Symbol Emergence in Robotics: A Survey

Humans can learn the use of language through physical interaction with their environment and semiotic communication with other people. It is very important to obtain a computational understanding of how humans can form a symbol system and obtain semiotic skills through their autonomous mental development. Recently, many studies have been conducted on the construction of robotic systems and machine-learning methods that can learn the use of language through embodied multimodal interaction with their environment and other systems. Understanding human social interactions and developing a robot that can smoothly communicate with human users in the long term, requires an understanding of the dynamics of symbol systems and is crucially important. The embodied cognition and social interaction of participants gradually change a symbol system in a constructive manner. In this paper, we introduce a field of research called symbol emergence in robotics (SER). SER is a constructive approach towards an emergent symbol system. The emergent symbol system is socially self-organized through both semiotic communications and physical interactions with autonomous cognitive developmental agents, i.e., humans and developmental robots. Specifically, we describe some state-of-art research topics concerning SER, e.g., multimodal categorization, word discovery, and a double articulation analysis, that enable a robot to obtain words and their embodied meanings from raw sensory--motor information, including visual information, haptic information, auditory information, and acoustic speech signals, in a totally unsupervised manner. Finally, we suggest future directions of research in SER.Comment: submitted to Advanced Robotic

Predicting continuous conflict perception with Bayesian Gaussian processes

Conflict is one of the most important phenomena of social life, but it is still largely neglected by the computing community. This work proposes an approach that detects common conversational social signals (loudness, overlapping speech, etc.) and predicts the conflict level perceived by human observers in continuous, non-categorical terms. The proposed regression approach is fully Bayesian and it adopts Automatic Relevance Determination to identify the social signals that influence most the outcome of the prediction. The experiments are performed over the SSPNet Conflict Corpus, a publicly available collection of 1430 clips extracted from televised political debates (roughly 12 hours of material for 138 subjects in total). The results show that it is possible to achieve a correlation close to 0.8 between actual and predicted conflict perception

Interactive Segmentation in Multimodal Medical Imagery Using a Bayesian Transductive Learning Approach

Labeled training data in the medical domain is rare and expensive to obtain. The lack of labeled multimodal medical image data is a major obstacle for devising learning-based interactive segmentation tools. Transductive learning (TL) or semi-supervised learning (SSL) offers a workaround by leveraging unlabeled and labeled data to infer labels for the test set given a small portion of label information. In this paper we propose a novel algorithm for interactive segmentation using transductive learning and inference in conditional mixture nave Bayes models (T-CMNB) with spatial regularization constraints. T-CMNB is an extension of the transductive nave Bayes algorithm [1, 20]. The multimodal Gaussian mixture assumption on the class-conditional likelihood and spatial regularization constraints allow us to explain more complex distributions required for spatial classification in multimodal imagery. To simplify the estimation we reduce the parameter space by assuming nave conditional independence between the feature space and the class label. The nave conditional independence assumption allows efficient inference of marginal and conditional distributions for large scale learning and inference [19]. We evaluate the proposed algorithm on multimodal MRI brain imagery using ROC statistics and provide preliminary results. The algorithm shows promising segmentation performance with a sensitivity and specificity of 90.37% and 99.74% respectively and compares competitively to alternative interactive segmentation schemes

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

Multimodal and nested preference structures in choice-based conjoint analysis: a comparison of bayesian choice models with discrete and continuous representations of heterogeneity

