Dynamic user profiles for web personalisation

Web personalisation systems are used to enhance the user experience by providing tailor-made services based on the user’s interests and preferences which are typically stored in user profiles. For such systems to remain effective, the profiles need to be able to adapt and reflect the users’ changing behaviour. In this paper, we introduce a set of methods designed to capture and track user interests and maintain dynamic user profiles within a personalisation system. User interests are represented as ontological concepts which are constructed by mapping web pages visited by a user to a reference ontology and are subsequently used to learn short-term and long-term interests. A multi-agent system facilitates and coordinates the capture, storage, management and adaptation of user interests. We propose a search system that utilises our dynamic user profile to provide a personalised search experience. We present a series of experiments that show how our system can effectively model a dynamic user profile and is capable of learning and adapting to different user browsing behaviours

Multi-advisor deep reinforcement learning for smart home energy control

Effective automated smart home control is essential for smart-grid enabled approaches to demand response, named in the literature as automated demand response. At it’s heart, this is a multi-objective adaptive control problem because it requires balancing an appliance’s primary objective with demandresponse motivated objectives. This control problem is difficult due to the scale and heterogeneity of appliances as well as the time-varying nature of both dynamics and consumer preferences. Computational considerations further limit the types of acceptable algorithms to apply to the problem. We propose approaching the problem under the multi-objective reinforcement learning framework. We suggest a multi-agent multi-advisor reinforcement learning system to handle the consumer’s time-varying preferences across objectives. We design some simulations to produce preliminary results on the nature of user preferences and the feasibility of multi-advisor reinforcement learning. Further smarthome simulations are designed to demonstrate the linear scalability of the algorithm with respect to both number of agents and number of objectives. We demonstrate the algorithms performance in simulation against a comparable centrallized and decentrallized controller. Finally, we identify the need for stronger performance measures for a system of this type by considering the effect on agents of newly selected preferences

An agent-based adaptive e-content and e-learning architecture design and implementation

Individual students have different approaches towards learning because of different background knowledge, learning styles and preferences. Therefore, it is difficult for instructors to understand their student best learning approach. Furthermore, web application based on multi-agents for adaptive E-Content has been proposed to assist student individualized learning content in order to enhance their learning outcome.Existing systems normally utilize the main techniques of programming scripts and hierarchical course structure to support adaptive Electronic-Learning (E-Learning) course authoring for diverse category of students. These systems need instructor to obligate significant technical skills, and additionally to employ theories of learning styles, which are challenging requirements. To facilitate instructor to contribute in authoring adaptive E-Learning courses, we have designed web application architecture for administrator, assessor/instructor, and student. Three agents namely the exam agent, message agent and E-Content agent have been created to assist instructor and student. We designed the proposed architecture to be implemented for an online adaptive E-Content and E-Learning system. In addition, we conducted user studies to evaluate the effectiveness of the system

Building Ethically Bounded AI

The more AI agents are deployed in scenarios with possibly unexpected situations, the more they need to be flexible, adaptive, and creative in achieving the goal we have given them. Thus, a certain level of freedom to choose the best path to the goal is inherent in making AI robust and flexible enough. At the same time, however, the pervasive deployment of AI in our life, whether AI is autonomous or collaborating with humans, raises several ethical challenges. AI agents should be aware and follow appropriate ethical principles and should thus exhibit properties such as fairness or other virtues. These ethical principles should define the boundaries of AI's freedom and creativity. However, it is still a challenge to understand how to specify and reason with ethical boundaries in AI agents and how to combine them appropriately with subjective preferences and goal specifications. Some initial attempts employ either a data-driven example-based approach for both, or a symbolic rule-based approach for both. We envision a modular approach where any AI technique can be used for any of these essential ingredients in decision making or decision support systems, paired with a contextual approach to define their combination and relative weight. In a world where neither humans nor AI systems work in isolation, but are tightly interconnected, e.g., the Internet of Things, we also envision a compositional approach to building ethically bounded AI, where the ethical properties of each component can be fruitfully exploited to derive those of the overall system. In this paper we define and motivate the notion of ethically-bounded AI, we describe two concrete examples, and we outline some outstanding challenges.Comment: Published at AAAI Blue Sky Track, winner of Blue Sky Awar

An agent system to support student teams working online

Online learning is now a reality, with distributed learning and blended learning becoming more widely used in Higher Education. Novel ways in which undergraduate and postgraduate learning material can be presented are being developed, and methods for helping students to learn online are needed, especially if we require them to collaborate with each other on learning activities. Agents to provide a supporting role for students have evolved from Artificial Intelligence research, and their strength lies in their ease of operation over networks as well as their ability to act in response to stimuli. In this paper an application of a software agent is described, aimed at supporting students working on team projects in the online learning environment. Online teamwork is problematical for a number of reasons, such as getting acquainted with team members, finding out about other team members’ abilities, agreeing who should do which tasks, communications between team members and keeping up to date with progress that has been made on the project. Software agents have the ability to monitor progress and to offer advice by operating in the background, acting autonomously when the need arises. An agent prototype has been developed in Prolog to perform a limited set of functions to support students. Team projects have a planning, doing and completing stage, all of which require them to have some sort of agent support. This agent at present supports part of the planning stage, by prompting the students to input their likes, dislikes and abilities for a selection of task areas defined for the project. The agent then allocates the various tasks to the students according to predetermined rules. The results of a trial carried out using teams working on projects, on campus, indicate that students like the idea of using this agent to help with allocating tasks. They also agreed that agent support of this type would probably be helpful to both students working on team projects with face to face contact, as well as for teams working solely online. Work is ongoing to add more functionality to the agent and to evaluate the agent more widely