A Spreading Activation Approach for e-Commerce Site Selection System

This paper presents a dynamic associative network model for e-commerce site selection system based on the psycholinguistic theories of human memory; Spreading Activation Network (SAN).This paper presents a dynamic associative network model for e-commerce site selection system based on the psycholinguistic theories of human memory; Spreading Activation Network (SAN). The system is designed to give personalized suggestions based on the user’s current personal preferences, other user’s common preferences, web-link structure, and activation rules. This work employs a SAN as a technique to provide the evaluation and selection mechanism that provides multiple parallel processes for perception by representing dynamic associations among web-links, user activities, and the relevance subjects of the websites. The system attempts to evaluate a number of sites in an unpredictable complex dynamic environment. Spreading activation explains the predictive top-down effect of knowledge. These processes select the group of the most suitable websites (context) in response to the current conditions (e-commerce activities) while the system continues working towards the user objective goal

A Dynamic Associative e-learning model based on a spreading activation network

This paper proposes a dynamic semantic model for e-learning system based on the psycholinguistic theories of human memory, Spreading Activation Network (SAN). This work employs a SAN as a technique to provide the interface's action selection mechanism in an uncertain environment.Presenting information to an e-learning environment is a challenge, mostly, because of the hypertext/hypermedia nature and the richness of the context and information provides. This paper proposes a dynamic semantic model for e-learning system based on the psycholinguistic theories of human memory, Spreading Activation Network (SAN). This work employs a SAN as a technique to provide the interface's action selection mechanism in an uncertain environment. The paper combines the SAN with the temporal logic to provide an e-learning system that a learning activity level evolves according to their expected contextual relevance. The system differs from the other e-learning by representing dynamic associations between learning activities and the relevance subjects. This system equipped with an Event-Triggered learning interface (context) adaptation component. This component provides multiple parallel processes for perception. These processes provide context screen selection and learning task operation based upon the user current situation. The SAN attempts to achieve a number of goals in an unpredictable complex dynamic environment. Spreading activation explains the predictive top-down effect of knowledge. It supports general heuristics which may be used as the first step of more elaborated methods. This model is suited to deal with the interaction between semantic and episodic memories, as well as many other practical issues regarding e-learning, including the retroactive effect of semantics over perception. The system uses the SAN to activate the most suitable interface screen (context) in response to the current conditions (learning activities) while the system continues working towards the learning objective goal. The paper presents our efforts to realize such e-learning system. The proposed paradigm has been implemented to develop a prototypical system, and the experiments also illustrate the robustness of such an e-learning framework

An agent-based architecture for an adaptive human-robot interface

This paper describes an innovative agent-based architecture for mixed-initiative interaction between a human and a robot that interacts via a graphical user interface (GUI). Mixed-initiative interaction typically refers to a flexible interaction strategy between a human and a computer to contribute what is best-suited at the most appropriate time [1]. In this paper, we extend this concept to human-robot interaction (HRI). When compared to pure humancomputer interaction, HRIs encounter additional difficulty, as the user must assess the situation at the robot’s remote location via limited sensory feedback. We propose an agent-based adaptive human-robot interface for mixed-initiative interaction to address this challenge. The proposed adaptive user interface (UI) architecture provides a platform for developing various agents that control robots and user interface components (UICs). Such components permit the human and the robot to communicate missionrelevant information. 1