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ironfroggy

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  1. Honestly, it looks like all you are doing is overcomplicating currying or partial function application. The examples in the book seem a little off to show you exactly what a factory can be, because they don't have to create more than one kind of object, of course, and they can definately include initialization data for the objects they create. "Generator" is a different concept entirely, and I know it as an iterable object that generates its values on request with a co-routine kind of function, such as with Python.
  2. This kind of thing is nothing new. Games like the Creatures series are based on the idea, and I can't be sure but Spores might use something like this. And, BP can work for the training, given there is some way for the feedback to indicate correctness of output. Internal feedback can be used in situations where the gameplay doesnt allow for it (like tickling and spanking pets in Creatures), so the AI would need to be told when something was good or bad. An option is to actually feed the state after a decision into the ANN, and use it to determine if something was good or not. This could yield interesting results, like virtual women who will be naughty because they like a good spanking.
  3. You could make a fun game of this for testing it out. Have one or more players control the predator (a dog) and try and herd the prey (sheep) into a pen or something.
  4. So, Microsoft charges you for the slower compiler and gives the optimizing version away for free? That is a little confusing, I think. I'm glad I can just stick with GCC.
  5. Look into an open source RTS, like Stratagus (http://stratagus.sourceforge.net/) or Glest (http://www.glest.org/eng/index.htm).
  6. Take a look at 'The Battle for Wesnoth', its a great open source, hex-tile, turn-based tactical battle game. You could easily replace the maps and graphics and campaigns to create your game, and its very polished and stable.
  7. The AP is a little off in his/her reply, because perceptrons can be used in multiple layers and for non-linear results. A very good article about them can be found at http://generation5.org/content/1999/perceptron.asp
  8. If the individual particles of our universe are of a free will, we have no method of perceiving this fact, or conversing with them on the matter. Thus, the decisions they may make may as well be random. This brings about the convergence of your second and third possibilities, as there is no perceivable difference to the human mind, which is all we have to study the universe. As to the statements about reality being subject to definition, this is a major flaw in your line of thinking. Reality is a product of our imaginations, no matter what anyone things. We interact not on external sensory, but on internal representations that may be influenced heavily by external sensory, thus our definitions of the components of reality are of equal or greater importance than the driving forces behind those components. When we try to understand things like what it is to think and to feel and to be self-aware, it is natural to debate on the definitions of the terms used in the discussion. This is because of two reasons: the ambiguality of human languages and the classifications of realities components according to our own perceptions, differing from individual to individual (and, species to species?). Thus, without a proper and solid definition of the description we have of our universe, we can not properly analyze it. Pertaining to intelligence, this means that what it means to think and feel is dependant largely, if not solely, on our own definitions and understanding on what it means to think and feel and to be self-aware. Thus, as with any problem, we must break out questions into their primal pieces and recombine them in a more simple matter. Are we asking what it would take to make a machine think, or are we asking what it would take to make a machine perform the same functions we perceive as thinking in a human being, or even in a less organism with a sufficiently complex neural system? There is much debate between the intelligence of artificial intelligence and the immitation of intelligence by artificial intelligence. However, if we consider that intelligence is not in the implementation (being neurons in our case and software in a computer's), but in the resulting relationship between the being and the being's enviroment (humans and the universe, AI and data, robots and the universe), we find a very interesting conclusion: immitation is no less than being truly intelligent *. * Here, I use "truly intelligent" to mean the means of intelligence of a human being, or other living creature, as opposed to that of an artificial intelligence, in those cases where one thinks it important to make any distinction between the two. Of course, if your Possibility #1 is true, it may be safe to assume the architecture of the universe and this Will Element to be such that the Will affect matter only in situations and manners where it could not be perceived by any intelligence derived from a collection of Will-driven particles. The affects upon the matter would be sparse and subtle, but numerous, as to combine in a larger scale to generate the forces desired. If this is all true, no debate will ever determine the true nature of the universe, although any speculation we make considering Will to not exist as an element may as well be true, as still there is a reality both outside and inside, and it is only the second we truly live in. Irregaurdless of the reasons behind our conciousness, immitation indistinguisable from otherwise is as real as any. Immitation may have been the key design of our own race by the Unknown Creator(s), and so who are we to place judgement upon those we may likewise create? This, of course, brings questions into play on the nature of gods and their own intelligence and how it relates to ours. In the end, its nothing but another discussion of grammer and sytax.
  9. In reply to the AP: That is exactly why using a Neuron class is such a good idea. If you have reason to prefer grid-matrix later on, you can store matrices in a static matrix member of the class, and just rewrite the accesors to use the matrix, so none of the code actually using the Neurons needs to change.
  10. I prefer to have an actual Neuron class which stores weights of inputs. something like this is appropriate: class Connection { public: float weight; Neuron * source; }; class Neuron { private: std::vector<Connection> inputs; bool fireState; public: Neuron(std::vector<Connection> &); bool getState(); void processInput(); void addInput(Neuron *, float); void dropInput(Neuron *); void adjustWeight(Neuron *, float); };
  11. An ANN for this might not have to be too complex. If you have the AI move back to the goal (maybe a little randomly offsetted each time?) after hitting the puck, the ANN will be reacting from the same basic position everytime, simplifying things a lot. Feed the position and vector of the puck into the ANN when the puck becomes within a min and max pair of radiuses from the goal, this simulates differing reaction times. Make that min/max pair close enough, and the AI will be able to react uniquely, but reliably. The output could be a simple vector to move in. The longer the vector, the higher the velocity. Limit this, of course. Train this manually, genetically, or through back-propagation, or some such and you might come up with a convincing AI player. Later, you might even add more ANNs, like one that controls the movement back to the goal, or one to pass the time until the reaction (move back and forth or something maybe?). I'm a fan of multi-part AIs with events to trigger which are used when.
  12. One method could be a recursive procedure of Check-For-Collision and Resolve-Collision, where after each resolution you then check the involved objects for additional collisions during the same time-frame. Thus, you would detect the collision between F and E first, then resolve them by an energy transerence from F to E, and then check if either has any further collisions in this frame. In this case, E collides with A, so you resolve that, check for more collisions, find none, etc. The repeating collision detections can be optimized because you already know the objects that may or may not collide, from previous tests.