A look-ahead learning algorithm for inductive learning through examples

1992 ◽  
Author(s):  
Ray R. Hashemi ◽  
Frederick R. Jelovsek
Keyword(s):  
Learning Algorithm ◽  
Inductive Learning ◽  
Look Ahead

Application of a Rough Set-Based Inductive Learning System

1993 ◽  
Vol 18 (2-4) ◽  
pp. 209-220
Author(s):  
Michael Hadjimichael ◽  
Anita Wasilewska
Keyword(s):  
Machine Learning ◽  
Rough Set ◽  
Presidential Election ◽  
Predictive Accuracy ◽  
Learning Algorithm ◽  
Inductive Learning ◽  
Real Data ◽  
Semantic Content ◽  
Learning System ◽  
Voter Preferences

We present here an application of Rough Set formalism to Machine Learning. The resulting Inductive Learning algorithm is described, and its application to a set of real data is examined. The data consists of a survey of voter preferences taken during the 1988 presidential election in the U.S.A. Results include an analysis of the predictive accuracy of the generated rules, and an analysis of the semantic content of the rules.

scholarly journals How to Shift Bias: Lessons from the Baldwin Effect

1996 ◽  
Vol 4 (3) ◽  
pp. 271-295 ◽  
Author(s):  
Peter Turney
Keyword(s):  
Learning Algorithm ◽  
Inductive Learning ◽  
Learning Algorithms ◽  
Darwinian Evolution ◽  
Baldwin Effect ◽  
Lamarckian Evolution ◽  
Inheritance Of Acquired Characteristics ◽  
The Baldwin Effect ◽  
Fixed Bias ◽  
Shift Of Bias

An inductive learning algorithm takes a set of data as input and generates a hypothesis as output. A set of data is typically consistent with an infinite number of hypotheses; therefore, there must be factors other than the data that determine the output of the learning algorithm. In machine learning, these other factors are called the bias of the learner. Classical learning algorithms have a fixed bias, implicit in their design. Recently developed learning algorithms dynamically adjust their bias as they search for a hypothesis. Algorithms that shift bias in this manner are not as well understood as classical algorithms. In this paper, we show that the Baldwin effect has implications for the design and analysis of bias shifting algorithms. The Baldwin effect was proposed in 1896 to explain how phenomena that might appear to require Lamarckian evolution (inheritance of acquired characteristics) can arise from purely Darwinian evolution. Hinton and Nowlan presented a computational model of the Baldwin effect in 1987. We explore a variation on their model, which we constructed explicitly to illustrate the lessons that the Baldwin effect has for research in bias shifting algorithms. The main lesson is that it appears that a good strategy for shift of bias in a learning algorithm is to begin with a weak bias and gradually shift to a strong bias.

A SIMPLE FORECASTING GAME

2006 ◽  
Vol 17 (02) ◽  
pp. 279-286 ◽  
Author(s):  
M. ANDRECUT
Keyword(s):  
Learning Algorithm ◽  
Inductive Learning ◽  
Numerical Results ◽  
Future State ◽  
Unexpected Consequence ◽  
The Future ◽  
Correct Forecast

We consider a population of Boolean agents playing a simple forecasting game, in which the goal of each agent is to give a correct forecast of the future state of its neighbors. The numerical results show that by using a simple inductive learning algorithm the agents are able to accurately achive the goal of the game. However, this remarkable performance has an unexpected consequence: by learning to forecast the future, the agents dynamics freezes up at the end of the game; the only way to regain their dynamics is to forget what they have learned.

scholarly journals Graphical evolving transformation system machine

2015 ◽  
Author(s):  
◽  
Thanh Thieu
Keyword(s):  
Learning Algorithm ◽  
Minimum Description Length ◽  
Inductive Learning ◽  
Human Perception ◽  
Machine Intelligence ◽  
Transformation System ◽  
Noisy Input ◽  
Wide Range ◽  
Real World Datasets ◽  
Class Representation

