LEARNING STEPPINGSTONES FOR PROBLEM SOLVING

1993 ◽  
Vol 07 (03) ◽  
pp. 527-540
Author(s):  
DAVID RUBY ◽  
DENNIS KIBLER
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
General Problem ◽  
Real World ◽  
Logic Synthesis ◽  
Vlsi Design ◽  
Problem Solver ◽  
Problem Difficulty ◽  
Learning Problem ◽  
Real World Problems

One goal of Artificial Intelligence is to develop and understand computational mechanisms for solving difficult real-world problems. Unfortunately, domains traditionally used in general problem-solving research lack important characteristics of real-world domains, making it difficult to apply the techniques developed. Most classic AI domains require satisfying a set of Boolean constraints. Real-world problems require finding a solution that meets a set of Boolean constraints and performs well on a set of real-valued constraints. In addition, most classic domains are static while domains from the real world change. In this paper we demonstrate that SteppingStone, a general learning problem solver, is capable of solving problems with these characteristics. SteppingStone heuristically decomposes a problem into simpler subproblems, and then learns to deal with the interactions that arise between the subproblems. In lieu of an agreed upon metric for problem difficulty, we choose significant problems that are difficult for both people and programs as good candidates for evaluating progress. Consequently we adopt the domain of logic synthesis from VLSI design to demonstrate SteppingStone’s capabilities.

From Problem-Solving Paradigm to Co-Ontogenic Drift: How Do Learning Narratives Self-Generate?

Leonardo ◽  
2011 ◽  
Vol 44 (2) ◽  
pp. 133-138
Author(s):  
Johann van der Merwe ◽  
Julia Brewis
Keyword(s):  
Problem Solving ◽  
Real World ◽  
Design Education ◽  
Design Theory ◽  
Social Situation ◽  
Theory And Practice ◽  
Use Of Self ◽  
Real World Problems

It is now an accepted maxim in design theory and practice that real-world problems needing the attention of design practitioners are not neat and well-structured, but ill-structured and “wicked”—part of a larger, complex social situation. For design education, then, to take its lead from contemporary social, political and economic structures, it will have to seriously re-think its problem-solving paradigms. The authors investigate the use of self-generating learning narratives in the classroom and contrast the approach they introduce with the still-too-prevalent notion that knowledge can be transferred from teacher to student. Their methodology draws from ideas formulated by Maturana and Varela on autopoiesis, specifically the notion of co-ontogenic drift.

Rule Generation Methods Based on Logic Synthesis

2011 ◽  
pp. 978-983
Author(s):  
Marco Muselli
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Real World ◽  
Inductive Reasoning ◽  
Logic Synthesis ◽  
Rule Generation ◽  
Research Areas ◽  
Real World Problem

One of the most relevant problems in artificial intelligence is allowing a synthetic device to perform inductive reasoning, i.e. to infer a set of rules consistent with a collection of data pertaining to a given real world problem. A variety of approaches, arising in different research areas such as statistics, machine learning, neural networks, etc., have been proposed during the last 50 years to deal with the problem of realizing inductive reasoning.

scholarly journals The International 
General Game Playing Competition

AI Magazine ◽  
2013 ◽  
Vol 34 (2) ◽  
pp. 107 ◽  
Author(s):  
Michael Genesereth ◽  
Yngvi Björnsson
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
General Problem ◽  
Game Playing ◽  
Test Bed ◽  
General Game Playing ◽  
Research Challenges ◽  
History Of ◽  
World Class ◽  
General Game

Games have played a prominent role as a test-bed for advancements in the field of Artificial Intelligence ever since its foundation over half a century ago, resulting in highly specialized world-class game-playing systems being developed for various games. The establishment of the International General Game Playing Competition in 2005, however, resulted in a renewed interest in more general problem solving approaches to game playing. In general game playing (GGP) the goal is to create game-playing systems that autonomously learn how to skillfully play a wide variety of games, given only the descriptions of the game rules. In this paper we review the history of the competition, discuss progress made so far, and list outstanding research challenges.

Special issue HAIS 2015: Recent advancements in hybrid artificial intelligence systems and its application to real-world problems

2019 ◽  
Vol 326-327 ◽  
pp. 69-70
Author(s):  
Pablo García Bringas ◽  
Igor Santos ◽  
Enrique Onieva ◽  
Eneko Osaba ◽  
Héctor Quintián ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Special Issue ◽  
Hybrid Artificial Intelligence ◽  
Artificial Intelligence Systems ◽  
Real World Problems

What Is in the Daily News? Problem-Solving Opportunities!

