Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-05-30T17:44:26Z No. of bitstreams: 1 anamaradeoliveirafigueiredo.pdf: 5190215 bytes, checksum: f9ec4e5f37ac1ca446fcef9ac91c1fb5 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-06-01T11:48:59Z (GMT) No. of bitstreams: 1 anamaradeoliveirafigueiredo.pdf: 5190215 bytes, checksum: f9ec4e5f37ac1ca446fcef9ac91c1fb5 (MD5) / Made available in DSpace on 2017-06-01T11:48:59Z (GMT). No. of bitstreams: 1 anamaradeoliveirafigueiredo.pdf: 5190215 bytes, checksum: f9ec4e5f37ac1ca446fcef9ac91c1fb5 (MD5) Previous issue date: 2015-09-10 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / O reconhecimento de ações humanas é um problema desafiador em visão computacional que tem potenciais áreas de aplicações. Para descrever o principal movimento do vídeo um novo descritor de movimento é proposto neste trabalho. Este trabalho combina dois métodos para estimar o movimento entre as imagens: casamento de blocos e de gradiente de intensidade de brilho da imagem. Neste trabalho usa-se um algoritmo de casamento de blocos de tamanho variável para extrair vetores de deslocamento, os quais contém a informação de movimento. Estes vetores são computados em uma sequência de frames obtendo a trajetória do bloco, que possui a informação temporal. Os vetores obtidos através do casamento de blocos são usados para clusterizar as trajetórias esparsas de acordo com a forma. O método proposto computa essa informação para obter tensores de orientação e gerar o descritor final. Este descritor é chamado de autodescritor porque depende apenas do vídeo de entrada. O tensor usado como descritor global é avaliado através da classificação dos vídeos das bases de dados KTH, UCF11 e Hollywood2 com o classificador não linear SVM. Os resultados indicam que este método de trajetórias esparsas é competitivo comparado ao já conhecido método de trajetórias densas, usando tensores de orientação, além de requerer menos esforço computacional. / Human action recognition is a challenging problem in Computer Vision which has many potential applications. In order to describe the main movement of the video a new motion descriptor is proposed in this work. We combine two methods for estimating the motion between frames: block matching and brightness gradient of image. In this work we use a variable size block matching algorithm to extract displacement vectors as a motion information. The cross product between the block matching vector and the gra dient is used to obtain the displacement vectors. These vectors are computed in a frame sequence, obtaining the block trajectory which contains the temporal information. The block matching vectors are also used to cluster the sparse trajectories according to their shape. The proposed method computes this information to obtain orientation tensors and to generate the final descriptor. It is called self-descriptor because it depends only on the input video. The global tensor descriptor is evaluated by classification of KTH, UCF11 and Hollywood2 video datasets with a non-linear SVM classifier. Results indicate that our sparse trajectories method is competitive in comparison to the well known dense tra jectories approach, using orientation tensors, besides requiring less computational effort.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-06-06T19:12:19Z No. of bitstreams: 1 fabioluizmarinhodeoliveira.pdf: 1410854 bytes, checksum: cb71ee666cda7d462ce0dd33963a988c (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-06-07T13:33:08Z (GMT) No. of bitstreams: 1 fabioluizmarinhodeoliveira.pdf: 1410854 bytes, checksum: cb71ee666cda7d462ce0dd33963a988c (MD5) / Made available in DSpace on 2017-06-07T13:33:08Z (GMT). No. of bitstreams: 1 fabioluizmarinhodeoliveira.pdf: 1410854 bytes, checksum: cb71ee666cda7d462ce0dd33963a988c (MD5) Previous issue date: 2016-09-05 / Descrição de movimento tem sido um tema desafiador e popular há muitos anos em visão computacional e processamento de sinais, mas também intimamente relacionado a aprendizado de máquina e reconhecimento de padrões. Frequentemente, para realizar essa tarefa, informação de movimento é extraída e codificada em um descritor. Este trabalho apresenta um método simples e de rápida computação para extrair essa informação e codificá-la em descritores baseados em histogramas de deslocamentos relativos. Nossos descritores são compactos, globais, que agregam informação de quadros inteiros, e o que chamamos de auto-descritor, que não depende de informações de sequências senão aquela que pretendemos descrever. Para validar estes descritores e compará-los com outros tra balhos, os utilizamos no contexto de Reconhecimento de Ações Humanas, no qual cenas são classificadas de acordo com as ações nelas exibidas. Nessa validação, obtemos resul tados comparáveis aos do estado-da-arte para a base de dados KTH. Também avaliamos nosso método utilizando as bases UCF11 e Hollywood2, com