For years, scientists have challenged the machine intelligence problem. Learning classes of objects followed by the classification of objects into their classes is a common task in machine intelligence. For this task, two objects representation schemes are often used: a vector-based representation, and a graph-based representation. While the vector representation has sound mathematical background and optimization tools, it lacks the ability to encode relations between the patterns and their parts, thus lacking the complexity of human perception. On the other hand, the graph-based representation naturally captures the intrinsic structural properties, but available algorithms usually have exponential complexity. In this work, we build an inductive learning algorithm that relies on graph-based representation of objects and their classes, and test the framework on a competitive dataset of human actions in static images. The method incorporates three primary measures of class representation: likelihood probability, family resemblance typicality, and minimum description length. Empirical benchmarking shows that the method is robust to the noisy input, scales well to real-world datasets, and achieves comparable performance to current learning techniques. Moreover, our method has the advantage of intuitive representation regarding both patterns and class representation. While applied to a specific problem of human pose recognition, our framework, named graphical Evolving Transformation System (gETS), can have a wide range of applications and can be used in other machine learning tasks.

scholarly journals A Dynamic Holding Approach to Stabilizing a Bus Line Based on the Q-Learning Algorithm with Multistage Look-Ahead

2021 ◽  
Author(s):  
Sheng-Xue He ◽  
Jian-Jia He ◽  
Shi-Dong Liang ◽  
June Qiong Dong ◽  
Peng-Cheng Yuan
Keyword(s):  
Learning Algorithm ◽  
Control Strategies ◽  
Waiting Times ◽  
Line System ◽  
Q Learning ◽  
Unstable Phase ◽  
Look Ahead ◽  
Bus Bunching ◽  
Detailed Simulation ◽  
Past Experiences

The unreliable service and the unstable operation of a high-frequency bus line are shown as bus bunching and the uneven distribution of headways along the bus line. Although many control strategies, such as the static and dynamic holding strategies, have been proposed to solve the above problems, many of them take on some oversimplified assumptions about the real bus line operation. So it is hard for them to continuously adapt to the evolving complex system. In view of this dynamic setting, we present an adaptive holding method that combines the classic approximate dynamic programming (ADP) with the multistage look-ahead mechanism. The holding time, the only control means used in this study, will be determined by estimating its impact on the operation stability of the bus line system in the remaining observation period. The multistage look-ahead mechanism introduced into the classic Q-learning algorithm of the ADP model makes it easy that the algorithm gets through its earlier unstable phase more quickly and easily. During the implementation of the new holding approach, the past experiences of holding operations can be cumulated effectively into an artificial neural network used to approximate the unavailable Q-factor. The use of a detailed simulation system in the new approach makes it possible to take into account most of the possible causes of instability. The numerical experiments show that the new holding approach can stabilize the system by producing evenly distributed headway and removing bus bunching thoroughly. Compared with the terminal station holding strategies, the new method brings a more reliable bus line with shorter waiting times for passengers.

scholarly journals A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems

2019 ◽  
Vol 33 ◽  
pp. 1393-1400 ◽  
Author(s):  
Ling Pan ◽  
Qingpeng Cai ◽  
Zhixuan Fang ◽  
Pingzhong Tang ◽  
Longbo Huang
Keyword(s):  
Reinforcement Learning ◽  
Spatial Information ◽  
Learning Algorithm ◽  
Service Level ◽  
Divide And Conquer ◽  
Gradient Algorithm ◽  
Learning Framework ◽  
Imbalance Problem ◽  
Look Ahead ◽  
Bike Sharing

Bike sharing provides an environment-friendly way for traveling and is booming all over the world. Yet, due to the high similarity of user travel patterns, the bike imbalance problem constantly occurs, especially for dockless bike sharing systems, causing significant impact on service quality and company revenue. Thus, it has become a critical task for bike sharing operators to resolve such imbalance efficiently. In this paper, we propose a novel deep reinforcement learning framework for incentivizing users to rebalance such systems. We model the problem as a Markov decision process and take both spatial and temporal features into consideration. We develop a novel deep reinforcement learning algorithm called Hierarchical Reinforcement Pricing (HRP), which builds upon the Deep Deterministic Policy Gradient algorithm. Different from existing methods that often ignore spatial information and rely heavily on accurate prediction, HRP captures both spatial and temporal dependencies using a divide-and-conquer structure with an embedded localized module. We conduct extensive experiments to evaluate HRP, based on a dataset from Mobike, a major Chinese dockless bike sharing company. Results show that HRP performs close to the 24-timeslot look-ahead optimization, and outperforms state-of-the-art methods in both service level and bike distribution. It also transfers well when applied to unseen areas.

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