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  • Boosting my GA with Neural Networks and/or Reinforcement Learning

    - by AlexT
    As I have mentioned in previous questions I am writing a maze solving application to help me learn about more theoretical CS subjects, after some trouble I've got a Genetic Algorithm working that can evolve a set of rules (handled by boolean values) in order to find a good solution through a maze. That being said, the GA alone is okay, but I'd like to beef it up with a Neural Network, even though I have no real working knowledge of Neural Networks (no formal theoretical CS education). After doing a bit of reading on the subject I found that a Neural Network could be used to train a genome in order to improve results. Let's say I have a genome (group of genes), such as 1 0 0 1 0 1 0 1 0 1 1 1 0 0... How could I use a Neural Network (I'm assuming MLP?) to train and improve my genome? In addition to this as I know nothing about Neural Networks I've been looking into implementing some form of Reinforcement Learning, using my maze matrix (2 dimensional array), although I'm a bit stuck on what the following algorithm wants from me: (from http://people.revoledu.com/kardi/tutorial/ReinforcementLearning/Q-Learning-Algorithm.htm) 1. Set parameter , and environment reward matrix R 2. Initialize matrix Q as zero matrix 3. For each episode: * Select random initial state * Do while not reach goal state o Select one among all possible actions for the current state o Using this possible action, consider to go to the next state o Get maximum Q value of this next state based on all possible actions o Compute o Set the next state as the current state End Do End For The big problem for me is implementing a reward matrix R and what a Q matrix exactly is, and getting the Q value. I use a multi-dimensional array for my maze and enum states for every move. How would this be used in a Q-Learning algorithm? If someone could help out by explaining what I would need to do to implement the following, preferably in Java although C# would be nice too, possibly with some source code examples it'd be appreciated.

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  • Help with Neuroph neural network

    - by user359708
    For my graduate research I am creating a neural network that trains to recognize images. I am going much more complex than just taking a grid of RGB values, downsampling, and and sending them to the input of the network, like many examples do. I actually use over 100 independently trained neural networks that detect features, such as lines, shading patterns, etc. Much more like the human eye, and it works really well so far! The problem is I have quite a bit of training data. I show it over 100 examples of what a car looks like. Then 100 examples of what a person looks like. Then over 100 of what a dog looks like, etc. This is quite a bit of training data! Currently I am running at about one week to train the network. This is kind of killing my progress, as I need to adjust and retrain. I am using Neuroph, as the low-level neural network API. I am running a dual-quadcore machine(16 cores with hyperthreading), so this should be fast. My processor percent is at only 5%. Are there any tricks on Neuroph performance? Or Java peroformance in general? Suggestions? I am a cognitive psych doctoral student, and I am decent as a programmer, but do not know a great deal about performance programming.

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  • Entropy using Decision Tree's

    - by Matt Clements
    Train a decision tree on the data represented by attributes A1, A2, A3 and outcome C described below: A1 A2 A3 C 1 0 1 0 0 1 1 1 0 0 1 0 For log2(1/3) = 1.6 and log2(2/3) = 0.6, answer the following questions: a) What is the value of entropy H for the given set of training example? b) What is the portion of the positive samples split by attribute A2? c) What is the value of information gain, G(A2), of attribute A2? d) What is IFTHEN rule(s) for the decision tree?

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  • whats the diference between train, validation and test set, in neural networks?

    - by Daniel
    Im using this library http://pastebin.com/raw.php?i=aMtVv4RZ to implement a learning agent. I have generated the train cases, but i dont know for sure what are the validation and test sets, the teacher says: 70% should be train cases, 10% will be test cases and the rest 20% should be validation cases. Thanks. edit i have this code, for training.. but i have no ideia when to stop training.. def train(self, train, validation, N=0.3, M=0.1): # N: learning rate # M: momentum factor accuracy = list() while(True): error = 0.0 for p in train: input, target = p self.update(input) error = error + self.backPropagate(target, N, M) print "validation" total = 0 for p in validation: input, target = p output = self.update(input) total += sum([abs(target - output) for target, output in zip(target, output)]) #calculates sum of absolute diference between target and output accuracy.append(total) print min(accuracy) print sum(accuracy[-5:])/5 #if i % 100 == 0: print 'error %-14f' % error if ? < ?: break

