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About 10,000 papers for “learning rules”

Learning rules

Peter Dayan · Current Biology · 2020

Cited by 0Open access

Learning Rules

Susmita Bandyopadhyay · Business analytics · 2025

Cited by 0

Learning rules

Dayan, P. · MPG.PuRe (Max Planck Society) · 2020

Why we learn, what we learn, what the conditions are under which we learn, how the learning happens, and what the answers to these questions imply about how we should teach are the topics of this urgent, compelling, and highly multidisciplinary book by Stanislas Dehaene. He is one of the world’s most storied and celebrated cognitive scientists and cognitive neuroscientists. Along the way, we get to enjoy some mental gymnastics concerning the joint roles of nature and nurture, a wealth of revealing and sometimes heart-wrenching individual examples, and a glimpse into the potential potency but current impoverishment of machine learning systems.

Cited by 0

Learning Rules: Overview

Klaus Obermayer, Joaquı́n J. Torres · Encyclopedia of Computational Neuroscience · 2022

Cited by 0

Learning Transformation Rules

For Semantic Role, Ken Williams, Christopher Dozier et al. · 2007

This paper presents our work on Semantic Role Labeling using a Transformation-Based ErrorDriven approach in the style of Eric Brill (Brill, 1995). Our approach achieved an overall F 1 score of 43.48 on non-verb annotations. We believe our approach is noteworthy because of its novelty in this area and because it produces short lists of human-understandable transformation rules as its output.

Cited by 0

Learning Liability Rules

Donald Wittman, Daniel Friedman, Stephanie Crevier et al. · The Journal of Legal Studies · 1997

We conduct experiments regarding the equilibrium and convergence properties of three different liability rules: negligence with contributory negligence, comparative negligence, and no‐fault. Our experimental results show that, in comparison to contributory negligence, comparative negligence promotes a faster and more reliable convergence to the efficient equilibrium. Furthermore, as predicted by theory, the no‐fault equilibrium yields suboptimal amounts of effort. Along the way we also test various hypotheses regarding learning and other adjustment dynamics. Thus our article extends the traditional static notion of institutional choice—liability rules with efficient equilibria are chosen—to a more dynamic perspective—rules that rapidly achieve efficient equilibria are chosen.

Cited by 33

Learning Musical Rules

Eduardo Morales M., Roberto Morales M. · 1995

Traditional musical analysis attempts to understand and explain the choices made by a composer in a particular piece. History in composition and analysis have shown that composers using the same patterns in structure and harmony get different results depending on the way these patterns are resolved. In particular, musical analysis can be described by a sequence of states and transitions between states representing the personal criteria that each composer follows when solving a musical structure. A system that could be trained with the preference criteria, used by a composer, for transitions between states, could be used to analyze his/her work and provide suggestions for his/her compositions. A first-order learning system, called Pal, is used to learn transition criteria for counterpoint analysis, in the form of Horn clauses from pairs of musical states (given as sets of notes) and general purpose musical knowledge. It is shown how the rules learned by Pal can be used for musical analy...

Cited by 0

Learning Musical Rules

Eduardo F. Morales, Ramon Morales · 1995

Traditional musical analysis attempts to understand and explain the choices made by a composer in a particular piece. History in composition and analysis have shown that composers using the same patterns in structure and harmony get different results depending on the way these patterns are resolved. In particular, musical analysis can be described by a sequence of states and transitions between states representing the personal criteria that each composer follows when solving a musical structure. A system that could be trained with the preference criteria, used by a composer, for transitions between states, could be used to analyze his/her work and provide suggestions for his/her compositions. A first-order learning system, called Pal, is used to learn transition criteria for counterpoint analysis, in the form of Horn clauses from pairs of musical states (given as sets of notes) and general purpose musical knowledge. It is shown how the rules learned by Pal can be used for musical analy...