menores taxas de reconhe cimento, considerando suas maiores complexidades. Nossa abordagem é promissora, pelas razoáveis taxas de reconhecimento obtidas com um método muito menos complexo que os do estado-da-arte, em termos de velocidade de computação e compacidade dos descritores obtidos. Adicionalmente, experimentamos com o uso de Aprendizado de Métrica para a classificação de nossos descritores, com o intuito de melhorar a separabilidade e a com pacidade dos descritores. Os resultados com Aprendizado de Métrica apresentam taxas de reconhecimento inferiores, mas grande melhoria na compacidade dos descritores. / Motion description has been a challenging and popular theme over many years within computer vision and signal processing, but also very closely related to machine learn ing and pattern recognition. Very frequently, to address this task, one extracts motion information from image sequences and encodes this information into a descriptor. This work presents a simple and fast computing method to extract this information and en code it into descriptors based on histograms of relative displacements. Our descriptors are compact, global, meaning it aggregates information from whole frames, and what we call self-descriptors, meaning they do not depend on information from sequences other than the one we want to describe. To validate these descriptors and compare them to other works, we use them in the context of Human Action Recognition, where scenes are classified according to the action portrayed. In this validation, we achieve results that are comparable to those in the state-of-the-art for the KTH dataset. We also evaluate our method on the UCF11 and Hollywood2 datasets, with lower recognition rates, considering their higher complexity. Our approach is a promising one, due to the fairly good recogni tion rates we obtain with a much less complex method than those of the state-of-the-art, in terms of speed of computation and final descriptor compactness. Additionally, we ex periment with the use of Metric Learning in the classification of our descriptors, aiming to improve the separability and compactness of the descriptors. Our results for Metric Learning show inferior recognition rates, but great improvement for the compactness of the descriptors.

Motion is an important cue in videos that captures the dynamics of moving objects. It helps in effective analysis of various event related tasks such as human action recognition, anomaly detection, tracking, crowd behavior analysis, traffic monitoring, etc. Generally, accurate motion information is computed using various optical flow estimation techniques. On the other hand, coarse motion information is readily available in the form of motion vectors in compressed videos. Utilizing these encoded motion vectors reduces the computational burden involved in flow estimation and enables rapid analysis of video streams. In this work, the focus is on analyzing motion patterns, retrieved from either motion vectors or optical flow, in order to do various event analysis tasks such as video classification, anomaly detection and crowd flow segmentation. In the first section, we utilize the motion vectors from H.264 compressed videos, a compression standard widely used due to its high compression ratio, to address the following problems. i) Video classification: This work proposes an approach to classify videos based on human action by capturing spatio-temporal motion pattern of the actions using Histogram of Oriented Motion Vector (HOMV) ii) Crowd flow segmentation: In this work, we have addressed the problem of flow segmentation of the dominant motion patterns of the crowds. The proposed approach combines multi-scale super-pixel segmentation of the motion vectors to obtain the final flow segmentation. iii) Anomaly detection: This problem is addressed by local modeling of usual behavior by capturing features such as magnitude and orientation of each moving object. In all the above approaches, the focus was to reduce computations while retaining comparable accuracy to pixel domain processing. In second section, we propose two approaches for anomaly detection using optical flow. The first approach uses spatio-temporal low level motion features and detects anomalies based on the reconstruction error of the sparse representation of the candidate feature over a dictionary of usual behavior features. The main contribution is in enhancing each local dictionary by applying an appropriate transformation on dictionaries of the neighboring regions. The other algorithm aims to improve the accuracy of anomaly localization through short local trajectories of super pixels belonging to moving objects. These trajectories capture both spatial as well as temporal information effectively. In contrast to compressed domain analysis, these pixel level approaches focus on improving the accuracy of detection with reasonable detection speed.

