scholarly journals Reuse of constraint knowledge bases and problem solvers explored in engineering design

2014 ◽  
Vol 29 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Peter M.D. Gray ◽  
Trevor Runcie ◽  
Derek Sleeman
Keyword(s):  
Problem Solving ◽  
Parametric Design ◽  
Knowledge Engineering ◽  
Knowledge Bases ◽  
Problem Solver ◽  
Task Structure ◽  
Data Tables ◽  
Propagation Technique ◽  
Problem Solvers

AbstractReuse has long been a major goal of the knowledge engineering community. We present a case study of the reuse of constraint knowledge acquired for one problem solver, by two further problem solvers. For our analysis, we chose a well-known benchmark knowledge base (KB) system written in CLIPS, which was based on the propose and revise problem-solving method and which had a lift/elevator KB. The KB contained four components, including constraints and data tables, expressed in an ontology that reflects the propose and revise task structure. Sufficient trial data was extracted manually to demonstrate the approach on two alternative problem solvers: a spreadsheet (Excel) and a constraint logic solver (ECLiPSe). The next phase was to implement ExtrAKTor, which automated the process for the whole KB. Each KB that is processed results in a working system that is able to solve the corresponding configuration task (and not only for elevators). This is in contrast to earlier work, which produced abstract formulations of the problem-solving methods but which were unable to perform reuse of actual KBs. We subsequently used the ECLiPSe solver on some more demanding vertical transport configuration tasks. We found that we had to use a little-known propagation technique described by Le Provost and Wallace (1991). Further, our techniques did not use any heuristic “fix”’ information, yet we successfully dealt with a “thrashing” problem that had been a key issue in the original vertical transit work. Consequently, we believe we have developed a widely usable approach for solving this class of parametric design problem, by applying novel constraint-based problem solvers to data and formulae stored in existing KBs.

Speech2Mindmap: Testing the Accuracy of Unsupervised Automatic Mindmapping Technology with Speech Recognition

2021 ◽  
pp. 1-40
Author(s):  
Bumsoo Lee ◽  
Brian Feldman ◽  
Katherine Fu
Keyword(s):  
Speech Recognition ◽  
Problem Solving ◽  
Case Studies ◽  
Human Cognition ◽  
Virtual Collaboration ◽  
First Case ◽  
Human Creativity ◽  
Speech Data ◽  
Problem Solvers

Abstract This research aims to augment human cognition through the advancement and automation of mindmapping technologies, which could later support human creativity and virtual collaboration. Mindmapping is a visual brainstorming technique that allows problem solvers to utilize the human brain's ability to retrieve knowledge through similarity and association. While it is a powerful tool to generate concepts in any phase of problem-solving or design, the content of mindmaps is usually manually generated while listening or conversing and generating ideas, requiring a high cognitive load. This work introduces the development of a speech-driven automated mindmapping technology, called Speech2Mindmap. The specifics of the Speech2Mindmap algorithm are detailed, along with two case studies that serve to test its accuracy in comparison to human generated mindmaps, using audio recorded speech data as input. In the first case study, the Speech2Mindmap algorithm was evaluated on how well it represents manually generated human mindmapping output. The second case study evaluated the reliability of the Speech2Mindmap algorithm and examined the best performing methods and conditions to achieve the greatest similarity to human generated mindmaps. This research demonstrates that the Speech2Mindmap algorithm is capable of representing manually generated human mindmapping output, and found the best performing methods and conditions to generate a mindmap that is 80% similar, on average, to human generated mindmaps.

Thinking Aloud: Analysing Students' Mathematics Performance

1987 ◽  
Vol 8 (4) ◽  
pp. 233-244 ◽  
Author(s):  
Michael J. Lawson ◽  
Donald N. Rice
Keyword(s):  
Problem Solving ◽  
Mathematics Performance ◽  
Common Interest ◽  
Problem Solver ◽  
Instructional Setting ◽  
Thinking Aloud ◽  
Mathematical Content ◽  
Mathematics Problems ◽  
Major Factors ◽  
Problem Solvers

Psychologists, teachers and researchers have a common interest in understanding how students solve mathematics problems. We want, and need, to understand how solutions to problems are developed so that interactions with both successful and unsuccessful problem-solvers can become more effective. In order to build a more sophisticated understanding of problem-solving we must consider a number of major factors — the instructional setting, the nature of the problem-solver, the resources available, the structure of the mathematical content and the student's understanding of that, and the processes used in the solution. Also needed is a technique for identifying those processes in samples of students' mathematics performance. What we learn from using the technique influences the design of future instruction.

scholarly journals A Review on Intelligent Agent Systems

2011 ◽  
pp. 200-215
Author(s):  
Lokanath Sarangi ◽  
Dr. Chittaranjan Panda
Keyword(s):  
Problem Solving ◽  
Real Life ◽  
Transportation Systems ◽  
Performance Measure ◽  
Future Application ◽  
Problem Solver ◽  
Multi Agent System ◽  
Agent System ◽  
Multi Agent ◽  
Problem Solvers

