Hend Al Tair

Hend Al Tair
Khalifa University | KU · Department of Electrical and Computer Engineering

About

6
Publications
665
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
34
Citations

Publications

Publications (6)
Article
In this work we present αPOMDP: a User-Adaptive Decision-Making technique for social robots. This technique is based on the classical POMDP formulation which we extend with novel aspects inspired by Reward Shaping and Model-Based Reinforcement Learning. Our technique innovates in two main ways: by applying a novel set of rewarding schemes based on...
Article
Despite the fact that robots have reached a high level of autonomy in recent years, the need for human presence in certain situations is still essential, especially in search and rescue operations. The human extends the robots capabilities beyond of what they are capable of with current technologies. While current robotic devices are able to naviga...
Article
Full-text available
This paper presents an architecture of a context- aware pro-active recommender system. The system uses contextual information in order to provide recommendations that are more suitable to the particular individual user. Reduction-based theory has been used in order to be able to use the contextual information besides the user and item components of...
Article
Full-text available
Recommender systems currently used in many applications, including tourism, tend to simply be reactive to user request. The recommender system proposed in this paper uses multi-agents and multi-dimensional contextual information to achieve proactive behavior. User profile and behavior get implicitly incorporated and subsequently updated in the syst...
Conference Paper
Full-text available
Integration between multi-agents systems and mobile services can lead to advanced creative applications on smart handsets. Communication and learning are some of the main features of agents. These features can be utilized to implement a smart system that can be proactive and adaptive to a user's context/-situation. This paper highlights a number of...
Article
Full-text available
This paper presents a multi-agent recommender system in which agents collaborate with each other to facilitate in providing travel recommendations. A set of rules are organized to be used as knowledge-base that enables travel agents to act in an intelligent and pro-active way. The agents are also in charge of building and updating profiles of trave...

Network

Cited By