In recent years, the economic approach to human behavior has been challenged by contributions of cognitive science. Thus two methodological strands in economics disagree with each other: the objectivistic approach favors the methods of natural science; the subjectivistic approach takes the teleological structure of human action as its cornerstone. It is argued that the position of the latter has been undermined and often degraded to a mere instrumentalist tool because it builds upon the primitive version of the teleological structure. Its deeper realist analysis is needed, which is the task for economic phenomenology: it identifies invariant pragmatic structures of human action, with various degrees of their anonymity. If the economic approach is founded on those structures adequately, then both rational choice theory and bounded rationality theories become compatible, as they differ in their degrees of anonymity only; they both belong to the body of the (subjectivistic) economic approach to human behavior. Economic phenomenology also offers a solution to the phenomenon of inconsistency of human action which is documented by cognitive sciences as a proof of human irrationality. The thesis shows that once the decision maker's description of the choice is allowed, inconsistency may disappear. Consistency is a matter of thinking, not acting. Therefore, a conceptual analysis of human thinking is needed. An example of the analysis is presented. It concentrates on the phenomenon of Self and works up the concept of the horizontality of Self. With this concept, inconsistency of human action is derived as a natural characteristic of our being-in-the-world. Inconsistency of human action is a pragmatic structure of human action, which even allows the decision maker some intentional control.

As the theme of the study indicates the study is a narrative study seeking to respond to two of the major challenges which congregations are facing within the context of ministry, namely postmodernity and globalization. After seeking a fuller description of these two challenges I sought a theological orientation within such a context (postmodern global village) as well as an ecclesiological praxis that could be transformative and redemptive within such a context. I believe to have found in the narrative orientation an appropriate way for doing theology in the postmodern context. The narrative orientation will guide the story of this study within four movements, namely descriptive theology (stories of need), historical theology (texts and tradition), systematic theology (re-authored story of the past) and lastly strategic practical theology (imagined story of the future). The climax of this journey (story) is in the fusion of horizons between the theory-laden questions of descriptive theology and the historical texts of the Christian faith within the narrative orientation of the study. I discovered that truly transformative and redemptive praxis is only possible within language communities (narrative communities). These narrative communities cannot exist in isolation, but are continuously confronted and relativised by the stories of other communities in the global village and therefore these language communities need to be open to the fragmentation and pluralism of the global village, otherwise they will not be able to respond to the reality of the globalization and postmodernity. The narrative communities needed a story (sacred story) that did not deny the reality of fragmentation and pluralism, but could incorporate this reality into its story. I found this story in the story of the cross and therefore refer to the narrative communities as communities of and under the cross of Christ. These ideas formed the basis for a transformative praxis within a specific congregation, namely Pastoral Redemptive Communities. The journey within these four movements was a critical journey in dialogue with other disciplines (economics, philosophy, psychology and sociology) and I tried to defend and describe my journey within the parameters of validity claims thereby opening the study for further dialogue. / Thesis (PhD (Practical Theology))--University of Pretoria, 2005. / Practical Theology / unrestricted

