Arman Didandeh

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Given a set of observations or new information, agents should be able to update their understandings of the world. As a part of any agents' world ontology, concepts need to evolve in time. In this paper we present a new representation for non-unanimous concepts based on the combination of feature-values and their probabilities. This representation leads us(More)
This article deals with the issue of concept learning and tries to have a game theoretic view over the process of cooperative concept learning among agents in a multi-agent system, in which an extreme sense of competition has arisen. This gives birth to a new realm labeled as ”Learning Games”. We study the cooperative view and give a novel idea(More)
Visual analytics (VA) combines the strengths of humans and computers such that joint cognitive systems are formed. To be effective, a VA tool should be designed such that the component parts of the whole system are strongly coupled and function in a harmonious fashion. These components include cognitive and perceptual issues, tasks, algorithms, data models,(More)
A critical issue of Neural Network based large-scale data mining algorithms is how to speed up their learning algorithm. This problem is particularly challenging for Error Back-Propagation (EBP) algorithm in Multi-Layered Perceptron (MLP) Neural Networks due to their significant applications in many scientific and engineering problems. In this paper, we(More)
Classification of some objects in classes of related concepts is an essential and even breathtaking task in many applications. A solution is discussed here based on Multi-Agent systems. A kernel of some expert agents in several classes is to consult a central agent decide among the classification problem of a certain object. This kernel is moderated with(More)
Traditionally, communication among agents has been established based on the group commitment to a common ontology which is unfortunately often too strong or unrealistic. In the real world of communicating agents, it is preferred to enable agents to exchange information while they keep their own individual ontology. While this assumption makes agents(More)
In this paper, we intend to have a game theoretic study on the concept learning problem in a multi-agent system. Concept learning is a very essential and well-studied domain of machine learning when it is studied under the characteristics of a multi-agent system. The most important reasons are the partiality of the environment perception for any agent and(More)
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