might be inside the governmental boundaries of a city, your scope (phone) could tell you your current zone is a different city, if that city’s center is closer  So I just keep deploying? Yes, in the early levels of the game, just keep deploying and harvesting your bases  You will earn XP (experience points) proving your loyalty to your Faction 	You will level up quickly and soon have access to many more options  So all I can do is just attack? You only get assault bots to start  They are the most basic type of nanobot formation  As you level up you will get many more options, including bots that are good at defense, energy attacks, long-range deployments, formations that will buff the shields for all your faction-mates in the zone, and many more  My faction already controls this zone should I still attack? Yes, assault bots won’t attack your friends  You will increase the bot counts for that zone, which will deter opponents  Attack bots can defend your zone; they just aren’t very good at it  As you level up you will unlock defensive formations that are better for deploying if your faction already holds the zone  I’ll never knock out my enemy at this rate! In the first few levels your impact might feel minimal, but every deployment helps you gain experience  It won’t take long to level up if you keep at it  If you are unlucky enough to be in a zone where someone has already built up huge defenses, you may be in for a long fight  But remember, your scope moves with you  Go explore the world and find softer targets  Once you level up, it won’t seem like a toy gun against a battleship  You’ll get your big weapons once you prove yourself  We have already seen operatives brag about taking down 1,000,000 nanobots in just a couple days  Nothing is impossible  How do I attack a different zone? As you level up you will unlock more and stronger formations  Those new formations will have range  While at the early levels you can only attack nearby zones, as you level up your attacks will go 10-20 miles (roughly 15-30 Km) and you will eventually gain access to nanomissiles that can go hundreds of miles  What should I buy in the depot first? The smallest thing to buy is a refresh  We give you some of these as you level up so you can try them out  Refreshes will refill (or partially fill depending on the size of your tank) your bot bar or your energy bar  But after that, it depends on your goals  There is much to choose from  Do you want to be able to deploy more nanobots on every launch? Do you want to boost your offensive or defensive bots? Do you want to be able to launch missiles into towns far away? All of these things are possible  Look through the depot and see what you like  Most of the time you will need to buy an upgrade before you can buy the ordinance  For example, missiles are fairly inexpensive, but you need to buy the MX Rack Missile Launcher before you can launch them  Buy the upgrade first  What is the difference between qredits and cubes in the depot? A qredit (aka: credit, which looks like the child of a Q and € ) is the type of currency you earn in the game by harvesting your bases  Cubes (aka: power cubes) are purchased with real money in the Bank section of the Depot  We want everyone to be able to do everything in the game for free, by earning qredits, but for those who want to move a bit faster, you can purchase cubes to speed things along  Purchasing cubes is how QONQR makes money  We very much appreciate your support  Every purchase you make helps us to keep making improvements in the game  Future enhancements will enable you to earn cubes in game  Why can’t I create another base? Additional bases become available as you level up  At the start, you will get a new base every 5 levels  If you don’t have any more bases available to build, you will need to level up  If you have a base available, but aren’t allowed to use it, it is because you already have a base in that zone  You can only have one base in a zone  Get yourself to another zone, then create your base there  My bases are collecting credits at different rates  Your bases collect resources faster if your faction controls the zone  Do your best to either put your bases in zones you can control by yourself, or find zones with strong players in the same faction and put your bases there to maximize your credit collection  The game says I’m in a town that doesn’t exist  QONQR tracks almost 3 million battle zones of varying strategic value in 250 countries  Sometimes those zones include locations that haven’t existed for over 100 years  That’s pretty cool if you ask us  If you find a zone that looks like a duplicate or is just plain wrong, however, let us know on the forums under “Zone Corrections”  How do I move my current position on the map? You might need to take a car, bus, plane or train depending on which zone you are trying to get to, but if you want to move on the map, you need to move in the real world  QONQR is a location-based game, which means you play where you are  However we don't want to make you move to play, we want you to plan when you move  QONQR goes with you as you move through the daily activities in your life  Where do I find the Strategy Guide? Here: http://community qonqr com/index php?/topic/1191-the-official-qonqr-strategy-guide/ How do I win? That is for you to decide  There is still much to discover  We don’t even know if QONQR is good or evil  Why is it here? What is its purpose? Help your faction further its goals and unlock all the secrets of QONQR!