<p>Robots have become increasingly common in various sectors, such as manufacturing, healthcare, and service industries. With the growing demand for automation and the expectation for interactive and assistive capabilities, robots must learn to adapt to unpredictable environments like humans can. This necessitates the development of learning methods that can effectively enable robots to collaborate with humans, learn from them, and provide guidance. Human experts commonly teach their collaborators to perform tasks via a few demonstrations, often followed by episodes of coaching that refine the trainee’s performance during practice. Adopting a similar approach that facilitates interactions to teaching robots is highly intuitive and enables task experts to teach the robots directly. Learning from Demonstration (LfD) is a popular method for robots to learn tasks by observing human demonstrations. However, for contact-rich tasks such as cleaning, cutting, or writing, LfD alone is insufficient to achieve a good performance. Further, LfD methods are developed to achieve observed goals while ignoring actions to maximize efficiency. By contrast, we recognize that leveraging human social learning strategies of practice and coaching in conjunction enables learning tasks with improved performance and efficacy. To address the deficiencies of learning from demonstration, we propose a Coaching by Demonstration (CbD) framework that integrates LfD-based practice with sparse coaching interactions from a human expert.</p> <p><br></p> <p>The LfD-based practice in CbD was implemented as an end-to-end off-policy reinforcement learning (RL) agent with the action space and rewards inferred from the demonstration. By modeling the reward as a similarity network trained on expert demonstrations, we eliminate the need for designing task-specific engineered rewards. Representation learning was leveraged to create a novel state feature that captures interaction markers necessary for performing contact-rich skills. This LfD-based practice was combined with coaching, where the human expert can improve or correct the objectives through a series of interactions. The dynamics of interaction in coaching are formalized using a partially observable Markov decision process. The robot aims to learn the true objectives by observing the corrective feedback from the human expert. We provide an approximate solution by reducing this to a policy parameter update using KL divergence between the RL policy and a Gaussian approximation based on coaching. The proposed framework was evaluated on a dataset of 10 contact-rich tasks from the assembly (peg-insertion), service (cleaning, writing, peeling), and medical domains (cricothyroidotomy, sonography). Compared to baselines of behavioral cloning and reinforcement learning algorithms, CbD demonstrates improved performance and efficiency.</p> <p><br></p> <p>During the learning process, the demonstrations and coaching feedback imbue the robot with expert knowledge of the task. To leverage this expertise, we develop a reverse coaching model where the robot can leverage knowledge from demonstrations and coaching corrections to provide guided feedback to human trainees to improve their performance. Providing feedback adapted to individual trainees' "style" is vital to coaching. To this end, we have proposed representing style as objectives in the task null space. Unsupervised clustering of the null-space trajectories using Gaussian mixture models allows the robot to learn different styles of executing the same skill. Given the coaching corrections and style clusters database, a style-conditioned RL agent was developed to provide feedback to human trainees by coaching their execution using virtual fixtures. The reverse coaching model was evaluated on two tasks, a simulated incision and obstacle avoidance through a haptic teleoperation interface. The model improves human trainees’ accuracy and completion time compared to a baseline without corrective feedback. Thus, by taking advantage of different human-social learning strategies, human-robot collaboration can be realized in human-centric environments. </p> <p><br></p>

