In multiagent environments, forms of social learning such as teaching and imitation have been shown to aid the transfer of knowledge from experts to learners in reinforcement learning (RL). We recast the problem of imitation in a Bayesian framework. Our Bayesian imitation model allows a learner to smoothly pool prior knowledge, data obtained through interaction with the environment, and informa...

In order to represent cooperative multi-agents who must reason with uncertain knowledge, a coherent framework is necessary. We choose multiply sectioned Bayesian networks (MSBNs) as the basis for this study because they are based on well established theory on Bayesian networks and because they are modular. In this paper, we focus on the semantics of a MSBN-based multi-agent system (MAS) for coo...

Hedge Fund Activism is a proactive investment strategy to earn better returns in stock price through involving the corporate governance of target firms . Annual profits generated from this approach are far better than that from the traditional “buy and wait” approach. Due to this reason, more and more hedge fund companies are coming to engage in this business. However, this approach involves co...

We compare the performance of Inverse Reinforcement Learning (IRL) with the relative new model of Multi-agent Inverse Reinforcement Learning (MIRL). Before comparing the methods, we extend a published Bayesian IRL approach that is only applicable to the case where the reward is only state dependent to a general one capable of tackling the case where the reward depends on both state and action. ...

abstract background: a computer aided diagnosis system was established using the wavelet transform and neural network to differentiate malignant from benign in a   group of patients with histo-pathologically proved breast lesions based on the data derived independ­ently from time-intensity profile.   materials and methods: the per­formance of the artificial neural network (ann) was evaluated us...

Multiply sectioned Bayesian networks (MSBNs) are extension of Bayesian networks for flexible modeling and cooperative multiagent probabilistic inference. A large and complex equipment is modeled by a set of Bayesian subnets in a MSBN each of which corresponds to a natural component of the equipment. Each subnet forms the core knowledge of an autonomous M&D agent. Inference is performed in a dis...

The Internet of Things has a set smart objects with connectivity that assists in monitoring real world environment during emergency situations. It could monitor the various applications situations such as road accidents, criminal acts including physical assaults, kidnap cases, and other threats to people’s way  life. In this work, proposed work is afford time services users through Convolutiona...

Organizations expose to financial risk that can lead to bankruptcy and loss of business is increased nowadays. This may leads to discontinuity in operations, increased legal fees, administrative costs and other indirect costs. Accordingly, the purpose of this study was to predict the financial crisis of Tehran Stock Exchange using neural network and genetic algorithm. This research is descripti...

natural fire inflicting irreparable damage to rangelands and forest areas is cause changes in landscape ecology. the purpose of this research is comparison of artificial neural network (ann) and line regression (lr) models to predict of forest and rangelands fires to this end, the data consist fire burned area and fire were used weather data over a period of 7 years (2006-2012(.the result indic...

The forecast of fluctuations and prices is the major concern in financial markets. Thus, developing an accurate and robust forecasting decision model is critically favorable to the investors. As gold has shown a special capability to smooth inflation fluctuations, governors use gold as a price controlling lever. Thus, more information about future gold price trends will help to make the firm de...