Congratulations to the Swarm on their overwhelming victory in Atlantis in May 2015 -- taking and retaining all Atlantis zones from beginning to end is hard to argue with -- most convincing -- well done Swarm!
Rumor has it that the Duggers have 20 scopes  Some families really do have a wife and ten kids    Ha Anyways     Multi scoping is generally not encouraged, usually if you mention that you do it on the forums or in groupme your not going to be a very liked person, even within your own faction  What tends to happen is if you start multiscoping then your enemies start doing it, then you get into a war where ever person has 6 phones and nobody is happy  I've seen or heard about situations like this in a lot of different locations 
Great job, faceless! It was actually an exciting Atlantis and I prefer it that way  Way to bring your A game 
You know what we need in this game? Nano-Nukes!!
Attention (insert faction here), We, the (insert faction here) are tired of the way you constantly (circle one)   A  Cube rage us B  Bully us with numbers C  Talk mean to us It hurts our feelings because (circle one)   A  We don't cube B  We don't have as many allies C  We have no sense of humor and/or no backbone Please refrain from participating in the above selected actions for above selected reasons so as the game is enjoyable for (insert faction here)  Regards, (insert name and faction here) There we go   this should streamline the entire complaint process of the forums  Copy and reuse as needed  You're welcome 
Silver, you mentioned in a blog that there are levels beyond 150, is it safe to say there is no level limit anymore or is that for us to discover? Another question, I'm not sure if I'm the only one noticing this but it seems like bot decay doesn't work against Zone Assault bots  I've hit two players I know have been inactive for well over 6 months but my attack had the same effect it did yesterday before the update  However I did notice against Deflection bots I am getting 2x the kill power  Also in response to decontaminatoR  Paid gaming isn't just something for adults  When I was 13 the best options for handheld gaming was GameBoy or GameGear and the games at the time cost anywhere between $29-$39 dollars  I had a paper route to pay for my games  So, no offense, but you can afford $0 99 for a game  You don't even need a paper route, just check under your couch cushions and I'm sure you'll find a few quarters 
I want to start by thanking Faceless  This round of Atlantis, Legion and Faceless were doubled in sized and probably spending by Swarm  I contacted some great players from the other side and put together a nonaggression pact, this pact was one of the most impressive agreements I've seen in the more than two years of playing  Hundreds of people worldwide stuck to this agreement and put past feelings behind us  It was awesome to see both sides stick so closely to each other in fighting against Swarm  I want to really thank everyone who showed honor by standing behind me and the faceless command when we suggested that, the people who really gave it a chance and then most importantly, to all the players who honored it  Faceless, thank you very much! We stood no chance of winning without you! The battle came down to literally one launch in one of three zones  I'd say that with that being said everyone fought incredibly hard, so I want to give Swarm the respect they deserve  You guys really show out and play to win  Good game, 2 against 1 is not easy, no matter how large your crew is  And Legion, we had many late, late nights, many very long days  You guys killed it this month! We didn't take home a trophy, but I would say we all have something to be proud of! The other leaders who helped me coordinate everything were awesome! So many great people kept everything moving forward 24 hours a day for the whole week  Thank you to everyone who gave it your all for the whole week even when we saw that Swarm had 4x our bots at the end of just one day  Many people would have given up, but we held in and **** near won! I've won Atlantis battles with Faceless and with Legion but I will say, nothing was as fun or incredible as this round! You guys are fantastic and I can't wait to do it all again next month! Hopefully we have more Legion and Faceless show up for the next round, but I know that even if we don't, we will figure out a way, just as warriors do  PEW! PEW! PEW!
