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

Can social agents be assertive and persuade users? To what extent do the persuasion abilities of robots depend on the users' own traits? In this paper, we describe the results of a study in which participants interacted with robotic Non-Player Characters (NPC) displaying different levels of assertiveness (high and low), in a storytelling scenario. We sought to understand how the level of assertiveness displayed by the robots impacted the participants' decision-making process and game experience. Our results suggest that NPCs displaying lower levels of assertiveness evoke more positive emotional responses but are not more effective at influencing players' decisions when compared to NPCs displaying higher levels of this trait. However, NPCs displaying a personality trait are more effective persuaders than NPCs not displaying this feature. Overall, this paper highlights the importance of considering the player's personality and its relation to task-specific attributes during the process of game design.

https://dl.acm.org/doi/pdf/10.1145/3311350.3347162

The Role of Assertiveness in a Storytelling Game with Persuasive Robotic Non-Player Characters

Future human-robot interactions will have to consider different human traits. One human feature that may be affected by the presence of virtual agents or robots is human honesty. Will people try to take advantage in the presence of a robot/virtual agent? Some previous studies have shown that the physical presence of a robot can decrease cheating in humans. In this paper, we investigated if merely a simple video of a robot looking at the user was enough to affect human's cheating behavior. Further, we also investigated if the Honesty-Humility personality trait predicted cheating. We conducted a study with 160 participants that were randomly allocated to one of two conditions: (1) performing the task with a video of a robot looking at them, or (2) doing the task alone. Results showed that being alone or with a video of a robot produced equal levels of cheating and the Honesty-Humility dimension predicted cheating, particularly the fairness sub-domain was responsible for predicting cheating behavior. This study has implications for future scenarios where dishonesty might be tempting, and physical presence of an observer might not be possible.

The effect of a robotic agent on dishonest behavior

Understanding the behavioral dynamics that underline human-robot interactions in groups remains one of the core challenges in social robotics research. However, despite a growing interest in this topic, there is still a lack of established and validated measures that allow researchers to analyze human-robot interactions in group scenarios; and very few that have been developed and tested specifically for research conducted in-the-wild. This is a problem because it hinders the development of general models of human-robot interaction, and makes the comprehension of the inner workings of the relational dynamics between humans and robots, in group contexts, significantly more difficult. In this paper, we aim to provide a reflection on the current state of research on human-robot interaction in small groups, as well as to outline directions for future research with an emphasis on methodological and transversal issues.

Human-robot interaction in groups: Methodological and research practices

Today, many interactive games and virtual communities engage several users and intelligent virtual agents (IVAs) all interacting in the same virtual environment, which additionally, may present collaborative tasks to the participants. The success of the interactions relies on the ability of the agents to meet the user's expectations, thus, showing a coherent and believable set of behaviours. For this reason, in scenarios where users and IVAs interact as a group, it is very important that the interactions follow a believable group dynamics. Focusing on this problem, we have developed a model that supports the dynamics of a group of IVAs, inspired by theories of group dynamics developed in human social psychological sciences. The dynamics is driven by a characterization of the different types of interactions that may occur in the group. The model was implemented in a computer game that engage the user with a group of four IVAs in the resolution of collaborative tasks. This game was used in an evaluation experiment which showed that the model had a positive effect on the users' social engagement in the group, namely on their trust and identification with the group.

/pdf/intelligent-virtual-agents-in-collaborative-scenarios-57jdfoki90.pdf

Intelligent virtual agents in collaborative scenarios

The way gaze cues are used in social interactions is by no means irrelevant because they are fundamentally important for understanding social interactions. In this paper, we argue that social conflict is a form of relating and that gaze clues are critical to understanding the underlying cognitive processes in this phenomenon. To learn more about conflict, we created an experimental setting that reduces real life to a mixed-motive game. We analyse the gaze patterns of 22 10- to 12-year-old children in specific game moments that could have been conductive to conflict. Our aim is to understand how subtle forms of conflict unfold, by analysing micro-level behaviours and establishing a link to high-level psychological constructs. Their gazes show that children are being more competitive or cooperative at different stages of the game. Children tend to avoid confrontation by averting face-directed gazes when they are asking for larger profits, and they gaze longer to attempt to persuade the other child.

/pdf/looking-for-conflict-gaze-dynamics-in-a-dyadic-mixed-motive-4i0qyp9scg.pdf

Ana Paiva

Papers

The Role of Assertiveness in a Storytelling Game with Persuasive Robotic Non-Player Characters

The effect of a robotic agent on dishonest behavior

Human-robot interaction in groups: Methodological and research practices

Intelligent virtual agents in collaborative scenarios

Looking for Conflict: Gaze Dynamics in a Dyadic Mixed-Motive Game