Expect the unexpected: the co-construction of assistive artifacts

This paper aims to explain emerging design activities within community-based rehabilitation contexts through the science of self-organization and adaptivity. It applies an evolutionary systematic worldview (Heylighen, 2011) to frame spontaneous collaboration between different local agents which produce self-made assistive artifacts. Through a process of distinction creation and distinction destruction occupational therapist, professional non-designers, caregivers and disabled people co-evolve simultaneously towards novel possibilities which embody a contemporary state of fitness. The conversation language is build on the principles of emotional seeding through stigmergic prototyping and have been practically applied as a form of design hacking which blends design time and use time. Within this process of co-construction the thought experiment of Maxwell’s Demon is used to map perceived behavior and steer the selecting process of following user-product adaptation strategies. This practice-based approach is illustrated through a case study and tries to integrate both rationality and intuition within emerging participatory design activities

Toward a social psychophysics of face communication

As a highly social species, humans are equipped with a powerful tool for social communication—the face, which can elicit multiple social perceptions in others due to the rich and complex variations of its movements, morphology, and complexion. Consequently, identifying precisely what face information elicits different social perceptions is a complex empirical challenge that has largely remained beyond the reach of traditional research methods. More recently, the emerging field of social psychophysics has developed new methods designed to address this challenge. Here, we introduce and review the foundational methodological developments of social psychophysics, present recent work that has advanced our understanding of the face as a tool for social communication, and discuss the main challenges that lie ahead

Emotion Recognition from Acted and Spontaneous Speech

Dizertační práce se zabývá rozpoznáním emočního stavu mluvčích z řečového signálu. Práce je rozdělena do dvou hlavních častí, první část popisuju navržené metody pro rozpoznání emočního stavu z hraných databází. V rámci této části jsou představeny výsledky rozpoznání použitím dvou různých databází s různými jazyky. Hlavními přínosy této části je detailní analýza rozsáhlé škály různých příznaků získaných z řečového signálu, návrh nových klasifikačních architektur jako je například „emoční párování“ a návrh nové metody pro mapování diskrétních emočních stavů do dvou dimenzionálního prostoru. Druhá část se zabývá rozpoznáním emočních stavů z databáze spontánní řeči, která byla získána ze záznamů hovorů z reálných call center. Poznatky z analýzy a návrhu metod rozpoznání z hrané řeči byly využity pro návrh nového systému pro rozpoznání sedmi spontánních emočních stavů. Jádrem navrženého přístupu je komplexní klasifikační architektura založena na fúzi různých systémů. Práce se dále zabývá vlivem emočního stavu mluvčího na úspěšnosti rozpoznání pohlaví a návrhem systému pro automatickou detekci úspěšných hovorů v call centrech na základě analýzy parametrů dialogu mezi účastníky telefonních hovorů.Doctoral thesis deals with emotion recognition from speech signals. The thesis is divided into two main parts; the first part describes proposed approaches for emotion recognition using two different multilingual databases of acted emotional speech. The main contributions of this part are detailed analysis of a big set of acoustic features, new classification schemes for vocal emotion recognition such as “emotion coupling” and new method for mapping discrete emotions into two-dimensional space. The second part of this thesis is devoted to emotion recognition using multilingual databases of spontaneous emotional speech, which is based on telephone records obtained from real call centers. The knowledge gained from experiments with emotion recognition from acted speech was exploited to design a new approach for classifying seven emotional states. The core of the proposed approach is a complex classification architecture based on the fusion of different systems. The thesis also examines the influence of speaker’s emotional state on gender recognition performance and proposes system for automatic identification of successful phone calls in call center by means of dialogue features.

Schelling, von Neumann, and the Event that Didn’t Occur

Thomas Schelling was recognized by the Nobel Prize committee as a pioneer in the application of game theory and rational choice analysis to problems of politics and international relations. However, although he makes frequent references in his writings to this approach, his main explorations and insights depend upon and require acknowledgment of its limitations. One of his principal concerns was how a country could engage in successful deterrence. If the behavioral assumptions that commonly underpin game theory are taken seriously and applied consistently, however, nuclear adversaries are almost certain to engage in devastating conflict, as John von Neumann forcefully asserted. The history of the last half century falsified von Neumann’s prediction, and the “event that didn’t occur” formed the subject of Schelling’s Nobel lecture. The answer to the question “why?” is the central concern of this paper

Learning to Teach Reinforcement Learning Agents

In this article we study the transfer learning model of action advice under a budget. We focus on reinforcement learning teachers providing action advice to heterogeneous students playing the game of Pac-Man under a limited advice budget. First, we examine several critical factors affecting advice quality in this setting, such as the average performance of the teacher, its variance and the importance of reward discounting in advising. The experiments show the non-trivial importance of the coefficient of variation (CV) as a statistic for choosing policies that generate advice. The CV statistic relates variance to the corresponding mean. Second, the article studies policy learning for distributing advice under a budget. Whereas most methods in the relevant literature rely on heuristics for advice distribution we formulate the problem as a learning one and propose a novel RL algorithm capable of learning when to advise, adapting to the student and the task at hand. Furthermore, we argue that learning to advise under a budget is an instance of a more generic learning problem: Constrained Exploitation Reinforcement Learning

An information theory based behavioral model for agent-based crowd simulations

Crowds must be simulated believable in terms of their appearance and behavior to improve a virtual environment’s realism. Due to the complex nature of human behavior, realistic behavior of agents in crowd simulations is still a challenging problem. In this paper, we propose a novel behavioral model which builds analytical maps to control agents’ behavior adaptively with agent-crowd interaction formulations. We introduce information theoretical concepts to construct analytical maps automatically. Our model can be integrated into crowd simulators and enhance their behavioral complexity. We made comparative analyses of the presented behavior model with measured crowd data and two agent-based crowd simulators

Monetary Policy Transparency in the UK:The Impact of Independence and Inflation Targeting

There is a widespread belief that the transparency of UK monetary policy has increased substantially as a result of the introduction of inflation targeting in 1992 and a number of procedural and institutional reforms which accompanied and followed it. In this paper, we use money market responses (and other data) to test the possibility that improved anticipation of policy moves may be the result of developments other than the institutional reforms popularly cited. We find overwhelming evidence that the switch to inflation targeting itself significantly reduced monetary policy surprises, while subsequent reforms have contributed little. Where we advance substantially on earlier work is to look at the cross-sectional dispersion of agents’ anticipation. If the benefit of transparency is the elimination of policy surprise, there is little benefit if the averagely correct anticipations of agents conceal a wide dispersion of view. The most striking feature is the general decline in cross-sectional one year-ahead forecast uncertainty of the interbank rate. So, even though we do not find that agents on average have improved monetary policy anticipation since 1997, we do find that they have become more unanimous about forecasting future money market rates. However, further testing reveals that it is a simultaneous fall in the dispersion of inflation rate forecasts that explains the increased consensus on interest rates, rather than institutional reforms in 1997 and later.Monetary Policy; transparency; independence; inflation targeting