Die Choice-Based Conjoint-Analyse (CBC) ist heutzutage die am weitesten verbreitete Variante der Conjoint-Analyse, einer Klasse von Verfahren zur Messung von Nachfragerpräferenzen. Der Hauptgrund für die zunehmende Dominanz des CBC-Ansatzes in jüngerer Zeit besteht darin, dass hier das tatsächliche Wahlverhalten von Nachfragern sehr realistisch nachgestellt werden kann, indem die Befragten wiederholt ihre bevorzugte Alternative aus einer Menge mehrerer Alternativen (Choice Sets) auswählen. Im Rahmen der CBC-Analyse ist das Multinomiale Logit- (MNL) Modell das am häufigsten verwendete diskrete Wahlmodell. Das MNL-Modell weist jedoch zwei wesentliche Einschränkungen auf: (a) Es impliziert proportionale Substitutionsmuster zwischen den Alternativen, was als Independence of Irrelevant Alternatives- (IIA) Eigenschaft bezeichnet wird, und (b) es berücksichtigt keine Nachfragerheterogenität, da per Definition Teilnutzenwerte für alle Konsumenten als homogen angenommen werden. Seit den 1990er-Jahren werden hierarchisch bayesianische (HB) Modelle für die Teilnutzenwertschätzung in der CBC-Analyse verwendet. Solche HB-Modelle ermöglichen zum einen eine Schätzung individueller Teilnutzenwerte, selbst bei einer beschränkten Datenlage, zum anderen können sie aufgrund der Modellierung von Heterogenität die IIA-Eigenschaft stark abmildern. Der Schwerpunkt der vorliegenden Thesis liegt auf der Verwendung von HB-Modellen mit unterschiedlichen Darstellungen von Nachfragerheterogenität (diskret vs. kontinuierlich) für CBC-Daten sowie außerdem auf einem speziellen HB-Modell, das die IIA-Eigenschaft durch Berücksichtigung von unterschiedlichen Ähnlichkeitsgraden zwischen Teilmengen von Alternativen (Nestern) zusätzlich abschwächt. Insbesondere wird die statistische Performance von einfachen MNL-, Latent Class- (LC) MNL-, HB-MNL-, Mixture-of-Normals- (MoN) MNL-, Dirichlet Process Mixture- (DPM) MNL- und HB-Nested Multinomialen Logit- (NMNL) Modellen (unter experimentell variierenden Bedingungen) hinsichtlich der Recovery von Präferenzstrukturen, der Anpassungsgüte und der Prognosevalidität analysiert. Dazu werden zwei umfangreiche Monte-Carlo-Studien durchgeführt, ferner werden die verschiedenen Modelltypen auf einen empirischen CBC-Datensatz angewandt. In der ersten Monte-Carlo-Studie liegt der Fokus auf dem Vergleich zwischen dem HB-MNL und dem HB-NMNL bei multimodalen und genesteten Präferenzstrukturen. Die Ergebnisse zeigen, dass es keine wesentlichen Unterschiede zwischen beiden Modelltypen hinsichtlich der Anpassungsgüte und insbesondere hinsichtlich der Prognosevalidität gibt. In Bezug auf die Recovery von Präferenzstrukturen schneidet das HB-MNL-Modell zunehmend schlechter ab, wenn die Korrelation in mindestens einem Nest höher ist, während sich das HB-NMNL-Modell erwartungsgemäß an den Grad der Ähnlichkeit zwischen Alternativen anpasst. Die zweite Monte-Carlo-Studie befasst sich mit multimodalen und segmentspezifischen Präferenzstrukturen. Um Unterschiede zwischen den Klassen von Modellen mit unterschiedlichen Darstellungen von Heterogenität herauszuarbeiten, werden hier gezielt die Grade der Heterogenität innerhalb von Segmenten und zwischen Segmenten manipuliert. Unter experimentell variierenden Bedingungen werden die state-of-the-art Ansätze zur Modellierung von Heterogenität (einfaches MNL, LC-MNL, HB-MNL) mit erweiterten Wahlmodellen, die sowohl Nachfragerheterogenität zwischen Segmenten als auch innerhalb von Segmenten abbilden können (MoN-MNL und DPM-MNL), verglichen. Das zentrale Ergebnis dieser Monte-Carlo-Studie ist, dass sich das HB-MNL-Modell, welches eine multivariate Normalverteilung zur Modellierung von Präferenzheterogenität unterstellt, als äußerst robust erweist. Darüber hinaus kristallisiert sich der LC-MNL-Segmentansatz als der beste Ansatz heraus, um die „wahre“ Anzahl von Segmenten zu identifizieren. Abschließend werden die zuvor vorgestellten Wahlmodelle auf einen realen CBC-Datensatz angewandt. Die Ergebnisse zeigen, dass Modelle mit einer kontinuierlichen Darstellung von Heterogenität (HB-MNL, HB-NMNL, MoN-MNL und DPM-MNL) eine bessere Anpassungsgüte und Prognosevalidität aufweisen als Modelle mit einer diskreten Darstellung von Heterogenität (einfaches MNL, LC-MNL). Weiterhin zeigt sich, dass das HB-MNL-Modell für