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Active character technology based on neural networks Genetic Algorithms and AI

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  1. Introduction
  2. Basic biological neuron
  3. Artificial neuron with weights
  4. Decision making: Firing rules
  5. Active character technology in multimedia & gaming
  6. Characters created virtually by Endorphine
  7. Virtual Stunt men, created using neural networks & genetic algorithms, replicating a fall
  8. Conclusion
  9. References

A combination of Neural networks genetic algorithms and conventional computing in Multimedia and Gaming industry has never been attempted before. Existing technologies operate on fixed databases and manipulate limited data that is bound by some logical algorithm. Even many of the AI based products don't implement intelligent thinking. Using a combination of AI,ANN genetic algorithms & conventional computers intelligent gaming characters and intelligent Virtual Stuntmen can be created that simulate a realistic atmosphere. Even though the technical implementation is difficult, the future is promising. This paper looks at the possibilities of such a complex system and its different components. Artificial Neural Networks are a very different paradigm of computing. Artificial Neural Networks are self-learning mechanisms, which don't require the traditional skills of a programmer. A few of these neuron-based structures, paradigms actually, are being used commercially.

[...] This output is then input into other processing elements, or to an outside connection, as dictated by the structure of the network. Neural Networks have a large number of Artificial neurons that have massive interaction capabilities spread over ,generally layers: Fig: Layers Of Neural Net Decision making: Firing rules The firing rule is an important concept in neural networks and accounts for their high flexibility. A firing rule determines whether a neuron should fire for any input pattern. It relates to all the input patterns, not only the ones on which the node was trained. [...]

[...] This training phase can consume a lot of time and is considered complete when the neural network reaches a user defined performance level. This level signifies that the network has achieved the desired statistical accuracy as it produces the required outputs for a given sequence of inputs. When no further learning is necessary, the weights are typically frozen for the application. Some network types allow continual training, at a much slower rate, while in operation. This helps a network to adapt to gradually changing conditions. [...]

[...] Active Character Technology In Multimedia & Gaming : There are different stunts and scenes in multimedia where use of humans is too risky or too costly esp when on a massive scale. One way is to use animated characters which are drawn frame by frame. This is very resource hogging and time consuming process hence Virtual characters that actually ?learn' how to move their bodies, and generate action sequences that usually have to be performed by stuntmen or animated characters can be used. [...]

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