•Journal•ISSN: 1687-7470
Advances in Artificial Intelligence
Hindawi Publishing Corporation
About: Advances in Artificial Intelligence is an academic journal. The journal publishes majorly in the area(s): Artificial neural network & Cognition. It has an ISSN identifier of 1687-7470. It is also open access. Over the lifetime, 135 publications have been published receiving 4515 citations.
Papers
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TL;DR: From basic techniques to the state-of-the-art, this paper attempts to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.
Abstract: As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenges, such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, privacy protection, etc., and their possible solutions. We then present three main categories of CF techniques: memory-based, modelbased, and hybrid CF algorithms (that combine CF with other recommendation techniques), with examples for representative algorithms of each category, and analysis of their predictive performance and their ability to address the challenges. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.
3,406 citations
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TL;DR: The MOISE+ model as discussed by the authors is a MAS model that considers structural, functional, and deontic aspects of an agent's organization from a structural and functional point of view.
Abstract: A Multiagent System (MAS) that explicitly represents its organization normally focuses either on the functioning or the structure of this organization. However, addressing both aspects is a prolific approach when one wants to design or describe a MAS organization. The problem is to define these aspects in such a way that they can be both assembled in a single coherent specification. The MOISE+ model - described here through a soccer team example - intends to be a step in this direction since the organization is seen under three points of view: structural, functional, and deontic.
191 citations
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TL;DR: This work proposes a genetically based PID controller tuned with fixed PID parameters that tends to operate the CSTR process in its entire operating range to overcome the limitations of the linear PID controller.
Abstract: Genetic algorithm (GA) based PID (proportional integral derivative) controller has been proposed for tuning optimized PID parameters in a continuous stirred tank reactor (CSTR) process using a weighted combination of objective functions, namely, integral square error (ISE), integral absolute error (IAE), and integrated time absolute error (ITAE). Optimization of PID controller parameters is the key goal in chemical and biochemical industries. PID controllers have narrowed down the operating range of processes with dynamic nonlinearity. In our proposed work, globally optimized PID parameters tend to operate the CSTR process in its entire operating range to overcome the limitations of the linear PID controller. The simulation study reveals that the GA based PID controller tuned with fixed PID parameters provides satisfactory performance in terms of set point tracking and disturbance rejection.
113 citations
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TL;DR: SocialCollab is developed, a novel neighbour-based collaborative filtering algorithm to predict, for a given user, other users they may like to contact, based on user similarity in terms of both attractiveness and taste, which goes beyond traditional, merely taste-based, collaborative filtering for item selection.
Abstract: Predicting people other people may like has recently become
an important task in many online social networks. Traditional collaborative
filtering approaches are popular in recommender systems to effectively
predict user preferences for items. However, in online social
networks people have a dual role as both "users" and "items", e.g., both
initiating and receiving contacts. Here the assumption of active users and
passive items in traditional collaborative filtering is inapplicable. In this
paper we propose a model that fully captures the bilateral role of user
interactions within a social network and formulate collaborative filtering
methods to enable people to people recommendation. In this model
users can be similar to other users in two ways – either having similar
"taste" for the users they contact, or having similar "attractiveness" for
the users who contact them.We develop SocialCollab, a novel neighbourbased
collaborative filtering algorithm to predict, for a given user, other
users they may like to contact, based on user similarity in terms of both
attractiveness and taste. In social networks this goes beyond traditional,
merely taste-based, collaborative filtering for item selection. Evaluation
of the proposed recommender system on datasets from a commercial
online social network show improvements over traditional collaborative
filtering.
99 citations
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TL;DR: The proposed scheme has been used to present and design a general multicriteria software assistant (SA) that can consider the user, operator, and/or the QoS view points and results show that the proposed scheme and SA have better and more robust performance over the random-based selection.
Abstract: In the next generation of heterogeneous wireless networks (HWNs), a large number of different radio access technologies (RATs) will be integrated into a common network. In this type of networks, selecting the most optimal and promising access network (AN) is an important consideration for overall networks stability, resource utilization, user satisfaction, and quality of service (QoS) provisioning. This paper proposes a general scheme to solve the access network selection (ANS) problem in the HWN. The proposed scheme has been used to present and design a general multicriteria software assistant (SA) that can consider the user, operator, and/or the QoS view points. Combined fuzzy logic (FL) and genetic algorithms (GAs) have been used to give the proposed scheme the required scalability, flexibility, and simplicity. The simulation results show that the proposed scheme and SA have better and more robust performance over the random-based selection.
98 citations