دانلود رایگان مقاله دامنه و محدوده شبیه سازی در استدلال خودکار

abstract

In scientific computing and in realistic graphic animation, simulation – that is, step-by-step calculation of the complete trajectory of a physical system – is one of the most common and important modes of calculation. In this article, we address the scope and limits of the use of simulation, with respect to AI tasks that involve high-level physical reasoning. We argue that, in many cases, simulation can play at most a limited role. Simulation is most effective when the task is prediction, when complete information is available, when a reasonably high quality theory is available, and when the range of scales involved, both temporal and spatial, is not extreme. When these conditions do not hold, simulation is less effective or entirely inappropriate. We discuss twelve features of physical reasoning problems that pose challenges for simulation-based reasoning. We briefly survey alternative techniques for physical reasoning that do not rely on simulation.

5. Conclusion

the limits of simulation With infinite computational power, infinite time and unlimited memory, perhaps anything could be simulated, from the bottom up, much as Pierre Simon Laplace [37] once imagined, An intellect which at a certain moment would know all forces that set nature in motion, and all positions of all items of which nature is composed, if this intellect were also vast enough to submit these data to analysis, it would embrace in a single formula the movements of the greatest bodies of the universe and those of the tiniest atom; for such an intellect nothing would be uncertain and the future just like the past would be present before its eyes. But in the real world, computational power is finite, decisions must be made in finite time, and memory resources are limited. Neither the mind nor an automated problem solver can hope to model the interactions of everyday objects, let alone the interactions of complex agents like human beings, by simulations that originate at a quantum or even a molecular level. Instead, real-world simulations that run in realistic time with reasonable resources must use approximations and idealization at a higher level of abstraction. In many cases, setting up the simulation – choosing the approximations and idealizations appropriate to the problem – and interpreting the output of the simulation – deciding how accurate the results of the simulation are likely to be, which parts of the simulation are valid, and which parts are artifacts of the idealization – can be much more difficult than executing the simulation. It is simply naïve to imagine that, for example, an automated reasoner could infer all that it needs to know about physics by running a Newtonian physical simulator on every entity that it encountered. With the many examples that we have reviewed in this paper, we hope to have made clear that the full-simulation view of cognition is entirely unrealistic and by the same token that simulation, however precise, is equally unlikely to solve the problems of automated commonsense physical reasoning.