An Introduction to MultiAgent Systems.

The seminal work on Affective Computing in 1995 by Picard set the base for computing that relates to, arises from, or influences emotions. Affective computing is a multidisciplinary field of research spanning the areas of computer science, psychology, and cognitive science. Potential applications include automated driver assistance, healthcare, human-computer interaction, entertainment, marketing, teaching and many others. Thus, quickly, the field acquired high interest, with an enormous growth of the number of papers published on the topic since its inception. This paper aims to (1) Present an introduction to the field of affective computing though the description of key theoretical concepts; (2) Describe the current state-of-the-art of emotion recognition, tracing the developments that helped foster the growth of the field; and lastly, (3) point the literature take-home messages and conclusions, evidencing the main challenges and future opportunities that lie ahead, in particular for the development of novel machine learning (ML) algorithms in the context of emotion recognition using physiological signals.

/pdf/a-review-current-challenges-and-future-possibilities-on-365dzvqva1.pdf

A Review, Current Challenges, and Future Possibilities on Emotion Recognition Using Machine Learning and Physiological Signals

From social butterfly to engaged citizen: urban informatics, social media, ubiquitous computing, and mobile technology to support citizen engagement

Design research is important for understanding and interrogating how emerging technologies shape human experience. However, design research with Machine Learning (ML) is relatively underdeveloped. Crucially, designers have not found a grasp on ML uncertainty as a design opportunity rather than an obstacle. The technical literature points to data and model uncertainties as two main properties of ML. Through post-phenomenology, we position uncertainty as one defining material attribute of ML processes which mediate human experience. To understand ML uncertainty as a design material, we investigate four design research case studies involving ML. We derive three provocative concepts: thingly uncertainty: ML-driven artefacts have uncertain, variable relations to their environments; pattern leakage: ML uncertainty can lead to patterns shaping the world they are meant to represent; and futures creep: ML technologies texture human relations to time with uncertainty. Finally, we outline design research trajectories and sketch a post-phenomenological approach to human-ML relations.

Machine Learning Uncertainty as a Design Material: A Post-Phenomenological Inquiry

https://dl.acm.org/doi/pdf/10.1145/3411764.3445481

There is an increasing interest in analyzing, extracting, and representing human movements in terms of a set of spatial, temporal, and qualitative characteristics for applications such as human-computer interactions and sports and health movement analysis. Information visualization techniques can be used to help people better understand the contents of movements. While all the characteristics of movement may not always be visible or detectable by humans, visualizations can illustrate detailed information about the characteristics of the movement. We present the prototype of an interactive movement analytics framework, called Mova, for feature extraction, feature visualization, and analysis of human movement data. Integrated with a library of feature extraction methods, this platform can be used to anaylze movement qualities and investigate the relationships between its characteristics. In addition, Mova can be used to develop and validate new feature extraction methods with the help of parallel visualization of multiple features. We discuss test-cases in which Mova can be used and detail the road-map for its further development. Link to the platform: http://www.sfu.ca/~oalemi/mova

Mova: Interactive Movement Analytics Platform

The expressivity of virtual, animated agents plays an important role in their believability. While the planning and goal-oriented aspects of agent movements have been addressed in the literature extensively, expressing the emotional state of the agents in their movements is an open research problem. We present our interactive animated agent model with controllable affective movements. We have recorded a corpus of affect-expressive motion capture data of two actors, performing various movements, and annotated based on their arousal and valence levels. We train a Factored, Conditional Restricted Boltzmann Machine (FCRBM) with this corpus in order to capture and control the valence and arousal qualities of movement patterns. The agents are then able to control the emotional qualities of their movements through the FCRBM for any given combination of the valence and arousal. Our results show that the model is capable of controlling the arousal level of the synthesized movements, and to some extent their valence, through manually defining the level of valence and arousal of the agent, as well as making transitions from one state to the other. We validate the expressive abilities of the model through conducting an experiment where participants were asked to rate their perceived affective state for both the generated and recorded movements.

Affect-expressive movement generation with factored conditional Restricted Boltzmann Machines

Morse Things are Internet connected ceramic cups and bowls that communicate with each other in Morse Code This ongoing research iteratively asks thing-centered questions of things and technology A premise of the Morse Things is that any understanding of a thing or technology is unstable and arguably fragile In this pictorial, we reflect on how we as researchers, in the course of doing research with the Morse Things, unexpectedly found ourselves literally entangled in the conceptual and physical fragility of the research This pictorial describes our growing awareness of this instability, beginning with a kintsugi repair of a broken Morse Thing, our false confidence in our package design for shipping, and the difficulties to conceptualize the machine learning world that we ourselves created We reflect on these experiences, and now see these as reminders of the inevitable fragility and instability of research on thing-perspectives

Fragile! Handle with Care: The Morse Things

Embodied interaction has the potential to provide users with uniquely engaging and meaningful experiences. m+m: Movement + Meaning middleware is an open source software framework that enables users to construct real-time, interactive systems that are based on movement data. The acquisition, processing, and rendering of movement data can be local or distributed, real-time or off-line. Key features of the m+m middleware are a small footprint in terms of computational resources, portability between different platforms, and high performance in terms of reduced latency and increased bandwidth. Examples of systems that can be built with m+m as the internal communication middleware include those for the semantic interpretation of human movement data, machine-learning models for movement recognition, and the mapping of movement data as a controller for online navigation, collaboration, and distributed performance.

/pdf/m-m-a-novel-middleware-for-distributed-movement-based-6f4q07vhmd.pdf

m+m: A novel Middleware for Distributed, Movement based Interactive Multimedia Systems

This paper proposes and explores an interaction protocol for incorporating helpful behavior into agent teamwork. In the proposed Mutual Assistance Protocol (MAP), an agent can directly assist a teammate who requests help, provided that the two agents jointly determine, based on their individual beliefs, that the expected outcome of the help act is in the interest of the team. This distributed decision is reached through a bidding sequence similar to the one in the Contract Net Protocol. The deliberation about help is approximate in that each agent only assesses the team impact of the change to its own individual plan. The paper introduces two versions of the protocol: Action MAP, in which the helper performs an action within a teammate's individual plan, and Resource MAP, in which one or more helpers provide resources to a teammate. Both versions include refinements for the handling of simultaneous help requests. A cooperative game simulation demonstrates the advantages of Action MAP over action help protocols that use unilateral decision mechanisms, and over teamwork scenarios without help. The experiments show how the team performance depends on: the teammates' mutual awareness of each other's abilities, dynamic disturbance in the environment, communication costs, and computation costs.

Omid Alemi

Papers

Mova: Interactive Movement Analytics Platform

Affect-expressive movement generation with factored conditional Restricted Boltzmann Machines

Fragile! Handle with Care: The Morse Things

m+m: A novel Middleware for Distributed, Movement based Interactive Multimedia Systems

An Interaction Protocol for Mutual Assistance in Agent Teamwork