Probabilistic planning captures the uncertainty of plan execution by probabilistically modeling the effects of actions in the environment, and therefore the probability of reaching different states from a given state and action. In order to compute a solution for a probabilistic planning problem, planners need to manage the uncertainty associated with the different paths from the initial state to a goal state. Several approaches to manage uncertainty were proposed, e.g., consider all paths at once, perform determinization of actions, and sampling. In this paper, we introduce trajectory-based short-sighted Stochastic Shortest Path Problems (SSPs), a novel approach to manage uncertainty for probabilistic planning problems in which states reachable with low probability are substituted by artificial goals that heuristically estimate their cost to reach a goal state. We also extend the theoretical results of Short-Sighted Probabilistic Planner (SSiPP) [1] by proving that SSiPP always finishes and is asymptotically optimal under sufficient conditions on the structure of short-sighted SSPs. We empirically compare SSiPP using trajectorybased short-sighted SSPs with the winners of the previous probabilistic planning competitions and other state-of-the-art planners in the triangle tireworld problems.

Probabilistic planning captures the uncertainty of plan execution by probabilistically modeling the effects of actions in the environment, and therefore the probability of reaching different states from a given state and action. In order to compute a solution for a probabilistic planning problem, planners need to manage the uncertainty associated with the different paths from the initial state to a goal state. Several approaches to manage uncertainty were proposed, e.g., consider all paths at once, perform determinization of actions, and sampling. In this paper, we introduce trajectory-based short-sighted Stochastic Shortest Path Problems (SSPs), a novel approach to manage uncertainty for probabilistic planning problems in which states reachable with low probability are substituted by artificial goals that heuristically estimate their cost to reach a goal state. We also extend the theoretical results of Short-Sighted Probabilistic Planner (SSiPP) [ref] by proving that SSiPP always finishes and is asymptotically optimal under sufficient conditions on the structure of short-sighted SSPs.

Eugene Schieffelin was the eccentric ornithologist who in 1890 shipped 60 starlings from London to New York City and set them free in Central Park. The next year he released 40 more, and today there are maybe 200 million starlings in the United States and Southern Canada. As immigrants go, starlings are shrewd flyers, clever mimics, and often unwelcome. The truth is they're no more than uptown blackbirds, stocky, three-ounce grifters with iridescent blue and green plumage, along with yellow beaks and a long history of displacing woodpeckers and flycatchers, and destroying entire crops of berries and cherries. Not to mention the havoc they cause at many airports.

Probabilistic planning captures the uncertainty of plan execution by probabilistically modeling the effects of actions in the environment, and therefore the probability of reaching different states from a given state and action. In order to compute a solution for a probabilistic planning problem, planners need to manage the uncertainty associated with the different paths from the initial state to a goal state. Several approaches to manage uncertainty were proposed, e.g., consider all paths at once, perform determinization of actions, and sampling. In this paper, we introduce trajectory-based short-sighted Stochastic Shortest Path Problems (SSPs), a novel approach to manage uncertainty for probabilistic planning problems in which states reachable with low probability are substituted by artificial goals that heuristically estimate their cost to reach a goal state. We also extend the theoretical results of Short-Sighted Probabilistic Planner (SSiPP) [ref] by proving that SSiPP always finishes and is asymptotically optimal under sufficient conditions on the structure of short-sighted SSPs.

The artificial intelligence and voice recognition space has been growing rapidly. According to a Gartner report, by 2020, 85% of customer interactions will be managed without a human. This is pretty much likely, as we are already teaching our machines to interpret data into logical solutions. Apps running on artificial intelligence should make a user's life easy, but what goes into building such apps? To understand it further, we spoke to some successful founders who have built and scaled apps running on artificial intelligence algorithms.

The artificial intelligence and voice recognition space has been growing rapidly. According to a Gartner report, by 2020, 85% of customer interactions will be managed without a human. This is pretty much likely, as we are already teaching our machines to interpret data into logical solutions. Apps running on artificial intelligence should make a user's life easy, but what goes into building such apps? In a #Bitesize interview with us, Xavier Amatriain, VP of engineering at Quora, explained the key to successful machine learning in developing products.

Probabilistic planning captures the uncertainty of plan execution by probabilistically modeling the effects of actions in the environment, and therefore the probability of reaching different states from a given state and action. In order to compute a solution for a probabilistic planning problem, planners need to manage the uncertainty associated with the different paths from the initial state to a goal state. Several approaches to manage uncertainty were proposed, e.g., consider all paths at once, perform determinization of actions, and sampling. In this paper, we introduce trajectory-based short-sighted Stochastic Shortest Path Problems (SSPs), a novel approach to manage uncertainty for probabilistic planning problems in which states reachable with low probability are substituted by artificial goals that heuristically estimate their cost to reach a goal state. We also extend the theoretical results of Short-Sighted Probabilistic Planner (SSiPP) [ref] by proving that SSiPP always finishes and is asymptotically optimal under sufficient conditions on the structure of short-sighted SSPs. We empirically compare SSiPP using trajectory-based short-sighted SSPs with the winners of the previous probabilistic planning competitions and other state-of-the-art planners in the triangle tireworld problems. Trajectory-based SSiPP outperforms all the competitors and is the only planner able to scale up to problem number 60, a problem in which the optimal solution contains approximately $10^{70}$ states.