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

Leveraging hierarchy in multimodal generative models for effective cross-modality inference

This paper discusses the concept of "influence", as an alternative to "direct" control of game characters, describing how influence can be achieved in computer games. To illustrate the notion of influence we will briefly present a game called FantasyA where players interact with it by influencing the emotions of their semi-autonomous avatars using a tangible interface called SenToy. We show how "influence" was built into this game, the role of SenToy as an influencing device, and the reactions of the users to this type of control.

From Motion Control to Emotion Influence: Controlling Autonomous Synthetic Characters in a Computer Game

This research was supported by Fundac¸ ˜ao para a Ciˆencia e
Tecnologia (FCT) through grants SFRH/BD/94736/2013,
PTDC/EEI-SII/5081/2014, PTDC/MAT/STA/3358/2014 and by
multi-annual funding of CBMA and INESC-ID (under the projects
UID/BIA/04050/2013 and UID/CEC/50021/2013) provided by FCT.

Execution errors enable the evolution of fairness in the ultimatum game

From March 23 2014 to March 28, the seminar "Computational Models of Cultural Behavior for Human-Agent Interaction" held in Schloss Dagstuhl -- Leibniz Center for Informatics. During the seminar, an interdisciplinary group of researchers explored and discussed theories and techniques for computational models of culture as part of virtual human simulations. Culturally-sensitive agents do not only improve the acceptance of man-machine interfaces by adapting their verbal and non-verbal behavior to the user's assumed cultural background. They also bear enormous potential for a rapidly growing number of ICT-based language and cultural training scenarios that make use of role-play with virtual characters. The seminar brought together researchers with an interdisciplinary background that profited from each other's perspective and explored challenges for the future.

https://drops.dagstuhl.de/opus/volltexte/2014/4594/pdf/dagrep_v004_i003_p103_s14131.pdf/

Computational Models of Cultural Behavior for Human-Agent Interaction (Dagstuhl Seminar 14131)

Modeling synthetic characters which interact with objects in dynamic virtual worlds is important when we want the agents to act in an autonomous and non-preplanned way. Such interactions with objects would allow the synthetic characters to behave in a more believable way. Once objects offer innumerous uses, it is essential that the agent is able to acquire the necessary knowledge to identify action possibilities in the objects while interacting with them. We propose a conceptual framework that allows the agents to identify possible interactions with objects based in past experiences with other objects. Starting from sensory patterns collected during interactions with objects, the agent is able to acquire conceptual knowledge about regularities of the world, its internal states and its own actions. The presented work also proposes that such acquired knowledge may be used by the agent in order to satisfy its needs and goals by interacting with objects. Preliminary tests were made and it is possible to state that our agents are able to acquire valid conceptual knowledge about the regularities in the environment and its objects, its own actions and causal relations between them.

/pdf/learning-to-interact-connecting-perception-with-action-in-1k7slx7qbz.pdf

Ana Paiva

Papers

Leveraging hierarchy in multimodal generative models for effective cross-modality inference

From Motion Control to Emotion Influence: Controlling Autonomous Synthetic Characters in a Computer Game

Execution errors enable the evolution of fairness in the ultimatum game

Computational Models of Cultural Behavior for Human-Agent Interaction (Dagstuhl Seminar 14131)

Learning to interact: connecting perception with action in virtual environments