Dans cette thèse nous étudions les algorithmes d'apprentissage automatique pour la reconnaissance visuelle. Un accent particulier est mis sur l'apprentissage automatique de représentations, c.à.d. l'apprentissage automatique d'extracteurs de caractéristiques; nous insistons également sur l'apprentissage conjoint de ces dernières avec le modèle de prédiction des problèmes traités, tels que la reconnaissance d'objets, la reconnaissance d'activités humaines, ou la segmentation d'objets. Dans ce contexte, nous proposons plusieurs contributions : Une première contribution concerne les modèles de type bags of words (BoW), où le dictionnaire est classiquement appris de manière non supervisée et de manière autonome. Nous proposons d'apprendre le dictionnaire de manière supervisée, c.à.d. en intégrant les étiquettes de classes issues de la base d'apprentissage. Pour cela, l'extraction de caractéristiques et la prédiction de la classe sont formulées en un seul modèle global de type réseau de neurones (end-to-end training). Deux algorithmes d'apprentissage différents sont proposés pour ce modèle : le premier est basé sur la retro-propagation du gradient de l'erreur, et le second procède par des mises à jour dans le diagramme de Voronoi calculé dans l'espace des caractéristiques. Une deuxième contribution concerne l'intégration d'informations géométriques dans l'apprentissage supervisé et non-supervisé. Elle se place dans le cadre d'applications nécessitant une segmentation d'un objet en un ensemble de régions avec des relations de voisinage définies a priori. Un exemple est la segmentation du corps humain en parties ou la segmentation d'objets spécifiques. Nous proposons une nouvelle approche intégrant les relations spatiales dans l'algorithme d'apprentissage du modèle de prédication. Contrairement aux méthodes existantes, les relations spatiales sont uniquement utilisées lors de la phase d'apprentissage. Les algorithmes de classification restent inchangés, ce qui permet d'obtenir une amélioration du taux de classification sans augmentation de la complexité de calcul lors de la phase de test. Nous proposons trois algorithmes différents intégrant ce principe dans trois modèles : - l'apprentissage du modèle de prédiction des forêts aléatoires, - l'apprentissage du modèle de prédiction des réseaux de neurones (et de la régression logistique), - l'apprentissage faiblement supervisé de caractéristiques visuelles à l'aide de réseaux de neurones convolutionnels. / In this thesis, we present our research on visual recognition and machine learning. Two types of visual recognition problems are investigated: action recognition and human body part segmentation problem. Our objective is to combine spatial information such as label configuration in feature space, or spatial layout of labels into an end-to-end framework to improve recognition performance. For human action recognition, we apply the bag-of-words model and reformulate it as a neural network for end-to-end learning. We propose two algorithms to make use of label configuration in feature space to optimize the codebook. One is based on classical error backpropagation. The codewords are adjusted by using gradient descent algorithm. The other is based on cluster reassignments, where the cluster labels are reassigned for all the feature vectors in a Voronoi diagram. As a result, the codebook is learned in a supervised way. We demonstrate the effectiveness of the proposed algorithms on the standard KTH human action dataset. For human body part segmentation, we treat the segmentation problem as classification problem, where a classifier acts on each pixel. Two machine learning frameworks are adopted: randomized decision forests and convolutional neural networks. We integrate a priori information on the spatial part layout in terms of pairs of labels or pairs of pixels into both frameworks in the training procedure to make the classifier more discriminative, but pixelwise classification is still performed in the testing stage. Three algorithms are proposed: (i) Spatial part layout is integrated into randomized decision forest training procedure; (ii) Spatial pre-training is proposed for the feature learning in the ConvNets; (iii) Spatial learning is proposed in the logistical regression (LR) or multilayer perceptron (MLP) for classification.

