Fast planning through planning graph analysis
Artificial Intelligence
Automatically selecting and using primary effects in planning: theory and experiments
Artificial Intelligence
Using temporal logics to express search control knowledge for planning
Artificial Intelligence
An integrated environment for knowledge acquisition
Proceedings of the 6th international conference on Intelligent user interfaces
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
Autonomous Learning from the Environment
Autonomous Learning from the Environment
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
SATO: An Efficient Propositional Prover
CADE-14 Proceedings of the 14th International Conference on Automated Deduction
Plan evaluation with incomplete action descriptions
Eighteenth national conference on Artificial intelligence
Applications of SHOP and SHOP2
IEEE Intelligent Systems
PDDL2.1: an extension to PDDL for expressing temporal planning domains
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Pushing the envelope: planning, propositional logic, and stochastic search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Searching for planning operators with context-dependent and probabilistic effects
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Learning partially observable deterministic action models
Journal of Artificial Intelligence Research
Development of a tribological failure knowledge model
International Journal of Knowledge Engineering and Data Mining
Hi-index | 0.00 |
We present an action model learning system known as ARMS (Action-Relation Modelling System) for automatically discovering action models from a set of successfully observed plans. Current artificial intelligence (AI) planners show impressive performance in many real world and artificial domains, but they all require the definition of an action model. ARMS is aimed at automatically learning action models from observed example plans, where each example plan is a sequence of action traces. These action models can then be used by the human editors to refine. The expectation is that this system will lessen the burden of the human editors in designing action models from scratch. In this paper, we describe the ARMS in detail. To learn action models, ARMS gathers knowledge on the statistical distribution of frequent sets of actions in the example plans. It then builds a weighted propositional satisfiability (weighted SAT) problem and solves it using a weighted MAXSAT solver. Furthermore, we show empirical evidence that ARMS can indeed learn a good approximation of the finally action models effectively.