I must start with an announcement: Camels are not the only animal in the middle east  You have overused it already  It's saddens me that anyone would still find it funny  Get a bit of originality  For anyone not wanting to read all this drivel, skip to the end (hint: Bold stuff)  At what point have I bullied anyone? When was the last aggressive or threatening message you or any of your members received from me? Never  Legion and Swarm (in the UK) have teamed up because Faceless are dominating in London  That makes sense  It's a three sided fight and if one force becomes too powerful, the other two can join forces to try and take them down  But: We are dominating in the London area while your alliance is attacking players outside of that area  Then you expect me to sit back and do nothing  You are specifically using me as an excuse for why you have teamed up but then you're attacking players outside of my reach, sometimes with Europe involved  If that is not reason enough to attack you then I'm not sure what is  You lot cube, multiscope and have multi faction accounts and still complain  I don't complain about anything you do  Play the way you want to, ill do the same  I am never rude to anyone, always polite no matter what the message is, never brag about what I do, can do or have done, never threaten anyone, try and keep in touch with the few swarm or legion who are civilised to me, listen to any message form any side and if I can help in any way, I try my best to  Formed agreements with enemy (that I didn't need to) that ended up slapping me in the face  I have even in the past taken the time to find out who some of the younger players are so I know to avoid them  When I'm in a different country I try and find who the bullies are  Those players who threaten and brag and laugh at others  Those are my targets (if they exist in those areas)  I don't see how anyone can think I'm a bully  Is it your money? Its none of your business how I spend it  Either way your numbers are way off  700 dollars a day? Did you just decide to blindly strike the number pad to come up with that figure? Best part of one of your posts is saying that the devs should worry about my welfare  What concerns you about me spending my money? Maybe I'll self harm due to overspending? I wont have enough to buy food because I bought too many cubes? I don't understand what they are supposed to worry about  I have issues? At what point did you deduce this oh mighty psychologist? Wait a minute are you my bank account manager? What do you know about how much I can or cant afford? Random guy spends money on something he enjoys  The end  Told you last time to give up on all the drama but I guess thank you for the concern  You play to make me spend more? And? What do you think that accomplishes? In fact how do you even make me spend? You don't even attack me  You sulk when I'm in the UK and when I fly out and you find out, you call in Europe to help you take an zone or two  As they say, whatever floats your boat  As for Atlantis: Please try again  I quit Atlantis when we were winning  I have on occasion involved myself after I was asked to help out but on the whole I give it a miss  The last time (months ago) was the last 10 minutes of Atlantis and Legion fought hard  We lost  I was not the only Faceless player to quit Atlantis  Quite a few of us thought it lasted too long and had too many zones to fight over  I hear the duration has been reduced  You can't honestly believe I changed my sleeping pattern for the game  I was in California for a month  I was jet lagged  My sleeping pattern was a bit off when I got back  You're not even accurate about when I deploy  Pay attention  You should know this by now: I play as and when I want, sometimes every 20 minutes and sometimes I do long stretches and sometimes I'm busy and don't deploy for hours  You wont always win, and nor will I  Try and get satisfaction form at least trying to win or putting up a good fight  About the limit on cubing  Please  I suggested that last year  Twice  Unfortunately multiscoping is allowed and so prevalent that it kind of ruins the idea  These are the facts: YOU and YOUR side threatened Faceless members who support London  One of the members threatened is actually London based  What do you expect him to do while his city is under attacked? A few of your members don't know when to keep quiet  They sent messages to us threatening specific players and telling them their zones will be dropped just because they have helped London  Basically if they help London then they get their zones wiped  And you have the cheek to call me a bully? We stood up to your members specifically because they were trying to bully  That is the reason we went strong months ago and took those big zones  How is this me bullying you? This is me answering your threats  I didn't attack those zones "just because i can", it's just because I should  Because of your threats  You can blame your members for the loss of those 2 or 3 big zones  These are the basics: You attack one of our zones  We look at the list of attackers and pick one of the players who deployed the most, find a zone of his or hers and attack it  We don't need to justify our attacks with "because there are Faceless players within 30 miles"  What, every time we attack a zone we need to send some letter explaining why? You attack us or we attack you, for any reason  That's the game  When some of you were cheating and you could not win you complained, when you cube and lose you complain, when you invite all of Europe to attack and win you still complain  I just think you like to make a fuss  Last words (for now): You think I attack zones as a means of getting attention? I get enough of it form your threads  The only attention seeker here is you with your victim attitude and pity us posts 