1999 ◽  
Vol 5 (7) ◽  
pp. 390-394
Author(s):  
Robyn Silbey
Keyword(s):  
Problem Solving ◽  
Real World ◽  
Mathematics Curriculum ◽  
School Mathematics ◽  
Daily News ◽  
Evaluation Standards ◽  
Mathematical Ideas ◽  
The Everyday ◽  
Everyday World ◽  
Real World Problems

In An Agenda for Action, the NCTM asserted that problem solving must be at the heart of school mathematics (1980). Almost ten years later, the NCTM's Curriculum and Evaluation Standards for School Mathematics (1989) stated that the development of each student's ability to solve problems is essential if he or she is to be a productive citizen. The Standards assumed that the mathematics curriculum would emphasize applications of mathematics. If mathematics is to be viewed as a practical, useful subject, students must understand that it can be applied to various real-world problems, since most mathematical ideas arise from the everyday world. Furthermore, the mathematics curriculum should include a broad range of content and an interrelation of that content.

Design an Intelligent System to automatically Tutor the Method for Solving Problems

2020 ◽  
Vol 12 (7) ◽  
Author(s):  
Hien D. Nguyen ◽  
◽  
Dung A. Tran ◽  
Huan P. Do ◽  
Vuong T. Pham ◽  
...  
Keyword(s):  
Knowledge Base ◽  
Public Administration ◽  
Real World ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Intelligent Tutoring ◽  
Problem Solver ◽  
High School Mathematics ◽  
Knowledge Model ◽  
Real World Problems

Nowadays, intelligent systems have been applied in many real-word domains. The Intelligent chatbot is an intelligent system, it can interact with the human to tutor how to work some activities. In this work, we design an architecture to build an intelligent chatbot, which can tutor to solve problems, and construct scripts for automatically tutoring. The knowledge base of the intelligent tutoring chatbot is designed by using the requirements of an Intelligent Problem Solver. It is the combination between the knowledge model of relations and operators, and the structures of hint questions and sample problems, which are practical cases. Based on the knowledge base and tutoring scripts, a tutoring engine is designed. The tutoring chatbot plays as an instructor for solving real-world problems. It simulates the working of the instructor to tutor the user for solving problems. By utilizing the knowledge base and reasoning, the architecture of the intelligent chatbot are emerging to apply in the real-world. It is used to build an intelligent chatbot to support the learning of high-school mathematics and a consultant system in public administration. The experimental results show the effectiveness of the proposed method in comparison with the existing systems.

Is Participation in the Volunteer Income Tax Assistance (VITA) Program Associated with Students' Problem-Solving Skills and Professional Commitment?

2015 ◽  
Vol 31 (1) ◽  
pp. 71-90 ◽  
Author(s):  
Anne L. Christensen ◽  
Angela M. Woodland
Keyword(s):  
Problem Solving ◽  
Experiential Learning ◽  
Real World ◽  
Income Tax ◽  
Learning By Doing ◽  
Accounting Education ◽  
Accounting Students ◽  
Professional Commitment ◽  
Problem Solving Skills ◽  
Real World Problems

ABSTRACT The Accounting Education Change Commission (AECC 1990, 309) states accounting students “should identify and solve unstructured problems that require the use of multiple information sources. Learning by doing should be emphasized.” The Pathways Commission (2012) also emphasizes the importance of exposing students to complex, real-world problems. Volunteer Income Tax Assistance (VITA) participation is an experiential learning opportunity with real-world problems and real clients in a professional setting. Using survey data obtained from students at seven U.S. universities, we test whether students who participate in VITA programs have greater professionalism as measured by problem-solving skills and professional commitment. Our results generally indicate participation in VITA programs is positively and significantly associated with problem-solving skills, but not with commitment to the profession. We do not find strong evidence that the association between VITA participation and problem solving differs significantly between traditional (age 25 and under) and nontraditional students (over age 25) or that the association differs significantly for students who intend to pursue tax careers and those who do not. Our study contributes to the extant literature on the effectiveness of experiential learning, to our understanding of attributes of professionalism in students, and to the specific benefits of the VITA program.

Swarm Intelligence for Electromagnetic Problem Solving

2017 ◽  
pp. 69-100 ◽  
Author(s):  
Luciano Mescia ◽  
Pietro Bia ◽  
Diego Caratelli ◽  
Johan Gielis
Keyword(s):  
Stochastic Process ◽  
Sensitivity Analysis ◽  
Problem Solving ◽  
Swarm Intelligence ◽  
Real World ◽  
Computational Burden ◽  
Electromagnetic Problems ◽  
Definition Of ◽  
Quantum Pso ◽  
Real World Problems

The chapter will describe the potential of the swarm intelligence and in particular quantum PSO-based algorithm, to solve complicated electromagnetic problems. This task is accomplished through addressing the design and analysis challenges of some key real-world problems. A detailed definition of the conventional PSO and its quantum-inspired version are presented and compared in terms of accuracy and computational burden. Some theoretical discussions concerning the convergence issues and a sensitivity analysis on the parameters influencing the stochastic process are reported.

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