This Thursday 12:45 , March the 10th, I will be presenting my recent experiments on Coarse Preferences at room 4.31/4.33 in the Informatics Forum, University of Edinburgh. You can find the full list of speakers and abstracts below. (original post)
|When||Mar 10, 2016
from 12:45 PM to 01:30 PM
|Add event to calendar|| vCal
Speaker: Pavlos Andreadis
Talk title: Efficient Utility Elicitation using a Model of Coarse Preferences
Abstract: Preference elicitation is the process of learning a decision maker’s model of choice under non-strategic uncertainty. Starting with the hypothesis that the hypothesis space of real human choice makers admits varying levels of coarseness, we wish to construct learning and inference algorithms that can achieve preference elicitation more quickly and efficiently than with techniques that do not model this. A key technical issue is to identify the user’s own representation of the solution space, which we approach by reasoning about possible partitions over the original outcome space. We argue that learning a model in this way and making inferences using beliefs over the user’s individual representation of the solution space can yield significant benefits in performance, as measured for instance by the number of queries needed to make recommendations. I will present an instantiation of this methodology, including learning a model of coarse preferences through decision tree based representations. I will also present preliminary results on a synthetic data set from a ride-sharing scenario, and with real human choice data in the Sushi Dataset, showing that preference elicitation using our model incorporating coarseness is faster and no less accurate than the baseline which ignores this structure.
Speaker: Adam Erskine
Talk Title: Parameter selection in Particle Swarm optimisation
Abstract: The particle swarm optimisation (PSO) algorithm is a popular metaheuristic used to solve search and optimisation type problems. Originally inspired by bird flocking it is simple to implement and continues to be used and studied. It requires parameter tuning to obtain good solutions to a given problem. The parameters control the balance between exploration and exploitation of a problem space. Analysis of PSO as a random dynamical system predicts that there exists a locus of parameter values that lead to optimum performance. Here, we outline this approach and provide supporting empirical evidence.
Speaker: Tom Stone
Talk Title: Skyline-based Localisation for Aggressively Manoeuvring Robots using UV sensors and Spherical Harmonics
Abstract: Place recognition is a key capability for navigating robots. While significant advances have been achieved on large, stable platforms such as robot cars, achieving robust performance on rapidly manoeuvring platforms in outdoor natural conditions remains a challenge, with few systems able to deal with both variable conditions and large tilt variations caused by rough terrain. Taking inspiration from biology, we propose a novel combination of sensory modality and image processing to obtain a significant improvement in the robustness of sequence-based image matching for place recognition. We use a UV-sensitive fisheye lens camera to segment sky from ground, providing illumination invariance, and encode the resulting binary images using spherical harmonics to enable rotation-invariant image matching. In combination, these methods also produce substantial pitch and roll invariance, as the spherical harmonics for the sky shape are minimally affected, providing the sky remains visible.
Our system demonstrated an improved condition- and tilt-invariance, enabling robust place recognition during aggressive zigzag manoeuvring along bumpy trails and at tilt angles of up to 60 degrees. I will present our methods and also the resulting performance compared to a leading appearance-invariant technique (SeqSLAM) and a leading viewpoint-invariant technique (FAB-MAP 2.0) on three new outdoor datasets encompassing variable robot heading, tilt, and lighting conditions in both forested and urban environments.