Multi-agent system (MAS) is a common way of exploiting the potential power of agent by combining many agents in one system. Each agent in a multivalent system has incomplete information and is in capable of solving entire problem on its own. Multi-agent system offers modularity. If a problem domain is particularly complex, large and contain uncertainty, then the one way to address, it to develop a number of functional specific and modular agent that are specialized at solving various problems individually. It also consists of heterogeneous agents implemented by different tool and techniques. MAS can be defining as loosely coupled network of problem solvers that interact to solve problems that are beyond the individual capabilities or knowledge of each problem solver. These problem solvers, often ailed agent are autonomous and can be heterogeneous in nature. MAS is followed by characteristics, Future application, What to be change, problem solving agent, tools and techniques used, various architecture, multi agent applications and finally future Direction and conclusion. Various Characteristics are limited viewpoint, effectively, decentralized; computation is asynchronous, use of genetic algorithms. It has some drawbacks which must be change to make MAS more effective. In the session of problem solving of MAS, the agent performance measure contains many factors to improve it like formulation of problems, task allocation, organizations. In planning of multivalent this paper cover self-interested multivalent interactions, modeling of other agents, managing communication, effective allocation of limited resources to multiple agents with managing resources. Using of tool, to make the agent more efficient in task that are often used. The architecture o MAS followed by three layers, explore, wander, avoid obstacles respectively. Further different and task decomposition can yield various architecture like BDI (Belief Desire Intension), RETSINA. Various applications of multi agent system exist today, to solve the real-life problems, new systems are being developed two distinct categories and also many others like process control, telecommunication, air traffic control, transportation systems, commercial management, electronic commerce, entertainment applications, medical applications. The future aspect of MAS to solve problems that are too large, to allow interconnection and interoperation of multiple existing legacy systems etc.

HETEROGENEOUS DISTRIBUTED COOPERATIVE PROBLEM SOLVING SYSTEM HELIOS AND ITS COOPERATION MECHANISMS

1995 ◽  
Vol 04 (04) ◽  
pp. 369-385 ◽  
Author(s):  
AKIRA AIBA ◽  
KAZUMASA YOKOTA ◽  
HIROSHI TSUDA
Keyword(s):  
Problem Solving ◽  
Internal Representation ◽  
Problem Solver ◽  
Negotiation Strategy ◽  
Knowledge Representations ◽  
Negotiation Protocol ◽  
Cooperative Problem Solving ◽  
Implementation Model ◽  
Common Representation ◽  
Problem Solvers

For advanced and complicated knowledge processing, we need to integrate various kinds of problem-solvers such as constraint solvers, databases, and application programs. A heterogeneous distributed cooperative problem solving system HELIOS achieves this integration by introducing capsule and environment modules. To integrate heterogeneous problem-solvers that may be implemented in different languages and may have different knowledge representations, those heterogeneity should be absorbed. Capsules and environments are introduced into HELIOS for this purpose. A capsule surrounds each problem-solver and translates the contents of communication to and from the internal representation and a common representation. We call an encapsulated problem-solver an agent. An environment is a module which provides a field giving common representation, and agents communicate and cooperate with each other in each environment. Since an encapsulated environment with its agents can be considered as an agent, agent-environment structures can be nested in HELIOS. For negotiation between agents, negotiation protocol can be defined in each environment. A negotiation strategy that suits the given negotiation protocol can be defined in each capsule of an agent. In this framework, we define a transaction-based negotiation protocol. To check the validity of HELIOS design and its implementation model on computers connected by network, we implemented an experimental version of HELIOS on UNIX workstations.

scholarly journals A Selective Macro-learning Algorithm and its Application to the NxN Sliding-Tile Puzzle

1998 ◽  
Vol 8 ◽  
pp. 223-263 ◽  
Author(s):  
L. Finkelstein ◽  
S. Markovitch
Keyword(s):  
Problem Solving ◽  
Local Minimum ◽  
Learning Algorithm ◽  
Search Space ◽  
Problem Solver ◽  
Vast Number ◽  
Branching Factor ◽  
Problem Solvers ◽  
General Method ◽  
As If

One of the most common mechanisms used for speeding up problem solvers is macro-learning. Macros are sequences of basic operators acquired during problem solving. Macros are used by the problem solver as if they were basic operators. The major problem that macro-learning presents is the vast number of macros that are available for acquisition. Macros increase the branching factor of the search space and can severely degrade problem-solving efficiency. To make macro learning useful, a program must be selective in acquiring and utilizing macros. This paper describes a general method for selective acquisition of macros. Solvable training problems are generated in increasing order of difficulty. The only macros acquired are those that take the problem solver out of a local minimum to a better state. The utility of the method is demonstrated in several domains, including the domain of NxN sliding-tile puzzles. After learning on small puzzles, the system is able to efficiently solve puzzles of any size.

