Planning algorithms evaluate probability of success, suggest low-risk alternatives.
Imagine that you could tell your phone that you want to drive from your house in Boston to a hotel in upstate New York, that you want to stop for lunch at an Applebee’s at about 12:30, and that you don’t want the trip to take more than four hours. Then imagine that your phone tells you that you have only a 66 percent chance of meeting those criteria — but that if you can wait until 1:00 for lunch, or if you’re willing to eat at TGI Friday’s instead, it can get that probability up to 99 percent.
That kind of application is the goal of Brian Williams’ group at MIT’s Computer Science and Artificial Intelligence Laboratory — although the same underlying framework has led to software that both NASA and the Woods Hole Oceanographic Institution have used to plan missions.
At the annual meeting of the Association for the Advancement of Artificial Intelligence (AAAI) this month, researchers in Williams’ group will present algorithms that represent significant steps toward what Williams describes as “a better Siri” — the user-assistance application found in Apple products. But they would be just as useful for any planning task — say, scheduling flights or bus routes.
Together with Williams, Peng Yu and Cheng Fang, who are graduate students in MIT’s Department of Aeronautics and Astronautics, have developed software that allows a planner to specify constraints — say, buses along a certain route should reach their destination at 10-minute intervals — and reliability thresholds, such as that the buses should be on time at least 90 percent of the time. Then, on the basis of probabilistic models — which reveal data such as that travel time along this mile of road fluctuates between two and 10 minutes — the system determines whether a solution exists: For example, perhaps the buses’ departures should be staggered by six minutes at some times of day, 12 minutes at others.
If, however, a solution doesn’t exist, the software doesn’t give up. Instead, it suggests ways in which the planner might relax the problem constraints: Could the buses reach their destinations at 12-minute intervals? If the planner rejects the proposed amendment, the software offers an alternative: Could you add a bus to the route?
One aspect of the software that distinguishes it from previous planning systems is that it assesses risk. “It’s always hard working directly with probabilities, because they always add complexity to your computations,” Fang says. “So we added this idea of risk allocation. We say, ‘What’s your budget of risk for this entire mission? Let’s divide that up and use it as a resource.’”
The Latest on: Risk allocation software
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The Latest on: Risk allocation software
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