Cited by 9

Learning Policies by Learning Rules

Brandon Araki, Jeana Choi, Lillian Chin et al. · IEEE Robotics and Automation Letters · 2021

Efficiently learning interpretable policies for complex tasks from demonstrations is a challenging problem. We present Hierarchical Inference with Logical Options (HILO), a novel learning algorithm that learns to imitate expert demonstrations by learning the rules that the expert is following. The rules are represented as linear temporal logic (LTL) formulas, which are interpretable and capable of encoding complex behaviors. Unlike previous works, which learn rules from high-level propositions, HILO learns rules by taking both propositions and low-level trajectories as input. It does this by defining a Bayesian model over LTL formulas, propositions, and low-level trajectories. The Bayesian model bridges the gap from formula to low-level trajectory by using a planner to find an optimal policy for a given LTL formula. Stochastic variational inference is then used to find a posterior distribution over formulas and policies given expert demonstrations. We show that by learning rules from both propositions and low-level states, HILO outperforms previous work on a rule-learning task and on four planning tasks while needing less data. We also validate HILO in the real world by teaching a robotic arm a complex packing task.

Cited by 1Open access

Synaptic learning rules for sequence learning

Eric T. Reifenstein, Richard Kempter · bioRxiv (Cold Spring Harbor Laboratory) · 2020

Abstract Remembering the temporal order of a sequence of events is a task easily performed by humans in everyday life, but the underlying neuronal mechanisms are unclear. This problem is particularly intriguing as human behavior often proceeds on a time scale of seconds, which is in stark contrast to the much faster millisecond time-scale of neuronal processing in our brains. One long-held hypothesis in sequence learning suggests that a particular temporal fine-structure of neuronal activity — termed “phase precession” — enables the compression of slow behavioral sequences down to the fast time scale of the induction of synaptic plasticity. Using mathematical analysis and computer simulations, we find that — for short enough synaptic learning windows — phase precession can improve temporal-order learning tremendously and that the asymmetric part of the synaptic learning window is essential for temporal-order learning. To test these predictions, we suggest experiments that selectively alter phase precession or the learning window and evaluate memory of temporal order.

Cited by 4Open access

Learning to Plan by Learning Rules

Araki, Minoru Brandon · DSpace@MIT (Massachusetts Institute of Technology) · 2021

Many environments involve following rules and tasks; for example, a chef cooking a dish follows a recipe, and a person driving follows rules of the road. People are naturally fluent with rules: we can learn rules efficiently; we can follow rules; we can interpret rules and explain them to others; and we can rapidly adjust to modified rules such as a new recipe without needing to relearn everything from scratch. By contrast, deep reinforcement learning (DRL) algorithms are ill-suited to learning policies in rule-based environments, as satisfying rules often involves executing lengthy tasks with sparse rewards. Furthermore, learned DRL policies are difficult if not impossible to interpret and are not composable. The aim of this thesis is to develop a reinforcement learning framework for rule-based environments that can efficiently learn policies that are interpretable, satisfying, and composable. We achieve interpretability by representing rules as automata or Linear Temporal Logic (LTL) formulas in a hierarchical Markov Decision Process (MDP). We achieve satisfaction by planning over the hierarchical MDP using a modified version of value iteration. We achieve composability by building off of a hierarchical reinforcement learning (HRL) framework called the options framework, in which low-level options can be composed arbitrarily. And lastly, we achieve data-efficient learning by integrating our HRL framework into a Bayesian model that can infer a distribution over LTL formulas given a low-level environment and a set of expert trajectories. We demonstrate the effectiveness of our approach via a number of rule-learning and planning experiments in both simulated and real-world environments.

Cited by 0

Learning Complex Recursive Rules.

Ching-liang Tseng · 1993

We consider the problem of learning complex recursive rules which involve new concepts other than those given in the input relations and which must be discovered by the learning algorithm in the course of finding rules. The existing learning methods (FOIL, FORGE, and etc.) create rules based on the given concepts or relations but they cannot create new concepts. However, in many cases one must use new intermediate concepts in order to form the recursive rules. We give a new technique for constructing such intermediate concepts and learning rules based on those concepts. We illustrate the new technique with several examples, none of which can be handled by the existing methods. We have implemented the new technique in Common Lisp and tested many different examples.

Cited by 0Open access