Moving Up the Information Food Chain: Deploying Softbots on the World Wide Web

I view the World Wide Web as an information food chain (figure 1). The maze of pages and hyperlinks that comprise the Web are at the very bottom of the chain. The WebCrawlers and Alta Vistas of the world are information herbivores; they graze on Web pages and regurgitate them as searchable indices. Today, most Web users feed near the bottom of the information food chain, but the time is ripe to move up. Since 1991, we have been building information carnivores, which intelligently hunt and feast on herbivore

Negotiating the Maze: Case based, Collaborative Distance Learning in Dentistry

The module was developed as an elective to give motivated senior dental students an opportunity to expand their horizons in planning oral rehabilitation. It comprised one tutor and 12 students, from five universities world-wide, communicating on the World Wide Web (WWW), to develop oral rehabilitation plans for simulated patients. Trigger material came from one of two Case Profiles and consisted of diagnostic casts and details of the clinical and radiographic examination in WWW/CD-ROM form. No background material was supplied as to the "patient's" age, sex, history or main concern(s). Students worked in groups of three, each student from a different location. Individual students were given a role within the group: "Patient", who developed a "personal background" belonging to the trigger examination material, "Academic" who identified state-of-the-art treatment options available for the dental treatment needs identified by the group and "General Practitioner" who tailored these options to the "patient's" needs and wants. Student feedback focused on their perception of their experience with the program in response to a questionnaire comprising 11 structured and four "open" questions. All students felt that the program increased their confidence in planning oral rehabilitation. Ten students felt that the "best thing about the program" was the interaction with students from other universities and the exposure to different philosophies from the different schools. Eight students mentioned their increased awareness of the importance of patient input into holistic planning. Under the heading "What was the worst thing", students cited some technical hitches and the snowball effect of two sluggish students who were not identified early enough and thus impacted negatively on the working of their groups. Student feedback showed that the module succeeded in its aims but needed modification to improve the logistics of working with an extended campu

The Effects of Finger-Walking in Place (FWIP) on Spatial Knowledge Acquisition in Virtual Environments

Spatial knowledge, necessary for efficient navigation, comprises route knowledge (memory of landmarks along a route) and survey knowledge (overall representation like a map). Virtual environments (VEs) have been suggested as a power tool for understanding some issues associated with human navigation, such as spatial knowledge acquisition. The Finger-Walking-in-Place (FWIP) interaction technique is a locomotion technique for navigation tasks in immersive virtual environments (IVEs). The FWIP was designed to map a human’s embodied ability overlearned by natural walking for navigation, to finger-based interaction technique. Its implementation on Lemur and iPhone/iPod Touch devices was evaluated in our previous studies. In this paper, we present a comparative study of the joystick’s flying technique versus the FWIP. Our experiment results show that the FWIP results in better performance than the joystick’s flying for route knowledge acquisition in our maze navigation tasks

The Dreaming Variational Autoencoder for Reinforcement Learning Environments

Reinforcement learning has shown great potential in generalizing over raw sensory data using only a single neural network for value optimization. There are several challenges in the current state-of-the-art reinforcement learning algorithms that prevent them from converging towards the global optima. It is likely that the solution to these problems lies in short- and long-term planning, exploration and memory management for reinforcement learning algorithms. Games are often used to benchmark reinforcement learning algorithms as they provide a flexible, reproducible, and easy to control environment. Regardless, few games feature a state-space where results in exploration, memory, and planning are easily perceived. This paper presents The Dreaming Variational Autoencoder (DVAE), a neural network based generative modeling architecture for exploration in environments with sparse feedback. We further present Deep Maze, a novel and flexible maze engine that challenges DVAE in partial and fully-observable state-spaces, long-horizon tasks, and deterministic and stochastic problems. We show initial findings and encourage further work in reinforcement learning driven by generative exploration.Comment: Best Student Paper Award, Proceedings of the 38th SGAI International Conference on Artificial Intelligence, Cambridge, UK, 2018, Artificial Intelligence XXXV, 201

Topological Navigation of Simulated Robots using Occupancy Grid

Formerly I presented a metric navigation method in the Webots mobile robot simulator. The navigating Khepera-like robot builds an occupancy grid of the environment and explores the square-shaped room around with a value iteration algorithm. Now I created a topological navigation procedure based on the occupancy grid process. The extension by a skeletonization algorithm results a graph of important places and the connecting routes among them. I also show the significant time profit gained during the process