A novel method for learning policies from variable constraint data
نویسندگان
چکیده
منابع مشابه
A novel method for learning policies from variable constraint data
Many everyday human skills can be framed in terms of performing some task subject to constraints imposed by the environment. Constraints are usually unobservable and frequently change between contexts. In this paper, we present a novel approach for learning (unconstrained) control policies from movement data, where observations come from movements under different constraints. As a key ingredien...
متن کاملMethods for Learning Control Policies from Variable-Constraint Demonstrations
Many everyday human skills can be framed in terms of performing some task subject to constraints imposed by the task or the environment. Constraints are usually not observable and frequently change between contexts. In this chapter, we explore the problem of learning control policies from data containing variable, dynamic and non-linear constraints onmotion.We discuss how an effective approach ...
متن کاملA New Method for Solving Constraint Satisfaction Problems
Many important problems in Artificial Intelligence can be defined as Constraint Satisfaction Problems (CSP). These types of problems are defined by a limited set of variables, each having a limited domain and a number of Constraints on the values of those variables (these problems are also called Consistent Labeling Problems (CLP), in which “Labeling" means assigning a value to a variable.) Sol...
متن کاملA Constraint Acquisition Method for Data Clustering
A new constraint acquisition method for parwise-constrained data clustering based on user-feedback is proposed. The method searches for non-redundant intra-cluster and inter-cluster query-candidates, ranks the candidates by decreasing order of interest and, finally, prompts the user the most relevant query-candidates. A comparison between using the original data representation and using a learn...
متن کاملLearning Pedagogical Policies from Few Training Data
Learning a pedagogical policy in an Adaptive Educational System (AIES) fits as a Reinforcement Learning (RL) problem. However, to learn pedagogical policies requires to acquire a huge amount of experience interacting with the students, so applying RL to the AIES from scratch is infeasible. In this paper we describe RLATES, an AIES that uses RL to learn an accurate pedagogical policy to teach a ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Autonomous Robots
سال: 2009
ISSN: 0929-5593,1573-7527
DOI: 10.1007/s10514-009-9129-8