Inductive learning techniques in design process -- A design concept learning system

2001 ◽  
Vol 8 (2) ◽  
pp. 171-186 ◽  
Author(s):  
Ming Xi Tang
Keyword(s):  
Design Process ◽  
Concept Learning ◽  
Inductive Learning ◽  
Design Concept ◽  
Learning System ◽  
Learning Techniques

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.

Kinesthetic Communication for Learning in Immersive Worlds

2010 ◽  
pp. 102-116
Author(s):  
Christopher Ault ◽  
Ann Warner-Ault ◽  
Ursula Wolz ◽  
Teresa Marrin Nakra
Keyword(s):  
Video Game ◽  
Learning System ◽  
Learning Games ◽  
Learning Content ◽  
Learning Techniques ◽  
Foundational Research ◽  
The Subject ◽  
Game Environment ◽  
Drill And Practice ◽  
Close Integration

Despite the maturation of the video games medium, most self-identified learning games take the traditional but flawed approach of transmitting fact-based content to the user, frequently through the superimposition of “drill and practice” quizzes on top of interactive game-play that has little inherent relationship to the subject matter. A model is described for a Spanish-learning video game that adopts a different approach, through a close integration of the learning content and the game world context, and through the application of a motion-based controller that provides the user with an innovative and pedagogically potent mechanism for communicating with the learning system. Foundational research is discussed pertaining to kinesthetic learning techniques and their potential for language acquisition. A proof-of-concept is detailed, in which the user demonstrates learning by executing appropriate gestural responses to commands or questions spoken by non-player characters. Language mastery is essential to the user’s success in the immediate game environment, and also to resolving the game’s underlying narrative.

scholarly journals Cima: An interactive concept learning system for end-user applications

1997 ◽  
Vol 11 (7-8) ◽  
pp. 653-671 ◽  
Author(s):  
David Maulsby ◽  
Ian H. Witten
Keyword(s):  
Concept Learning ◽  
Learning System ◽  
End User

AN ALGEBRAIC APPROACH TO INDUCTIVE LEARNING

2001 ◽  
Vol 10 (01n02) ◽  
pp. 257-272
Author(s):  
ZDRAVKO MARKOV
Keyword(s):  
Concept Learning ◽  
Lattice Theory ◽  
Distance Measure ◽  
Inductive Learning ◽  
Algebraic Approach ◽  
Semantic Distance ◽  
Conceptual Clustering ◽  
Horn Clauses ◽  
Concept Hierarchies ◽  
Attribute Value

The paper presents a framework to induction of concept hierarchies based on consistent integration of metric and similarity-based approaches. The hierarchies used are subsumption lattices induced by the least general generalization operator (lgg) commonly used in inductive learning. Using some basic results from lattice theory the paper introduces a semantic distance measure between objects in concept hierarchies and discusses its applications for solving concept learning and conceptual clustering tasks. Experiments with well known ML datasets represented in three types of languages - propositional (attribute-value), atomic formulae and Horn clauses, are also presented.

Emotional Shape Generation System With Exchange of Others’ Viewpoints for Externalizing Customers’ Latent Sensitivity

2007 ◽  
Author(s):  
Hideyoshi Yanagisawa ◽  
Tamotsu Murakami
Keyword(s):  
Design Process ◽  
Design Concept ◽  
Generation System ◽  
Emotional Quality ◽  
Design Solutions ◽  
Product Aesthetics ◽  
Target Design

The aesthetics of a product’s shape has become an important factor to increase the value of mature products. However, such emotional quality regarding the customer’s need is difficult to capture due to its subjectivity. To address this issue, we have previously proposed shape generation methods that help the customers to externalize their image of product aesthetics into a shape. The previous methods enable one to generate design samples that fit the customer’s conscious image of a product shape based on his/her fixed sensitivity. However, customers also have latent sensitivities of which they are not aware. In this paper, we propose a shape generation system that enables the user to exchange design solutions and viewpoints with others. The aim of sharing solutions is to evoke the latent sensitivities by showing the unexpected viewpoints of others. To generate design samples, we improve the previous system in which the users generate design samples based on favored features to which they pay attention. We conduct a shape generation experiment using the proposed system to verify the effectiveness of exchanging solutions and viewpoints with others. We compared the effectiveness of self-solutions, which are generated without the exchange, with co-solutions, which are generated with the exchange. The result suggests that the co-solutions are more likely to be effective as to their preference and unpredictable quality. We observed certain effective patterns in the design process: All co-solutions generated by referring to unpredicted topological shapes produced effective results. Using such shapes, the subjects are able to discover new viewpoints for the target design concept. The stated metaphorical viewpoints of others also help to introduce such new viewpoints.

scholarly journals Implementation of modified Q learning technique in EMCAP control architecture

2017 ◽  
Vol 7 (1.5) ◽  
pp. 269
Author(s):  
D. Ganesha ◽  
Vijayakumar Maragal Venkatamuni
Keyword(s):  
Learning Algorithm ◽  
Control Strategies ◽  
Cognitive Architecture ◽  
Dynamic Environment ◽  
Learning System ◽  
Selection Strategy ◽  
Q Learning ◽  
Learning Techniques ◽  
Markov Decision ◽  
Environment Experiment

This research introduces a self learning modified (Q-Learning) techniques in a EMCAP (Enhanced Mind Cognitive Architecture of pupils). Q-learning is a modelless reinforcement learning (RL) methodology technique. In Specific, Q-learning can be applied to establish an optimal action-selection strategy for any respective Markov decision process. In this research introduces the modified Q-learning in a EMCAP (Enhanced Mind Cognitive Architecture of pupils). EMCAP architecture [1] enables and presents various agent control strategies for static and dynamic environment.  Experiment are conducted to evaluate the performace for each agent individually. For result comparison among different agent, the same statistics were collected. This work considered varied kind of agents in different level of architecture for experiment analysis. The Fungus world testbed has been considered for experiment which is has been implemented using SwI-Prolog 5.4.6. The fixed obstructs tend to be more versatile, to make a location that is specific to Fungus world testbed environment. The various parameters are introduced in an environment to test a agent’s performance.his modified q learning algorithm can be more suitable in EMCAP architecture.  The experiments are conducted the modified Q-Learning system gets more rewards compare to existing Q-learning.

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