| Goldenstein, S., Large, E. and Metaxas, D. Dynamic Autonomous Agents: Game Applications. Center for Human Modeling and Simulation, Computer Science Department, University of Pennsylvania. Dynamic Systems for Autonomous Agents Navigation. Complex Systems and Brain Sciences, Florida Atlantic University (Large 1999. |
....different set of governing rules. On the other hand, learning, perception, and dynamics based techniques [50,48,55,40,26,33,12] can easily adapt to dynamically changing environments. Some initial attempts to use nonlinear dynamical systems for low level behavior modeling have appeared recently in [24,25]. Methods from computational geometry have also been employed [57,28] Different systems and approaches have been developed to add distinct behaviors to autonomous agents [55,10,16,11] The usefulness of a layered approach to modeling low and high level behaviors has been advocated by several ....
....and goal direction. A simplistic approach would be to sum the obstacle and target contribu 7 tions to obtain (0, env) f = ftar q fobs. 2) Unfortunately this approach does not always produce the desired result. Undesirable behaviors may result due to the nonlinearity of the summed functions [36,24]. To overcome this problem we use an adaptive weighted sum of the target and obstacle contributions (see 3) instead of a direct sum. 4.1.1 Non spherical Obstacles Certain environments need non spherical objects to be well described. Examples include walls in rooms or mazes, the ocean s bottom, ....
S. Goldenstein, E. Large, and D. Metaxas. Dynamic autonomous agents: Game applications. In Proceedings of Computer Animation 98, June 1998.
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Goldenstein, S., Large, E. and Metaxas, D. Dynamic Autonomous Agents: Game Applications. Center for Human Modeling and Simulation, Computer Science Department, University of Pennsylvania. Dynamic Systems for Autonomous Agents Navigation. Complex Systems and Brain Sciences, Florida Atlantic University (Large 1999.
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