There have been several proposals for expressing planning problems with different forms of uncertainty, including nondeterminism and partial observability. In this paper we invest...
Temporally extended goals (TEGs) refer to properties that must hold over intermediate and/or final states of a plan. Current planners for TEGs prune the search space during planni...
We present a new algorithm for conformant probabilistic planning, which for a given horizon produces a plan that maximizes the probability of success under quantified uncertainty ...
Temporally extended goals (TEGs) refer to properties that must hold over intermediate and/or final states of a plan. The problem of planning with TEGs is of renewed interest becau...
We consider a planning problem that generalizes Alcuin's river crossing problem (also known as: The wolf, goat, and cabbage puzzle) to scenarios with arbitrary conflict graph...
We describe HTN-MAKER, an algorithm for learning hierarchical planning knowledge in the form of decomposition methods for Hierarchical Task Networks (HTNs). HTNMAKER takes as inpu...
The world is unpredictable, and acting intelligently requires anticipating possible consequences of actions that are taken. Assuming that the actions and the world are determinist...
The problem of minimally modifying a plan in response to changes in the specification of the planning problem has already been investigated in the literature. In this paper we cons...
abstract. In this paper we investigate a formalism for solving planning problems based on ordered task decomposition using Answer Set Programming (ASP). Our planning methodology is...
In this paper, we study strategies in incremental planning for ordering and grouping subproblems partitioned by the subgoals of a planning problem when each subproblem is solved b...