Learning action models for reactive autonomous agents
Learning action models for reactive autonomous agents
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Probabilistic Planning in the Graphplan Framework
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
Learning partially observable deterministic action models
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Incremental Learning of Planning Operators in Stochastic Domains
SOFSEM '07 Proceedings of the 33rd conference on Current Trends in Theory and Practice of Computer Science
An Experiment in Robot Discovery with ILP
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
A Simple Model for Sequences of Relational State Descriptions
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
The initial development of object knowledge by a learning robot
Robotics and Autonomous Systems
An Inductive Logic Programming Approach to Statistical Relational Learning
Proceedings of the 2005 conference on An Inductive Logic Programming Approach to Statistical Relational Learning
Autonomous development of a grounded object ontology by a learning robot
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Online learning and exploiting relational models in reinforcement learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Building relational world models for reinforcement learning
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Probabilistic rule learning in nonmonotonic domains
CLIMA'11 Proceedings of the 12th international conference on Computational logic in multi-agent systems
Learning and reasoning with action-related places for robust mobile manipulation
Journal of Artificial Intelligence Research
Action-model acquisition from noisy plan traces
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Refining incomplete planning domain models through plan traces
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
| Hi-index | 0.00 |
We present an algorithm for learning a model of the effects of actions in noisy stochastic worlds. We consider learning in a 3D simulated blocks world with realistic physics. To model this world, we develop a planning representation with explicit mechanisms for expressing object reference and noise. We then present a learning algorithm that can create rules while also learning derived predicates, and evaluate this algorithm in the blocks world simulator, demonstrating that we can learn rules that effectively model the world dynamics.