Motion planning has seen much attention over the past two decades. A great deal of progress has been made in sampling-based planning, whereby a planner builds an approximate representation of the planning space. While these planners have demonstrated success in many scenarios, there are still difficult problems where they lack robustness or efficiency, e.g., certain types of narrow spaces. Conv...

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 e ects, universally quanti ed preconditions and e ects, and with universally quanti ed goals. We prove ucpop is both sound and complete for this representation and describe a practical implementation that succeeds...

In this work, we consider the design of a probabilistic trajectory planner for a highly maneuverable unmanned air vehicle flying in a dense and complex city-like environment. Our design hinges on the decomposition of the problem into a) flight controls of fundamental agile-maneuvering flight modes and b) trajectory planning using these controlled flight modes from which almost any aggressive ma...

Planning with epistemic goals has received attention from both the dynamic logic and planning communities. In the single-agent case, under the epistemic closed-world assumption (ECWA), epistemic planning can be reduced to contingent planning. However, it is inappropriate to make the ECWA in some epistemic planning scenarios, for example, when the agent is not fully introspective, or when the ag...

The probabilistic contingent planner ZANDER (Majercik 2000) operates by converting the planning problem to a stochastic satisfiability problem and solving that problem instead. Although ZANDER can solve some simple standard test problems more efficiently than three alternative approaches to probabilistic planing, ZANDER is currently confined to small problems. We introduce APROPOS, a probabilis...

The current best conformant probabilistic planners encode the problem as a bounded length CSP or SAT problem. While these approaches can find optimal solutions for given plan lengths, they often do not scale for large problems or plan lengths. As has been shown in classical planning, heuristic search outperforms CSP/SAT techniques (especially when a plan length is not given a priori). The probl...

We review in this paper some recent yet fundamental results on evaluating queries over probabilistic databases. While one can see this problem as a special instance of general purpose probabilistic inference, we describe in this paper two key database specific techniques that significantly reduce the complexity of query evaluation on probabilistic databases. The first is the separation of the q...

The classical planning problem can be enriched with quantitative and qualitative user-defined preferences on how the system behaves on achieving the goal. In this paper, we propose the probabilistic preference planning problem for Markov decision processes, where the preferences are based on an enriched probabilistic LTL-style logic. We develop P4Solver, an SMT-based planner computing the prefe...

Most AI representations and algorithms for plan generation have not included the concept of informationproducing actions (also called diagnostics, or tests, in the decision making literature). We present a planning representation and algorithm that models information-producing actions and constructs plans that exploit the information produced by those actions. We extend the buridan (Kushmerick ...

This work focuses on developing domain-independent heuristics for probabilistic planning problems characterized by full observability and non-deterministic effects of actions that are expressed by probability distributions. The approach is to first search for a high probability deterministic plan using a classical planner. A novel probabilistic plan graph heuristic is used to guide the search t...