9329231
Idea Storming Cube: A Game-based System to Support Creative Thinking
This paper describes a collaborative game-based creativity support system, idea storming cube, in support of creative thinking. It aims to make people form a creative and perspective-shift thinking habit. The system acquires knowledge from domain expert, user inputs history, and individuals in the current brainstorming group, and then provides user-, goal- and context-sensitive supports. Comparing to classic tutoring systems, it focuses more on stimulating divergent thinking. The system can be put into the basic mode or the idea generation mode in order to support different gaming objectives. A case study for preliminary evaluation of the proposed HCI tool for collaborative idea generation is also reported in this paper.
5809000
VIBRANT: A Brainstorming Agent for Computer Supported Creative Problem Solving
This paper describes key issues underlying the design of a tutoring system that brainstorms with students in order to support qualitative problem solving. Cognitively oriented and socially oriented support are enabled by two technologies, namely heuristic-based feedback generation and community-data-driven social recommendation. Formal representations and corresponding automated reasoning procedures for these technologies are introduced.
12981432
A web-based tutoring system with styles-matching strategy for spatial geometric transformation
It has been a major objective for researchers to develop computer systems that can effectively deliver instruction to learners. Therefore, how to incorporate instructional strategies in computer-assisted learning systems in a systematic manner deserves further investigation. In this paper, a style-matching strategy that attempts to match learning materials' styles to learners' latent traits is proposed and realized in a web-based tutoring system, called CooTutor. The mechanism of adaptive material selection takes learners' different spatial ability and learning styles as an integral learning profile into account, and performs traits-based personalization of learning experience. This system is specifically designed to conquer the difficulty of tutoring the topic on fundamental spatial geometry in conventional curriculums. By conducting empirical evaluation with a small group of students, it is found that CooTutor is generally beneficial to learning the domain, but the effect of the styles-matching mechanism remains inconclusive. The work aims to contribute to the community of adaptive hypermedia in providing an explorative example adopting the concern of individual difference for personalization. The system design, a usage scenario, and an exploratory evaluation are presented in this paper as implications for further studies.
10547222
A Hybrid Ontology Directed Feedback Selection Algorithm for Supporting Creative Problem Solving Dialogues
We evaluate a new hybrid language processing approach designed for interactive applications that maintain an interaction with users over multiple turns. Specifically, we describe a method for using a simple topic hierarchy in combination with a standard information retrieval measure of semantic similarity to reason about the selection of appropriate feedback in response to extended language inputs in the context of an interactive tutorial system designed to support creative problem solving. Our evaluation demonstrates the value of using a machine learning approach that takes feedback from experts into account for optimizing the hierarchy based feedback selection strategy.
14482795
Collaborative multi-agents for workflow management
Workflow management helps organizations to organize their work on different activities in such a way that the predictable underlying processes are carried out effectively and efficiently in a distributed environment. We describe a business workflow management system with intelligent multi-agent support in a collaborative organization environment. With the help from multi-agent collaboration, our workflow management system can organize the organization's work processes and provide resource control, data mediation, semantic mapping, and knowledge visualization processing. Such systems are able to manage the organizational workflow more effectively and more efficiently.
16403613
Gradient Adaptive Image Restoration and Enhancement
Various methods have been proposed for image enhancement and restoration. The main difficulty is how to enhance the structures uniformly while suppressing the noise without artifacts. In this paper, we tackle this problem in the gradient domain instead of the traditional intensity domain. By enhancing the gradient field, we can enhance the structure uniformly without overshooting at the boundary. Because the gradient field is very sensitive to noise, we apply an orientation-isotropy adaptive filter to the gradient field, suppressing the gradients in the noise regions while enhancing along the object boundaries. Thus we obtain a modulated gradient field, which is usually not integrable. We reconstruct the enhanced image from the modulated gradient field with least square errors by solving a Poisson equation. This method can enhance the object contrast uniformly, suppress the noise with no artifacts, and avoid setting stopping time as in PDE methods. Experiments on noisy images show the efficacy of our method.
8726769
A Prototypes-Embedded Genetic K-means Algorithm
This paper presents a genetic algorithm (GA) for K-means clustering. Instead of the widely applied string-of-group-numbers encoding, we encode the prototypes of the clusters into the chromosomes. The crossover operator is designed to exchange prototypes between two chromosomes. The one-step K-means algorithm is used as the mutation operator. Hence, the proposed GA is called the prototypes-embedded genetic K-means algorithm (PGKA). With the inherent evolution process of evolutionary algorithms, PGKA has superior performance than the classical K-means algorithm, while comparing to other GA-based approaches, PGKA is more efficient and suitable for large scale data sets
4473346
Multi-objective hierarchical genetic algorithm for interpretable fuzzy rule-based knowledge extraction
A new scheme based on multi-objective hierarchical genetic algorithm (MOHGA) is proposed to extract interpretable rule-based knowledge from data. The approach is derived from the use of multiple objective genetic algorithm (MOGA), where the genes of the chromosome are arranged into control genes and parameter genes. These genes are in a hierarchical form so that the control genes can manipulate the parameter genes in a more effective manner. The effectiveness of this chromosome formulation enables the fuzzy sets and rules to be optimally reduced. Some important concepts about the interpretability are introduced and the fitness function in the MOGA will consider both the accuracy and interpretability of the fuzzy model. In order to remove the redundancy of the rule base proactively, we further apply an interpretability-driven simplification method to newborn individuals. In our approach, we first apply the fuzzy clustering to generate an initial rule-based model. Then the multi-objective hierarchical genetic algorithm and the recursive least square method are used to obtain the optimized fuzzy models. The accuracy and the interpretability of fuzzy models derived by this approach are studied and presented in this paper. We compare our work with other methods reported in the literature on four examples: a synthetic nonlinear dynamic system, a nonlinear static system, the Lorenz system and the Mackey-Glass system. Simulation results show that the proposed approach is effective and practical in knowledge extraction.
7446737
A web-service agent-based decision support system for securities exception management
Abstract With rising trading volumes and increasing risks in securities transactions, the securities industry is making an effort to shorten the trade lifecycle and minimize transaction risks. While attempting to achieve this, exception management is crucial to pass trade information within the trade lifecycle in a timely and accurate fashion. For a competitive solution to exception management, a web-service-agent-based decision support system is developed in this paper. Agent technology is applied to deal with the dynamic, complex, and distributed processes in exception management; web services techniques are proposed for more scalability and interoperability in network-based business environment. By integrating agent technology with web services to make use of the advantages from both, this approach leads to more intelligence, flexibility and collaboration in business exception management. The effectiveness of this approach is evaluated through a use case and demonstration feedback.