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  • Word characteristics tags

    - by theBlinker
    I want to do a riddle AI chatbot for my AI class. So i figgured the input to the chatbot would be : Something like : "It is blue, and it is up, but it is not the ceiling" Translation : <Object X> <blue> <up> <!ceiling> </Object X> (Answer : sky?) So Input is a set of characteristics (existing \ not existing in the object), output is a matched, most likely object. The domain will be limited to a number of objects, i could input all attributes myself, but i was thinking : How could I programatically build a database of characteristics for a word? Is there such a database available? How could i tag a word, how could i programatically find all it's attributes? I was thinking on crawling wikipedia, or some forum, but i can't see it build any reliable word tag database. Any ideas on how i could achieve such a thing? Any ideas on some literature on the subject? Thank you

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  • Continuous output in Neural Networks

    - by devoured elysium
    How can I set Neural Networks so they accept and output a continuous range of values instead of a discrete ones? From what I recall from doing a Neural Network class a couple of years ago, the activation function would be a sigmoid, which yields a value between 0 and 1. If I want my neural network to yield a real valued scalar, what should I do? I thought maybe if I wanted a value between 0 and 10 I could just multiply the value by 10? What if I have negative values? Is this what people usually do or is there any other way? What about the input? Thanks

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  • How to engineer features for machine learning

    - by Ivo Danihelka
    Do you have some advices or reading how to engineer features for a machine learning task? Good input features are important even for a neural network. The chosen features will affect the needed number of hidden neurons and the needed number of training examples. The following is an example problem, but I'm interested in feature engineering in general. A motivation example: What would be a good input when looking at a puzzle (e.g., 15-puzzle or Sokoban)? Would it be possible to recognize which of two states is closer to the goal?

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  • Are evolutionary algorithms and neural networks used in the same problem domains?

    - by Joe Holloway
    I am trying to get a feel for the difference between the various classes of machine-learning algorithms. I understand that the implementations of evolutionary algorithms are quite different from the implementations of neural networks. However, they both seem to be geared at determining a correlation between inputs and outputs from a potentially noisy set of training/historical data. From a qualitative perspective, are there problem domains that are better targets for neural networks as opposed to evolutionary algorithms? I've skimmed some articles that suggest using them in a complementary fashion. Is there a decent example of a use case for that? Thanks

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  • Operant conditioning algorithm?

    - by Ken
    What's the best way to implement real time operant conditioning (supervised reward/punishment-based learning) for an agent? Should I use a neural network (and what type)? Or something else? I want the agent to be able to be trained to follow commands like a dog. The commands would be in the form of gestures on a touchscreen. I want the agent to be able to be trained to follow a path (in continuous 2D space), make behavioral changes on command (modeled by FSM state transitions), and perform sequences of actions. The agent would be in a simulated physical environment.

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  • Using a Cyc Image in Windows

    - by nrhine1
    Hi, I am trying to use a Microtheory for a research project I am working on, and am having trouble getting my saved Image of constants I create to work correctly. I save the image after creating the constants using (write-image "world\MyImage") and then going to the \server\run\ directory and using run-cyc-32bit.bat -w "world\MyImage" It loads the server correctly, but not with my constants. I found the above command at the reference page here. Thank you for any help

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  • What is the 'order' of a perceptron

    - by Martin
    A few simple marks for those who know the answer. I'm doing revision for exams at the moment and one of the past questions is: What is meant by the order of a perceptron? I can't find any information about this in my lecture notes, and even google seems at a loss. My guess is that the order is the number of layers in a neural network, but this doesn't seem quite right.

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  • Is it theoretically possible to emulate a human brain on a computer?

