61,303 research outputs found
Incorporating stakeholders’ knowledge in group decision-making
International audienc
Aggregation procedure based on majority principle for collective identification of firm’s crucial knowledge
It is very important to identify, preserve, and transfer knowledge to those who need it within firm. However, the identification of knowledge and especially tacit knowledge is a complex process because knowledge cannot be measured quantitatively. In this paper we present an approach for inducing a set of collective decision rules representing a generalized description of the preferential information of a group of decision makers involved in a multicriteria classification problem to identify crucial knowledge to be capitalized
Multi-agent knowledge integration mechanism using particle swarm optimization
This is the post-print version of the final paper published in Technological Forecasting and Social Change. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2011 Elsevier B.V.Unstructured group decision-making is burdened with several central difficulties: unifying the knowledge of multiple experts in an unbiased manner and computational inefficiencies. In addition, a proper means of storing such unified knowledge for later use has not yet been established. Storage difficulties stem from of the integration of the logic underlying multiple experts' decision-making processes and the structured quantification of the impact of each opinion on the final product. To address these difficulties, this paper proposes a novel approach called the multiple agent-based knowledge integration mechanism (MAKIM), in which a fuzzy cognitive map (FCM) is used as a knowledge representation and storage vehicle. In this approach, we use particle swarm optimization (PSO) to adjust causal relationships and causality coefficients from the perspective of global optimization. Once an optimized FCM is constructed an agent based model (ABM) is applied to the inference of the FCM to solve real world problem. The final aggregate knowledge is stored in FCM form and is used to produce proper inference results for other target problems. To test the validity of our approach, we applied MAKIM to a real-world group decision-making problem, an IT project risk assessment, and found MAKIM to be statistically robust.Ministry of Education, Science and Technology (Korea
Learning to Look Around: Intelligently Exploring Unseen Environments for Unknown Tasks
It is common to implicitly assume access to intelligently captured inputs
(e.g., photos from a human photographer), yet autonomously capturing good
observations is itself a major challenge. We address the problem of learning to
look around: if a visual agent has the ability to voluntarily acquire new views
to observe its environment, how can it learn efficient exploratory behaviors to
acquire informative observations? We propose a reinforcement learning solution,
where the agent is rewarded for actions that reduce its uncertainty about the
unobserved portions of its environment. Based on this principle, we develop a
recurrent neural network-based approach to perform active completion of
panoramic natural scenes and 3D object shapes. Crucially, the learned policies
are not tied to any recognition task nor to the particular semantic content
seen during training. As a result, 1) the learned "look around" behavior is
relevant even for new tasks in unseen environments, and 2) training data
acquisition involves no manual labeling. Through tests in diverse settings, we
demonstrate that our approach learns useful generic policies that transfer to
new unseen tasks and environments. Completion episodes are shown at
https://goo.gl/BgWX3W
Towards a Reliable Framework of Uncertainty-Based Group Decision Support System
This study proposes a framework of Uncertainty-based Group Decision Support
System (UGDSS). It provides a platform for multiple criteria decision analysis
in six aspects including (1) decision environment, (2) decision problem, (3)
decision group, (4) decision conflict, (5) decision schemes and (6) group
negotiation. Based on multiple artificial intelligent technologies, this
framework provides reliable support for the comprehensive manipulation of
applications and advanced decision approaches through the design of an
integrated multi-agents architecture.Comment: Accepted paper in IEEE-ICDM2010; Print ISBN: 978-1-4244-9244-
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