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

Current approaches to the integration of action and music in Intelligent Virtual Environments are in most part, “hardwired” to fit a particular interaction scheme. This lack of independence between both parts prevents the music or sound director from exploring new musical possibilities in a systematic and focused way.

Mediating Action and Music with Augmented Grammars

Challenge Discussion on Socially Interactive Agents: Considerations on Social Interaction, Computational Architectures, Evaluation, and Ethics

In this article we describe a general cognitive model of human social behavior that is meant to increase the social intelligence of autonomous intelligent agents in different contexts. Despite the remarkable improvements that have been made on human-agent interaction, agents still have a limited capacity to be aware of the social reality that is present in the human mind and significantly guides human behavior. The model discussed in this paper is a step toward increasing that capacity significantly. Two different case studies are described in which the proposed model is used to better explain and predict human behavior. The first case study is the well known Ultimatum game. The second one is a variation of the “Game of Nines” played by children.

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A Model of Social Dynamics for Social Intelligent Agents

In this chapter, we discuss the relation between anticipation and believability. We start by introducing the concept of believability and the importance of both emotion and anticipation in the generation of believable behaviour. Then, we present some related work examplifying how emotions and anticipation have been used in the field of synthetic characters, with a particular focus on anticipation. Afterwards, we present how the relation between anticipation and emotion has been researched by the authors to create the emotivector, an anticipatory mechanism aimed at assisting the generation of autonomous believable behaviour for synthetic characters. As a fusion between the fields of affective computing and anticipatory computing, the emotivector generates affective signals from the mismatch between predicted and sensed values, and is inspired by the psychology of emotion and attention. We present two applications of the emotivector, demonstrating its adequacy: one in the realtime control of a situated, embodied agent inhabiting a virtual world, and another, in the real-time control of the affective expressions of a robotic affective chess `buddy'. Finally, we describe a successful integration of the low-level emotivector mechanism in a high-level cognitive agent architecture and the benefits of such integration.

Anticipation and Believability

With new advancements in interaction techniques, character animation also requires new methods, to support fields such as robotics, and VR/AR. Interactive characters in such fields are becoming driven by AI which opens up the possibility of non-linear and open-ended narratives that may even include interaction with the real, physical world. This paper presents and describes ERIK, an expressive inverse kinematics technique aimed at such applications. Our technique allows an arbitrary kinematic chain, such as an arm, snake, or robotic manipulator, to exhibit an expressive posture while aiming its end-point towards a given target orientation. The technique runs in interactive-time and does not require any pre-processing step such as e.g. training in machine learning techniques, in order to support new embodiments or new postures. That allows it to be integrated in an artist-friendly workflow, bringing artists closer to the development of such AI-driven expressive characters, by allowing them to use their typical animation tools of choice, and to properly pre-visualize the animation during design-time, even on a real robot. The full algorithmic specification is presented and described so that it can be implemented and used throughout the communities of the various fields we address. We demonstrate ERIK on different virtual kinematic structures, and also on a low-fidelity robot that was crafted using wood and hobby-grade servos, to show how well the technique performs even on a low-grade robot. Our evaluation shows how well the technique performs, i.e., how well the character is able to point at the target orientation, while minimally disrupting its target expressive posture, and respecting its mechanical rotation limits.

Ana Paiva

Papers

Mediating Action and Music with Augmented Grammars

Challenge Discussion on Socially Interactive Agents: Considerations on Social Interaction, Computational Architectures, Evaluation, and Ethics

A Model of Social Dynamics for Social Intelligent Agents

Anticipation and Believability

Expressive Inverse Kinematics Solving in Real-time for Virtual and Robotic Interactive Characters