Planning is vital to a wide range of domains, including robotics, military strategy, logistics, itinerary generation and more, that both humans and computers find difficult. Col...
Walter S. Lasecki, Jeffrey P. Bigham, James F. All...
We present an actor-critic scheme for reinforcement learning in complex domains. The main contribution is to show that planning and I/O dynamics can be separated such that an intra...
Pedro Alejandro Ortega, Daniel Alexander Braun, Si...
We introduce TiMDPpoly, an algorithm designed to solve planning problems with durative actions, under probabilistic uncertainty, in a non-stationary, continuous-time context. Miss...
A modified version of the Bellare and Rogaway (1993) adversarial model is encoded using Asynchronous Product Automata (APA). A model checker tool, Simple Homomorphism Verificatio...
In this paper, we present the partitioning of mutual-exclusion (mutex) constraints in temporal planning problems and its implementation in the SGPlan4 planner. Based on the strong...
Probabilistic planning problems are typically modeled as a Markov Decision Process (MDP). MDPs, while an otherwise expressive model, allow only for sequential, non-durative action...
We consider a dynamic planning problem for paratransit transportation. The focus is on a decision to take one day ahead: which requests to serve with own vehicles, and which reque...
Maria L. A. G. Cremers, Willem K. Klein Haneveld, ...
Case-based planning can take advantage of former problem-solving experiences by storing in a plan library previously generated plans that can be reused to solve similar planning p...
Landmarks for a planning problem are subgoals that are necessarily made true at some point in the execution of any plan. Since verifying that a fact is a landmark is PSPACE-complet...
We describe in this paper a new method for extracting knowledge on Hierarchical Task-Network (HTN) planning problems for speeding up the search. This knowledge is gathered by prop...