Probabilistic path planning driven by a potential field is a well established technique and has been successfully exploited to solve complex problems arising in a variety of domains. However, planners implementing this approach are rather inefficient in dealing with certain types of local minima occurring in the potential field, especially those characterized by deep or large attraction basins....

Models of planning under uncertainty, and in particular, MDPs and POMDPs have received much attention in the AI and DecisionTheoretic planning communities (Boutilier, Dean, and Hanks 1999; Kaelbling, Littman, and Cassandra 1998). These models allow for a richer and more realistic representation of real-world planning problems, but lead to increased complexity. Recently, a new approach for handl...

In this paper, we present a new hybrid motion planner that i s capable of exploiting previous planning episodes when confronted with new planning problems. Our approach i s applicable when several (similar) problems are successively posed for the same static environment, or when the environment changes incrementally between planning episodes. At the heart of our system lie two low-level motion ...

Conformant probabilistic planning (CPP) differs from conformant planning (CP) by two key elements: the initial belief state is probabilistic, and the conformant plan must achieve the goal with probability ≥ θ, for some 0 < θ ≤ 1. In earlier work we observed that one can reduce CPP to CP by finding a set of initial states whose probability≥ θ, for which a conformant plan exists. In previous solv...

Many real-world planning problems involve a combination of both time and uncertainty (Bresina et al. 2002). For instance, Aberdeen et al. (2004) investigate military operations planning problems that feature concurrent durative actions, probabilistic timed effects, resource consumption, and competing cost measures. It is the potential for such practical applications that motivates this research...

We de ne the probabilistic planning problem in terms of a probability distribution over initial world states, a boolean combination of propositions representing the goal, a probability threshold, and actions whose e ects depend on the execution-time state of the world and on random chance. Adopting a probabilistic model complicates the de nition of plan success: instead of demanding a plan that...

We consider how to learn useful relational features in linear approximated value function representations for solving probabilistic planning problems. We first discuss a current feature-discovering planner that we presented at the International Conference on Automated Planning and Scheduling (ICAPS) in 2007. We then propose how the feature learning framework can be further enhanced to improve p...

We describe the ucpop partial order planning algorithm which handles a subset of Pednault's ADL action representation. In particular, ucpop operates with actions that have conditional eeects, universally quan-tiied preconditions and eeects, and with universally quantiied goals. We prove ucpop is both sound and complete for this representation and describe a practical implementation that succeed...