    - by JoelK
    Our brain consists of billions of neurons which basically work with all the incoming data from our senses, handle our consciousness, emotions and creativity as well as our hormone system, etc. So I'm completely new to this topic but doesn't each neuron have a fixed function? E.g.: If a signal of strength x enters, if the last signal was x ms ago, redirect it. From what I've learned in biology about our nerves system which includes our brain because both consist of simple neurons, it seems to me as our brain is one big, complicated computer. Maybe so complicated that things such as intelligence and cognition become possible? As the most complicated things about a neuron pretty much are the chemical aspects on generating an electric singal, keeping itself alive, and eventually segmenting itself, it should be pretty easy emulating some on a computer, or? You won't have to worry about keeping your virtual neuron alive, or? If you can emulate a single neuron on a computer, which shouldn't be too hard, could you theoretically emulate more than 1000 billions of them, recreating intelligence, cognition and maybe even creativity? In my question I'm leaving out the following aspects: Speed of our current (super) computers Actually writing a program for emulating neurons I don't know much about this topic, please tell me if I got anything wrong :) (My secret goal: Make a copy of my brain and store it on some 10 million TB HDD and make someone start it up in the future)

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  • Information Modeling

    - by Betamoo
    The sensor module in my project consists of a rotating camera, that collects noisy information about moving objects in the surrounding environment. The information consists of distance, angle and relative change of the moving objects.. The limiting view range of the camera makes it essential to rotate the camera periodically to update environment information... I was looking for algorithms / ways to model these information, in order to be able to guess / predict / learn motion properties of these object.. My current proposed idea is to store last n snapshots of each object in a queue. I take weighted average of positions and velocities of moving object, but I think it is a poor method... Can you state some titles that suit this case? Thanks

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  • China’s Better Life Selects Oracle® Retail to Support Hypermarket Growth

    - by user801960
    On Monday, China’s first multi-format retailer, Better Life Commercial Chain Share Co. announced that it has selected a broad selection of Oracle solutions including specific Oracle Retail applications to support the growth of its hypermarket operations. Better Life currently operates 186 hypermarkets, department stores, consumer electronics stores, as well as entertainment and real estate operations across Southern China. The company’s expansion strategy for its hypermarket business is integral to its overall plan for rapid growth in an increasingly competitive market and after evaluating Oracle and SAP, Better Life identified a range of Oracle solutions including components of Oracle Retail Merchandising Operations Management, Oracle Retail Merchandise Planning and Optimization, and Oracle Retail In-Store Operations as key enablers to optimizing its operations. The Oracle Retail offering will help Better Life to create a consolidated view of product, price, inventory and associated back office information that facilitates improved fulfilment of customer demand.  These solutions will also provide a better understanding of inventory from buying through store transactions, delivering actionable insight with which it can make smarter, more profitable decisions around planning, forecasting and replenishment. You can read the full blog post here: http://www.oracle.com/us/corporate/press/1680357

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  • IDC: Oracle Doubles Down on Life Sciences Sales & Marketing

    - by charles.knapp
    "This past week Oracle held its 5th annual Life Sciences Forum in Princeton, NJ. The conference provided a wide range of content focused on their products and partnerships across the life science spectrum. But this year's conference placed strong emphasis on Oracle's new CRM On Demand Life Sciences Edition R17, and deservedly so. R17 is the largest, and most impressive, CRM On Demand release that Oracle has had to date, and it provides many significant upgrades over earlier versions." Read more here.

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  • The Future of Life Assurance Conference Recap

    - by [email protected]
    I recently wrote about the Life Insurance Conference held in Washington, DC last month. This week I was both an attendee and guest speaker the 13th Annual Future of Life Assurance Conference held at The Guoman Tower in London, UK. It's amazing that these two conferences were held on opposition sides of the Atlantic Ocean and addressed many of the same session topics and themes. Insurance is certainly a global industry! This year's conference was attended by many of the leading carriers and CEOs in the UK and across Europe.The sessions included a strong lineup of keynote speakers and panel discussions from carriers such as Legal & General, Skandia, Aviva, Standard Life, Friends Provident, LV=, Zurich UK, Barclays and Scottish Life. Sessions topics addressed a variety of business and regulatory issues including: Ensuring a profitable future Key priorities in regulation The future of advice The impact of the RDR on distribution Bancassurance Gaining control of the customer relationship Revitalizing product offerings In addition, Oracle speakers (Glenn Lottering and myself) led specific sessions on gearing up for Solvency II and speeding product development through adaptive rules-based systems. The main themes that played throughout many of the sessions included: change is here, focusing on customers, the current economic crisis has been challenging and the industry needs to get back to the basics and simplify - simplify - simplify. Additionally, it is clear that the UK Life & Pension markets will be going through some major changes as new RDR regulation related to advisor fees and commission and automatic enrollment are rolled out in 2012 Roger A.Soppe, CLU, LUTCF, is the Senior Director of Insurance Strategy, Oracle Insurance.