I've had some very angry emails today from a couple users who are upset their rival achieved the ability to switch factions freely having played for one of the factions for only 1 hour  I've been accused of doing favors, changing the rules, and various other backhanded deals  It appears it comes down reading the rules  You do not have to play for every faction for 60 days in order to earn free switching status  Here is a common scenario many players have used to achieve free switching status and avoiding playing for one faction they despise  1  Start with Swarm 2  Switch to Legion (earn Spy) play for 60 days 3  Switch back to Swarm (earn Double Agent) play for 60 days 4  Switch to Faceless (earn Mercenary) immediately switch back to Swarm or Legion Below is the complete text on the switch nanobots screen  It is the same text that has been there from Day 1 with the exception of the level 100 rules that went into place earlier this year, where you could switch as much as you want before level 100 , but those switches don't count towards the medals  This text has been part of this description since Jan 15, 2013  "Players that earn all three spy awards, may once again switch factions at any time as they could during the training levels 1 through 99 " Prior to Jan 15, the text said this  "Players that earn all three awards may be given the opportunity to switch factions more quickly in future updates (contact support for more information)" I pulled that right out of source control, which includes the entire change history  Here is the complete text from this page  http://portal qonqr    r/SwitchFaction WARNING: Defection has consequences! Self-destruct will be initiated on all your nanobots  Without the self-destruct, you would be required to battle against your former self to regain control of your zones  You will lose the capture and leadership of any zones you currently hold  Lifetime captures will be unaffected  If you are still completing the training levels and have not reached Level 100, you may switch as often as you like to find the faction that suits you best  Once you have reached Level 100 switching factions has rewards, but also has additional consequences beyond the self-destruct of all your nanobots  Defection will usually result in a demotion in rank  This is accomplished through awards with negative rank points  Those awards are: Spy - First switch to an opposing faction (-20 points) Double Agent - Return to a faction from which you had previously defected (-20 points) Mercenary - Become a member of all three factions (-20 points) Other Faction Change Details: You may not switch factions again until at least 60 days have passed since your last faction switch  Defection point penalties are applied only once per award Players that earn all three spy awards, may once again switch factions at any time as they could during the training levels 1 through 99  The decision to switch factions is one that must be made with strong determination  Nanobots cannot be reanimated once destroyed  You will retain your earned experience, level, formations, qredits, cubes, and upgrades  However, as far as your zones go, you will be starting over 
   has got to be one of the funniest moments I've seen in qonqr yet lol 
The **** change operation was a success!
As a relatively new player for faceless in a region dominated by swarm i can understand the OP  However, judging from the numbers i see here on a regular basis i think you are asking a bit much  My idea would be the opposite approach  Why not add a weapon with extremely short range, let's say like 5km that acts like a bomb and make it much stronk? That would add some serious home advantage  Or alternativley make attack formations lose power over range (exclude nanos and plasma)  Maybe something like that would allow newcomers to at least get a foothold in their homezones  It's just an idea, maybe i overlooked something?
This is by design  Some day it is possible (I said someday) we could offer skins for your scope  So we will need a uniform color scheme  You can tell the formation families based on the shape of the box  Trapezoid is attack, diamond is defense, and octagon is support  It will take some time to get comfortable with the change 
Geophysical based game  Anyways, probably not a bad idea but, considering the issues with the three platforms and the development of blue for those platforms, I doubt the resources are available for development on a new platform  Seen the blog? Its Qonqr meets wheel of fortune!
I am happy to announce that today I both completed the training and captured my first zone 
^THIS so now that qonqr has been thoroughly funded, can we have blue now? Or is that not happening still lol 
Atleast I am not legion and there for we can have this intelligent discussion rather than just compete over who has the best words XD ohhhhh someone bring the bill to legion cuz someone just served them extra double order of stir fried SNAPPPPPP If the devs wont make zone dueling for us I hope out there somewhere are those who would empty a zone and challenge one on one to a local battle  I'd like to see the transcript of deployments made / moves made as well that would be neat I think such events would be cool  I suppose if people give up on atlantis as it works now they can schedule their own tournements in empty atlantis zones   have a team clear the zone   put 1 vs 1 or teams vs teams   like fisticuffs challenges   find out what these warriors are really made of!
