Researchers at the Technion - Israel Institute of Technology found a way to simplify decision-making and problem-solving under uncertainty in a way that reduces the amount of information computers need to analyze. In an age of self-driving cars and other robots, this is an approach likely to enable autonomous online decision making in challenging scenarios, reduce response times, and achieve considerable savings in the cost of hardware and other resources.” The study is related to a problem in AI and robotics called autonomous decision-making under uncertainty, which concerns the capability of AIs to complete tasks reliably and autonomously over time in an unpredictable environment. The findings may help researchers solve decision-making problems using simplification and show that there are ways to save considerable amounts of computation time without a loss of accuracy. Technion noted that autonomous agents often do not have access to the variables related to a particular problem and instead function based on a "belief" based on probability models and measurements. In order to solve this problem, an AI must weigh the costs and benefits of a potential action, which requires the researchers to predict how the "belief" will change over time. “We demonstrate that we can significantly reduce computation time, without harming the successful execution of the task,” the researchers said. A major area of research in the new study was computationally efficient decision-making under these conditions, called belief space planning. A new peer-reviewed study published in the International Journal of Robotics Research, led by Prof. Vadim Indelman, who heads the Autonomous Navigation and Perception Lab (ANPL) at Technion's Faculty of Aerospace Engineering, and Khen Elimelech, shows the feasibility of reducing the amount of time for computers to process information without compromising the success of completing a function. (The Jerusalem Post). Continue reading.
Researchers at the Technion - Israel Institute of Technology found a way to simplify decision-making and problem-solving under uncertainty in a way that reduces the amount of information computers need to analyze.
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