On-line Learning of Macro Planning Operators using Probabilistic Estimations of Cause-Effects
نویسندگان
چکیده
In this work we propose an on-line learning method for learning action rules for planning. The system uses a probabilistic approach of a constructive induction method that combines a beam search with an example-based search over candidate rules to find those that more concisely describe the world dynamics. The approach permits a rapid integration of the knowledge acquired from experience. Exploration of the world dynamics is guided by the planner, and – if the planner fails because of incomplete knowledge – by a teacher through action instructions.
منابع مشابه
Integrating Task Planning and Interactive Learning for Robots to Work in Human Environments
Human environments are challenging for robots, which need to be trainable by lay people and learn new behaviours rapidly without disrupting much the ongoing activity. A system that integrates AI techniques for planning and learning is here proposed to satisfy these strong demands. The approach rapidly learns planning operators from few action experiences using a competitive strategy where many ...
متن کاملOn-Line Learning of a Persian Spoken Dialogue System Using Real Training Data
The first spoken dialogue system developed for the Persian language is introduced. This is a ticket reservation system with Persian ASR and NLU modules. The focus of the paper is on learning the dialogue management module. In this work, real on-line training data are used during the learning process. For on-line learning, the effect of the variations of discount factor (g) on the learning speed...
متن کاملOn-Line Learning of a Persian Spoken Dialogue System Using Real Training Data
The first spoken dialogue system developed for the Persian language is introduced. This is a ticket reservation system with Persian ASR and NLU modules. The focus of the paper is on learning the dialogue management module. In this work, real on-line training data are used during the learning process. For on-line learning, the effect of the variations of discount factor (g) on the learning speed...
متن کاملLearning to Choose Instance-Specific Macro Operators
The acquisition and use of macro actions has been shown to be effective in improving the speed of AI planners. Current macro acquisition work focuses on finding macro sets that, when added to the domain, can improve the average solving performance. In this paper, we present Instance-specific macro learning. This kind of macro filtering depends on building a predictor that can be used to estimat...
متن کاملProbabilistic Planning of Information Processing and Sensing Actions
The aim is to provide a (Bayesian) probabilistic i.e. decision-theoretic framework to analyze the information processing and sensor actions, with regard to planning a sequence of operators (and reasoning about the effects of applying the operators) in response to an user-provided query. Note that no assumption is being made on the Bayesian nature of the operators – the operators may not operate...
متن کامل