Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

Data Mining Practical Machine Learning Tools and Techniques

The power of feedback.

Emotion and Adaptation

In this paper we describe a demonstration of Traveller, an intercultural training tool for young adults between 18 and 25 years old. In Traveller, users travel to different countries and encounter ‘strange’ characters that show different behaviours depending on their synthetic culture. By interacting with these characters, users learn to deal with misunderstandings due to differences in conflicts.

http://www.fdg2013.org/program/posters/poster02_degens_etal.pdf

Traveller – Interacting with agents to deal with misunderstandings due to culture

Emergence of Emotion-Like Signals in Learning Agents

In this work, we address the problem of symmetry transfer in human-robot collaborative tasks, i.e., how certain actions can be extended to their symmetrical by exploiting symmetries in their execution. We contribute an approach capable of considering the symmetry inherent to a given task, such as the human or robot’s lateral symmetry, abstracting them from the robot’s decision process. We instantiate our approach in a robot-assisted backpack dressing scenario. A two-manipulator Baxter robot assists a human user in sequentially putting on both straps of a backpack. We evaluate the proposed symmetry-transfer approach in two complementary perspectives: the quality of the agent’s learned policy in a simulated environment and the efficiency of the complete system in a real-life scenario with a robotic platform. The results show that our approach allows the extension of the execution of single-side trained collaborative tasks to their symmetrical with no additional training and minimal performance loss.

Exploiting Symmetry in Human Robot-Assisted Dressing Using Reinforcement Learning

Using Storytelling to Teach Children Biodiversity

Several agent-based frameworks have been proposed to investigate the possible reasons that lead humans to act in the interest of others while giving up individual gains. In this paper we propose a novel framework for analyzing this phenomenon based on the notions of social importance and local discrimination. We propose a “favors game”, where a recipient agent can “claim” a favor to a donor agent, which may in turn “confer” its request at the expense of a certain cost. The proposed framework allows us to study the conditions under which cooperation occurs and the dynamics of the relationships formed within a population.

Ana Paiva

Papers

Traveller – Interacting with agents to deal with misunderstandings due to culture

Emergence of Emotion-Like Signals in Learning Agents

Exploiting Symmetry in Human Robot-Assisted Dressing Using Reinforcement Learning

Using Storytelling to Teach Children Biodiversity

The "Favors Game": A Framework to Study the Emergence of Cooperation through Social Importance (Extended Abstract)