Prognosezwecke sehr gut geeignet ist und im Vergleich zu den anderen (erweiterten) Modellen mindestens ebenso gute, wenn nicht sogar wesentlich bessere Vorhersagen liefert, was für Manager eine zentrale Erkenntnis darstellt.Choice-Based Conjoint (CBC) is nowadays the most widely used variant of conjoint analysis, a class of methods for measuring consumer preferences. The primary reason for the increasing dominance of the CBC approach over the last 35 years is that it closely mimics real choice behavior of consumers by asking respondents repeatedly to choose their preferred alternative from a set of several offered alternatives (choice sets), respectively. Within the framework of CBC analysis, the multinomial logit (MNL) model is the most frequently used discrete choice model. However, the MNL model suffers from two major limitations: (a) it implies proportional substitution rates across alternatives, referred to as the Independence of Irrelevant Alternatives (IIA) property and (b) it does not account for unobserved consumer heterogeneity, as part-worth utilities are assumed to be equal for all respondents by definition. Since the 1990s, Hierarchical Bayesian (HB) models have been used for part-worth utility estimation in CBC analysis. HB models are able to determine part-worth utilities at the individual respondent level even with little individual respondent information on the one hand and, as a result of addressing consumer heterogeneity, can strongly soften the IIA property on the other hand. The focus of the present thesis is on CBC analysis using HB models with different representations of heterogeneity (discrete vs. continuous) as well as using a HB model which mitigates the IIA property to a further extent by allowing for different degrees of similarity between subsets (nests) of alternatives. In particular, we systematically explore the comparative performance of simple MNL, latent class (LC) MNL, HB-MNL, mixture-of-normals (MoN) MNL, Dirichlet Process Mixture (DPM) MNL and HB nested multinomial logit (NMNL) models (under experimentally varying conditions) using statistical criteria for parameter recovery, goodness-of-fit, and predictive accuracy. We conduct two extensive Monte Carlo studies and apply the different types of models to an empirical CBC data set. In the first Monte Carlo study, the focus lies on the comparative performance of the HB-MNL versus the HB-NMNL for multimodal and nested preference structures. Our results show that there seems to be no major differences between both types of models with regard to goodness-of-fit measures and in particular their ability to predict respondents’ choice behavior. Regarding parameter recovery, the HB-MNL model performs increasingly worse when correlation in at least one nest is higher, while the HB-NMNL model adapts to the degree of similarity between alternatives, as expected. The second Monte Carlo study deals with multimodal and segment-specific preference structures. More precisely, to carve out differences between the classes of models with different representations of heterogeneity, we specifically vary the degrees of within-segment and between-segment heterogeneity. We compare state-of-the-art methods to represent heterogeneity (simple MNL, LC-MNL, HB-MNL) and more advanced choice models representing both between-segment and within-segment consumer heterogeneity (MoN-MNL and DPM-MNL) under varying experimental conditions. The core finding from our Monte Carlo study is that the HB-MNL model appears to be highly robust against violations in its assumption of a single multivariate normal distribution of consumer preferences. In addition, the LC-MNL segment solution proves to be the best approach to recover the “true” number of segments. Finally, we apply the previously presented choice models to a real-life CBC data set. The results indicate that models with a continuous representation of heterogeneity (HB-MNL, HB-NMNL, MoN-MNL and DPM-MNL) perform better than models with a discrete representation of heterogeneity (simple MNL, LC-MNL). Further, it turns out that the HB-MNL model works extremely well for predictive purposes and provides at least as good if not considerably better predictions compared to the other (advanced) models, which is an important aspect for managers