@Qonqrd everyone you know must face palm every time you make a post  Its embarrassing  Mega cubers or whatever you want to call them are not great for the opposing team surrounding them but are great for the game itself (money) and for the team they are part of  Multiscopers are not not great for the opposing team surrounding them and bring nothing to the game but are great for the team they are part of  Both have a negative impact on enemy teams/players but only one benefits the game itself  Both can make people want to quit out of frustration  And that's not great for the game  @OP unlimited refresh is over powered  Its frustrating to fight against a ridiculous amount of refreshes  Unfortunately i dont see anything changing unless this game gets a lot more people playing  More people might mean more money for the company from various sources  More money from various players might mean they can limit the players who spend a ton and still generate a healthy income  The main issue i see with limiting refreshes is someone multiscoping and spending money  He now has two, three, four accounts to refresh with and gets the advantage  Its tricky 
ey dun new ho to yet it uff 
Yet you complain almost everyday here, on your website, Twitter, and YouTube channel that the game needs to change because cubing has such an impact 
It was fun  Swarm had me scared at first, but it turned into kind of a bullying match between us and legion  Last hour became p obvious which way it was gonna go  Legion rly stepped up their game in the end there, respect 
We are investigating this  Here is what we know: Several of the accounts used the same password  Most of the accounts belonged to people who knew each other personally  The accounts were all switched from the same IP Addresses  The person who logged in, got into each account on the first attempt, so they knew the password for each account  What you should know: QONQR never stores passwords, not even in the logs  Passwords are hashed (one way encrypted) and can never be decrypted When you authenticate to our servers, we hash the password you gave us and compare it to the encrypted password in the database to see if they match  Access to our database in the could is restricted tightly and we are confident no one breached the system  What you should do: Don't use the same password as other people you play with  Don't share your password with anyone 
I heard all the French players fled to the UK after one German player accidentally shot a single missile into France 
Most factions now use GroupMe or Line as their means of communication, the forums are too slow as a means of communication and insecure for specific faction conversations  Think of the forums are more of a gaming information resource rather than a means of communication  Contact the top players of your faction in the leader boards of your state and they will likely point you in the right direction to chatting with your local faction  The developers are also building some sort of new chat system into the game, we don't know much about it but apparently beta testing for the chat will be happening very soon (next couple of weeks) according to their timelines 
A way to honor the dead? Nah, how bout a way to dishonor the dead 
Could just build it up and retain their capture  Remember Bizzy, staq to the heavens 
I just read this entire thread  I am now tuckered 
Does anyone know if Bot Booster has an effect on Seekers and how much dmg they do to attacking players? Also on the topic of seekers, does the amount of skeers in a battlefield have any effect on how much damage they do?
Interactive map of real-time zone captures 
You know that is something I didn't factor in there  Time  The player who can consistently and constantly launch wins against the guy who casually picks up the phone on occasion or has to work away from a cell phone for 8 hours  Good point  And yes I can't argue skill doesn't factor in, it just seems like less of a factor than other games is all 
I cant see why a closed forum, open only to registered Qonqr accounts, cant be used  **** spammers!