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  • How to stop reducing life? [closed]

    - by SystemNetworks
    CODE Input input = gc.getInput(); int xpos = Mouse.getX(); int ypos = Mouse.getY(); emu = "Enemy Life : " + enemyLife; Life = "Your Life Is" + life; Mousepos = "X:" + xpos + "Y:" + ypos; //test test1 = "Test INT" + test1int; if(!repeatStop) { //if this button is press, the damage will add up. When //pressed fight, it would start reducing the enemy health. if(input.isKeyPressed(Input.KEY_1)) { test1int += 1; } } if((xpos>1007 &xpos<1297)&&(ypos>881 && ypos<971)) { //Fight button if(Mouse.isButtonDown(0)){ finishTurn=true; } } //fight has started if(finishTurn==true) { //this would reduce the enemy life if(floodControl1==false) { enemyLife-=test1int; } //PROBLEM: Does not stop reducing! //the below code was not successful. It did not stop it // from reducing further. if(test1int>10) { floodControl1=true; } } QUESTION: Ok now, this is what is does. When I press the key, 1, it adds up the damage to the enemy. When I press fight, It will then start to reduce the enemy's health. Now my problem is, it kept on reducing and deducting it until negative! How do I deduct it to my desired damage (My desired damage is the one when press key 1)?

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  • Game of life in F# with accelerator

    - by jpalmer
    I'm trying to write life in F# using accelerator v2, but for some odd reason my output isn't square despite all my arrays being square - It appears that everything but a rectangular area in the top left of the matrix is being set to false. I've got no idea how this could be happening as all my operations should treat the entire array equally. Any ideas? open Microsoft.ParallelArrays open System.Windows.Forms open System.Drawing type IPA = IntParallelArray type BPA = BoolParallelArray type PAops = ParallelArrays let RNG = new System.Random() let size = 1024 let arrinit i = Array2D.init size size (fun x y -> i) let target = new DX9Target() let threearr = new IPA(arrinit 3) let twoarr = new IPA(arrinit 2) let onearr = new IPA(arrinit 1) let zeroarr = new IPA(arrinit 0) let shifts = [|-1;-1|]::[|-1;0|]::[|-1;1|]::[|0;-1|]::[|0;1|]::[|1;-1|]::[|1;0|]::[|1;1|]::[] let progress (arr:BPA) = let sums = shifts //adds up whether a neighbor is on or not |> List.fold (fun (state:IPA) t ->PAops.Add(PAops.Cond(PAops.Rotate(arr,t),onearr,zeroarr),state)) zeroarr PAops.Or(PAops.CompareEqual(sums,threearr),PAops.And(PAops.CompareEqual(sums,twoarr),arr)) //rule for life let initrandom () = Array2D.init size size (fun x y -> if RNG.NextDouble() > 0.5 then true else false) type meform () as self= inherit Form() let mutable array = new BoolParallelArray(initrandom()) let timer = new System.Timers.Timer(1.0) //redrawing timer do base.DoubleBuffered <- true do base.Size <- Size(size,size) do timer.Elapsed.Add(fun _ -> self.Invalidate()) do timer.Start() let draw (t:Graphics) = array <- array |> progress let bmap = new System.Drawing.Bitmap(size,size) target.ToArray2D array |> Array2D.iteri (fun x y t -> if not t then bmap.SetPixel(x,y,Color.Black)) t.DrawImageUnscaled(bmap,0,0) do self.Paint.Add(fun t -> draw t.Graphics) do Application.Run(new meform())

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