Dans le contexte du programme de recherche scientifique autrichien en économie, le processus de marché doit être appréhendé par le prisme du concept d'entrepreneur. La fonction créative d'entrepreneur se présente comme un processus individuel largement épistémique, lequel s'élucide en une action à effet catallactique. Elle contribue à l'émergence spontanée de l`ordre, concrétisant ainsi l'évolutionnisme individuel et institutionnel. Or son apparition dépend de déterminants institutionnels particuliers. Par conséquent, le processus de marché, qui émerge des actions catallactiques, requiert un fondement institutionnel spécifique comme une condition sine qua non. En outre, notre analyse s'étend au processus politique à travers le prisme de l'action politique, afin d'examiner l'adéquation de cette action à la nature catallactique. Cette analyse permet aussi de décrypter les institutions qui régissent l'ordre politique confectionné. Cependant, cette investigation relève au cours du processus politique, la déficience du fondement institutionnel indispensable à l'émergence de la fonction d'entrepreneur dans l'action politique. Cela constitue une barrière infranchissable pour l'analogie catallactique dans la pratique politique, et contribue à expliquer la défaillance de la conjecture de marché politique, en raison de l'impossibilité de l'émergence d'un échange politique légitime. Du point de vue de l'éthique, l'action politique s'avère nocive à l'ensemble des droits individuels, voire aux droits de propriété privée. Elle diverge de celle entrepreneuriale car elle dénie la coordination. Par conséquent, cette réalité nous amène à réfuter la conjecture d'entrepreneur politique. / In the context of the Austrian scientific research program in economics, our study deals with the market process through the prism of Entrepreneur`s concept. The creative entrepreneurial function emerges as an epistemic individual process, which contributes to a catallactic action. It strengthens the emergence of the spontaneous order, thus shaping individual and institutional evolutionism. The emergence of such action mainly depends on institutional determinants. Therefore, the market process, emerging from catallactic actions, requires a specific institutional foundation. Thereby, our analysis concerns the political process; this, through the prism of the political action, to match the adequacy of this action to the catallactic one. This analysis also decrypts the institutions which determine the political order. However, this introspection identifies the deficiency of the institutional foundation required for the emergence of the entrepreneurial function through the political action. This barrier of the catallactic analogy in the political practice contributes to the refutation of the political market conjecture. The main reason of this failure is the impossibility of a legitimate political exchange emergence. From the objective ethics perspective, the political action harms the individual rights in general and the private property rights in particular. By denying the coordination, this political action shows a divergent nature from the entrepreneurial one. Therefore, this reality leads to refute the political entrepreneur conjecture.

<p>Anne Charlotte Leffler, Victoria Benedictsson and Frida Stéenhoff were all part of the Modern Breakthrough in Swedish literature. By utilizing Jürgen Habermas theoretical works on communicative action, and Nancy Fraser’s supplementary reading of his theory, this essay makes clear that the authors’ struggle for an understanding and a rethinking of social norms in their plays <em>Skådespelerskan </em>(1873), <em>Romeos Julia </em>(1888) and <em>Lejonets unge </em>(1896) can be read as a contribution to the public debate. Dialogue has a key function for female authors during the Modern Breakthrough. Women and mens’ possibilities to take part in conversation and argument as equals, requires the professional woman’s transgression and access to the privileges of both public (State) administration – “system”, and world of everyday life – “lifeworld”. As oppositional authors, Leffler, Benedictsson and Stéenhoff took advantage of the literary public domain, in this case the theatre. The theatre as public sphere had a more effective capacity to affect its audience than fiction. The plays were written and staged in a cultural period that, compared with the present one, in a greater sense influenced public opinion. The theatre was a powerful part of the public debate, and this debate involved parties from both on-stage and off-stage positions.</p><p>At the theatre Leffler, Benedictsson and Stéenhoff turned the private into politics. Their powerful contribution to the public debate were directed specifically towards the bourgeois audience. They carried on a controversy concerning the notion of the bourgeois family by showing scenarios that raised objection to its idealistic point of view. The bourgeois living room was exposed on stage. Conflicts related to the private sphere were brought up as a theme in a public sphere and by so means were incorporated into the public debate, which at that time was dominated by men. The plays mirrored the audience and the authors’ strategies were based upon the remodeling power inherent in conversation and argumentation. Thereby, the audience were confronted with an alternated reflection of themselves. This reflection should be read as a problematic representation of the writing of history. The alternate mirroring brought in itself forward an argument that emphasized why a new reflection was necessary.