The Writing Process: A Strategy for Problem Solvers

1990 ◽  
Vol 38 (3) ◽  
pp. 35-38
Author(s):  
Margaret I. Ford
Keyword(s):  
Problem Solving ◽  
Word Problems ◽  
Writing Process ◽  
School Mathematics ◽  
Problem Solver ◽  
Educational Progress ◽  
National Assessment ◽  
The Past ◽  
Mathematics Educators ◽  
Problem Solvers

Over the past decade, mathematics educators have promoted problem solving as the goal of school mathematics. Yet in 1987, the National Assessment of Educational Progress revealed that our nation's schoolchildren are still falling short of our goals for their problem solving abilities. Many students dislike word problems in mathematics, and many teachers report feeling frustration and discouragement in helping their students learn how to solve such problems (Ford 1988). What can teachers do to improve students' attitude toward problem solving and to realize the goal of helping students become better problem solver?

scholarly journals Analyzing the Gender Strategy in Math: Good, Routine, or Naive Problem Solver?

2021 ◽  
Vol 9 (2) ◽  
pp. 126
Author(s):  
Suci Dwiyani ◽  
A.Wilda Indra Nanna ◽  
Dedi Kusnadi
Keyword(s):  
Problem Solving ◽  
Primary School ◽  
Female Student ◽  
Male Student ◽  
Problem Solver ◽  
High Achievement ◽  
Primary School Students ◽  
School Students ◽  
Male And Female ◽  
Problem Solvers

<p class="JRPMAbstrakKeywords"><span>Each student's problem solving has different characteristics and can be seen in terms of gender differences. Gender in mathematics achievement is still a hot topic to be researched. Therefore, this study aims to describe the problem solving of primary school students on Geometry using the Polya strategy model in terms of gender. This study uses a qualitative approach design. Participants in this study consisted of 4 high and moderate achievement primary school students of the male and female gender. Data collection providing by problem-solving test, and then we conducted interviews with participants as part of the data triangulation process. Based on the study results, we found that: (1) there are differences in problem-solving strategies carried out by male and female students. For high achievement, male participants are superior, while for moderate achievement, female participants are superior; (2) A male student with high achievement is a good problem solver. A female student with moderate achievement is routine problem-solver, and naive problem-solvers are a female student with high achievement and a male student with moderate achievement. (3) Good problem solver were confident and able to find other ways when encounters obstacles. Routine problem solver do not do the looking back process, so there are mistakes at some of the problem-solving stages, and naive problem solvers only manipulate the numbers on the problem into some calculation operations.</span></p>

scholarly journals Domesticating Homecare Services; Vehicle Route Problem Solver Displaced

2016 ◽  
Vol 4 (2) ◽  
pp. 41 ◽  
Author(s):  
Jenny Melind Bergschöld
Keyword(s):  
Financial Control ◽  
Problem Solver ◽  
Vehicle Route Problem ◽  
Vehicle Route ◽  
Route Problem ◽  
The Everyday ◽  
Everyday Work ◽  
Problem Solvers ◽  
Optimal Driving

<p>This article presents a case study of a vehicle route problem solver in the context of homecare work. Vehicle route problem solvers are technologies that calculate geographically rational driving routes. Primarily framed as tools for financial control, they have been tested in homecare services with good results under controlled circumstances. However, they have not been studied as part of users’ everyday work after implementation. The case study shows how, through processes of domestication, the vehicle route problem solver becomes unable to provide homecare workers with ‘optimal’ driving routes. Additionally, it shows how this ‘malfunction’ renders it understood as inconsequential to the very activities it was designed to support which ultimately leads to its removal from driving route production processes. The results highlight the importance of carefully studying how vehicle route problem solvers and other technologies interact with the everyday lives of those who are meant to benefit from them.</p>

Soft Skills among Students: A Case Study of UiTM Cawangan Kelantan

2019 ◽  
Vol 5 (1) ◽  
pp. 1
Author(s):  
Muhammad Faizal Samat ◽  
Norazlan Annual ◽  
Raznee Atisya Md Rashidi
Keyword(s):  
Problem Solving ◽  
Unemployment Rate ◽  
Significant Influence ◽  
Education System ◽  
Communication Skill ◽  
Soft Skills ◽  
Team Building ◽  
Leadership Skill ◽  
Quality In Education

This article contributes to ongoing debates about soft skills among students. In 2017, the unemployment rate in Malaysia was at 3.42 percent as compared to 2.85 percent in 2014. Education system must aim towards employability and ensure quality in education to reduce the percentage of unemployment. Thus, this study aims to investigate the development of soft skills among students through co-curriculum activities in UiTM Cawangan Kelantan. The sample were 113 students from UiTM Cawangan Kelantan. Questionnaires adapted from previous research to measure the communication skill, problem solving skill, team building skill, leadership skill and soft development of soft skills among students through co-curriculum activities. SEM-PLS 3.0 were employed in this study. The findings revealed only team building skill has significant influence on developments of soft skills among students through co-curriculum activities. However, the study indicates that communication skill, problem solving skill and leadership skill are not significant towards development of soft skills among students through cocurriculum activities.

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