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

As the field of HRI evolves, it is important to understand how users interact with robots over long periods. This paper reviews the current research on long-term interaction between users and social robots. We describe the main features of these robots and highlight the main findings of the existing long-term studies. We also present a set of directions for future research and discuss some open issues that should be addressed in this field.

Social Robots for Long-Term Interaction: A Survey

We discuss the experience of constructing the application FearNot! (Fun with Empathic Agents Reaching Novel Outcomes in Teaching), an application of virtual drama to anti-bullying education inspired by Forum Theatre. An appraisal-driven agent architecture is presented as a mechanism for generating an emergent, that is, unscripted, narrative. A small-scale evaluation is discussed and the lessons learned are described.

Fearnot!: an experiment in emergent narrative

Interactive virtual environments (IVEs) are now seen as an engaging new way by which children learn experimental sciences and other disciplines. These environments are populated by synthetic characters that guide and stimulate the children activities. In order to build such environments, one needs to address the problem of how achieve believable and empathic characters that act autonomously. Inspired by the work of traditional character animators, this paper proposes an architectural model to build autonomous characters where the agent’s reasoning and behaviour is influenced by its emotional state and personality. We performed a small case evaluation in order to determine if the characters evoked empathic reactions in the users with positive results.

https://gaips.inesc-id.pt/component/gaips/publications/showPublicationPdf?pid=297&format=raw

Feeling and reasoning: a computational model for emotional characters

The design of an affect recognition system for socially perceptive robots relies on representative data: human-robot interaction in naturalistic settings requires an affect recognition system to be trained and validated with contextualised affective expressions, that is, expressions that emerge in the same interaction scenario of the target application. In this paper we propose an initial computational model to automatically analyse human postures and body motion to detect engagement of children playing chess with an iCat robot that acts as a game companion. Our approach is based on vision-based automatic extraction of expressive postural features from videos capturing the behaviour of the children from a lateral view. An initial evaluation, conducted by training several recognition models with contextualised affective postural expressions, suggests that patterns of postural behaviour can be used to accurately predict the engagement of the children with the robot, thus making our approach suitable for integration into an affect recognition system for a game companion in a real world scenario.

Automatic analysis of affective postures and body motion to detect engagement with a game companion

This paper presents a generic and flexible architecture for emotional agents, with what we consider to be the minimum set of functionalities that allows us to implement and compare different appraisal theories in a given scenario. FAtiMA Modular, the architecture proposed is composed of a core algorithm and by a set of components that add particular functionality (either in terms of appraisal or behaviour) to the architecture, which makes the architecture more flexible and easier to extend.

Ana Paiva

Papers

Social Robots for Long-Term Interaction: A Survey

Fearnot!: an experiment in emergent narrative

Feeling and reasoning: a computational model for emotional characters

Automatic analysis of affective postures and body motion to detect engagement with a game companion

FAtiMA Modular: Towards an Agent Architecture with a Generic Appraisal Framework