</p><p><em>Skådespelerskan</em>, <em>Romeos Julia </em>and <em>Lejonets unge</em> examine female artists in a bourgeois environment. Through the artist the structures that maintain bourgeois ideals are exposed and questioned, but first and foremost the artist show that communicative action between men and women was insufficient. The androcentric order did not acknowledge women and men as equals, in accordance with that, the possibility of conversation, in the sense of Habermas, came to nothing. Under such circumstances an understanding between men and women could not be reached. Love within the institution of marriage also suffered since it could not be founded in acknowledgement as long as the structure maintained. Saga and Adil, the main characters in <em>Lejonets unge</em>, personify, on the other hand, Stéenhoffs ideas of what is being needed to change the androcentric structure. They are citizens of the future.</p><p>My focus is on human action (in an Aristotelian sense) in the plays, the way characters stage themselves and bring the plot forward, shed light upon their possibilities and limitations in proportion to each other as men and women. My analysis thereby contrasts with the greater part of earlier research. I give prominence to the connection between psychological conflict and social position/role. In <em>Skådespelerskan</em>, <em>Romeos Julia</em> and <em>Lejonets unge</em> all of the characters’ psychological conflicts are based in gender issues, the consequences of being a man or woman in the 18th century, rather than explicit existential matters.</p>

Anne Charlotte Leffler, Victoria Benedictsson and Frida Stéenhoff were all part of the Modern Breakthrough in Swedish literature. By utilizing Jürgen Habermas theoretical works on communicative action, and Nancy Fraser’s supplementary reading of his theory, this essay makes clear that the authors’ struggle for an understanding and a rethinking of social norms in their plays Skådespelerskan (1873), Romeos Julia (1888) and Lejonets unge (1896) can be read as a contribution to the public debate. Dialogue has a key function for female authors during the Modern Breakthrough. Women and mens’ possibilities to take part in conversation and argument as equals, requires the professional woman’s transgression and access to the privileges of both public (State) administration – “system”, and world of everyday life – “lifeworld”. As oppositional authors, Leffler, Benedictsson and Stéenhoff took advantage of the literary public domain, in this case the theatre. The theatre as public sphere had a more effective capacity to affect its audience than fiction. The plays were written and staged in a cultural period that, compared with the present one, in a greater sense influenced public opinion. The theatre was a powerful part of the public debate, and this debate involved parties from both on-stage and off-stage positions. At the theatre Leffler, Benedictsson and Stéenhoff turned the private into politics. Their powerful contribution to the public debate were directed specifically towards the bourgeois audience. They carried on a controversy concerning the notion of the bourgeois family by showing scenarios that raised objection to its idealistic point of view. The bourgeois living room was exposed on stage. Conflicts related to the private sphere were brought up as a theme in a public sphere and by so means were incorporated into the public debate, which at that time was dominated by men. The plays mirrored the audience and the authors’ strategies were based upon the remodeling power inherent in conversation and argumentation. Thereby, the audience were confronted with an alternated reflection of themselves. This reflection should be read as a problematic representation of the writing of history. The alternate mirroring brought in itself forward an argument that emphasized why a new reflection was necessary. Skådespelerskan, Romeos Julia and Lejonets unge examine female artists in a bourgeois environment. Through the artist the structures that maintain bourgeois ideals are exposed and questioned, but first and foremost the artist show that communicative action between men and women was insufficient. The androcentric order did not acknowledge women and men as equals, in accordance with that, the possibility of conversation, in the sense of Habermas, came to nothing. Under such circumstances an understanding between men and women could not be reached. Love within the institution of marriage also suffered since it could not be founded in acknowledgement as long as the structure maintained. Saga and Adil, the main characters in Lejonets unge, personify, on the other hand, Stéenhoffs ideas of what is being needed to change the androcentric structure. They are citizens of the future. My focus is on human action (in an Aristotelian sense) in the plays, the way characters stage themselves and bring the plot forward, shed light upon their possibilities and limitations in proportion to each other as men and women. My analysis thereby contrasts with the greater part of earlier research. I give prominence to the connection between psychological conflict and social position/role. In Skådespelerskan, Romeos Julia and Lejonets unge all of the characters’ psychological conflicts are based in gender issues, the consequences of being a man or woman in the 18th century, rather than explicit existential matters.