Showing papers by "Luke Zettlemoyer published in 2004"
Proceedings Article•
02 Jun 2004TL;DR: In this paper, an algorithm for learning probabilistic STRIPS-like planning operators from examples is presented, and the authors demonstrate the effective learning of rule-based operators for a wide range of traditional planning domains.
Abstract: To learn to behave in highly complex domains, agents must represent and learn compact models of the world dynamics. In this paper, we present an algorithm for learning probabilistic STRIPS-like planning operators from examples. We demonstrate the effective learning of rule-based operators for a wide range of traditional planning domains.
56 citations
Proceedings Article•
03 Jun 2004TL;DR: An algorithm for learning probabilistic STRIPS-like planning operators from examples is presented and the effective learning of rule-based operators for a wide range of traditional planning domains is demonstrated.
Abstract: To learn to behave in highly complex domains, agents must represent and learn compact models of the world dynamics. In this paper, we present an algorithm for learning probabilistic STRIPS-like planning operators from examples. We demonstrate the effective learning of rule-based operators for a wide range of traditional planning domains.
10 citations
01 Jan 2004
TL;DR: A three-dimensional blocks-world simulation built with the OpenDynamics toolkit that represents world action dynamics using probabilistic planning rules to take advantage of the inherent structure found in many uncertain, complex environments.
Abstract: Figure 1: A three-dimensional blocks-world simulation built with the OpenDynamics toolkit [7]. The world consists of a table, blocks of roughly uniform size and mass, and a robotic hand that is moved by simulated motors. Motivation: Robust robotic control in complex worlds is a challenging problem. Hand-engineering a solution is difficult and time-consuming. Developing techniques that will allow robots to gather knowledge about the world and use it to design their own control strategies seems like a reasonable alternative. Previous Work: We represent world action dynamics using probabilistic planning rules. Figure refrelrules-fig shows two rules that model actions that can be performed by the robotic arm in the blocks world of Figure 1. Such rules enable us to take advantage of the inherent structure found in many uncertain, complex environments by making the following assumptions about the world:
5 citations