Gotta love synclock! I suspect linjin has a problem  Maybe the dvs should look into it 
No, Naamah   I clearly understood what you were trying to convey  I'll even go as far as to agree that what your facing now is, while fully allowed and deemed completely acceptable by the developers, unbalanced and wrong  However  the imbalance isn't in the game and isn't something that, from a business standpoint, is likely to be regulated  The game is fair  the advantages are provided to all players  It's the players themselves that throw the balance into chaos because, as you've said, not everyone can afford spending several thousand bucks a year in only one game  Truthfully, in my opinion, the moment you admitted to buying cubes yourself your complaint became silly  because I'm sure there's a player out there, who has bought NO cubes, who can make this same complaint about you that you are making about others here  I know these things because I've read them so   many   times in this forum  There was likely a time, ages ago when I was a young Massune, that I even posted a few myself  That was the purpose of my post  I was poking fun at addition of yet another cuber/bully/trash talk complaint on the forum  It wasn't directed at your personal plight so much as the idea that someone, yet again, finds it necessary to lobby for a spending cap on the only real way for this game to make money  As to your specific problem  you, like all those that have raised this topic before you, have few options to rectify the issue  Here are a few that seem to have worked for others  fight harder, recruit better, spend less time complaining and more time organizing, budget for more cubes or quit  I'd rather not see you opt for the latter  but to each their own 
I agree, we should find a way to honor the dead, but I don't think keeping their towers infinitely is necessarily the solution  The game must go on  I'm pretty sure the point of bot decay was to clear the game of inactive player bots so that new players can have a chance to rise up, not to dishonor the bots of dead players 
The following are frequently asked questions about the new server update (so far) It still says Training Complete on my iPhone  -	Download the update from iTunes It still says Training Complete on my Android  -	Sorry, Android will not be updated again until the QONQR Blue beta is released My XP per launch keeps going down -	This is XP throttling and is intended to limit the ability for people to leve1 from 1 to 100 in a single day through heavy cubing  The XP throttle was introduced with the original version of QONQR in 2012,and the throttle formula is the same for levels above 100  The throttle resets at midnight UTC every day  How do I buy the Bot Regeneration Accelerator? -	Currently the Bot Regen Accelerator can only be purchased through http://portal qonqr com  Go to the Depot and review your scope upgrades  The new QONQR Blue clients will allow for this purchase to be made in the app using your mobile billing  I don’t have a PayPal account -	For users interested in purchasing the Regen upgrade, but who do not have a PayPal account, PayPal does give you the option to checkout using your billing information without creating an account  PayPal is not allowed in my country, or I don’t have a credit or debit card -	Please contact support@QONQR com for alternate options Is Bot Regen Accelerator counted as part of the 100% scope upgrades? -	Yes, but there is a bug that does not increase scope upgrade percent when you purchase this upgrade, that will be fixed in the coming days  For all other questions, please read the 7 blog posts prior to 7/29/2015 for information on what was included in the update today 
Bye Fack, its been a pleasure being allied and against you 
The two big issues that are both killing the game slowly and keeping it from growing exponentially are cube injustice and new player ramp  The game obviously also needs to provide a consistent and growing revenue stream as well  I think Silver needs to rethink how revenue is generated if he is going to address cube injustice and new player ramp  For revenue generation I would suggest a model that doesn't give a significant combat advantages  Download and play for free from level 0-99 Pay small monthly fee to get full functionality or play for free at 50% of offensive/defensive funtionality Still buy cubes, but cubes are used for following: - Credit Boost: harvest more credits for a period of time - Range Extension: ability to use standard attack/defense formations at extended ranges - Base Share: get 100% credit attainment even in bases owned by another faction - Purchase additional ordinance - Zone Name Change - ability to customize zone names  "Breggland" - Faction Change with Bots - pay for the ability to keep up to 50% of your bots with faction change - Experience Boost: %increase in experienced gained while leveling - Other: anything that helps grow a player or provides enjoyment, but doesn't tip the battle capability of a scope  New Player Ramp/Integration into Game - Offer paid immediate ramp package: one price to become 100 with full upgrades - Like the changes in Blue - Create new zones in Metro areas that only 0-99 level can launch into, with statewide ranges Understand that catering to those who have money and like to use it for an advantage is a good business model and for those people it might be ok to offer very expensive options: - Shield generators: temporary energy shield that adds X% increase to defense or stops X% of damage - EMP's: turns Absorbs off for X minutes  Does not destroy, just turns off - Chain Lighting: Does damage across multiple players in a zone From a development standpoint I have no idea what is possible, easy or hard, but the general idea is to make the playing field more fair for the standard player while maintaining and growing a business revenue stream 
You need to come to the Northeast US  We handle our business like no other 



ground_truth: Keep deploying and harvesting your bases to earn experience points and level up quickly.