Relational world models that can be learned from experience in stochastic domains have received significant attention recently. However, efficient planning using these models rema...
We propose an approach to transformational planning and learning of everyday activity. This approach is targeted at autonomous robots that are to perform complex activities such a...
While exploring to nd better solutions, an agent performing online reinforcement learning (RL) can perform worse than is acceptable. In some cases, exploration might have unsafe, ...
Satinder P. Singh, Andrew G. Barto, Roderic A. Gru...
In this paper the notion of a partial-order plan is extended to task-hierarchies. We introduce the concept of a partial-order taskhierarchy that decomposes a problem using multi-ta...
— Probabilistic sampling-based motion planners are unable to detect when no feasible path exists. A common heuristic is to declare a query infeasible if a path is not found in a ...
Kris K. Hauser, Timothy Bretl, Jean-Claude Latombe