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

The 1990 progress report and future plans

This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers

A Belief Network Approach to Modeling of Environmental Change: The Methodology and Prospects for Application

In environmental management, assessments and far-reaching decisions must typically be made under very high or extreme uncertainty. The future development of the environment in interaction with societies in transition is very difficult to forecast. This is the case regardless of whether the change is introduced actively at the project or policy level, or passively through accumulated environmental deterioration or climatic change. This study presents a belief network methodology designed specifically for modeling environmental change. Belief networks contain a set of interlinked nodes. Prior probability distributions of nodes are updated with information from the rest of the network, according to transfer information in links. A link can transmit information in two directions. The existing belief network methodology was extended in several ways to meet the multiple requirements of environmental modeling. Most notably, two-layered parallel linking of nodes was allowed: the conventional probabilistic linking, and linking of outcomes of probability distributions using deterministic or logical relations. Moreover, several decision analysis techniques were included. The applicability of the methodology is discussed in reference to the following topics: knowledge acquisition, decision analytic modeling, mechanistic and process modeling, topological and spatial modeling, learning and adaptive modeling, and hybrid use

Human desire inference process and analysis

Ubiquitous computing becomes a more fascinating research area since it may offer us an unobtrusive way to help users in their environments that integrate surrounding objects and activities. To date, there have been numerous studies focusing on how user\u27s activity can be identified and predicted, without considering motivation driving an action. However, understanding the underlying motivation is a key to activity analysis. On the other hand, user\u27s desires often generate motivations to engage activities in order to fulfill such desires. Thus, we must study user\u27s desires in order to provide proper services to make the life of users more comfortable. In this study, we present how to design and implement a computational model for inference of user\u27s desire. First, we devised a hierarchical desire inference process based on the Bayesian Belief Networks (BBNs), that considers the affective states, behavior contexts and environmental contexts of a user at given points in time to infer the user\u27s desire. The inferred desire of the highest probability from the BBNs is then used in the subsequent decision making. Second, we extended a probabilistic framework based on the Dynamic Bayesian Belief Networks (DBBNs) which model the observation sequences and information theory. A generic hierarchical probabilistic framework for desire inference is introduced to model the context information and the visual sensory observations. Also, this framework dynamically evolves to account for temporal change in context information along with the change in user\u27s desire. Third, we described what possible factors are relevant to determine user\u27s desire. To achieve this, a full-scale experiment has been conducted. Raw data from sensors were interpreted as context information. We observed the user\u27s activities and get user\u27s emotions as a part of input parameters. Throughout the experiment, a complete analysis was conducted whereas 30 factors were considered and most relevant factors were selectively chosen using correlation coefficient and delta value. Our results show that 11 factors (3 emotions, 7 behaviors and 1 location factor) are relevant to inferring user\u27s desire. Finally, we have established an evaluation environment within the Smart Home Lab to validate our approach. In order to train and verify the desire inference model, multiple stimuli are provided to induce user\u27s desires and pilot data are collected during the experiments. For evaluation, we used the recall and precision methodology, which are basic measures. As a result, average precision was calculated to be 85% for human desire inference and 81% for Think-Aloud

Proceedings of the 11th European Agent Systems Summer School Student Session

This volume contains the papers presented at the Student Session of the 11th European Agent Systems Summer School (EASSS) held on 2nd of September 2009 at Educatorio della Providenza, Turin, Italy. The Student Session, organised by students, is designed to encourage student interaction and feedback from the tutors. By providing the students with a conference-like setup, both in the presentation and in the review process, students have the opportunity to prepare their own submission, go through the selection process and present their work to each other and their interests to their fellow students as well as internationally leading experts in the agent field, both from the theoretical and the practical sector. Table of Contents: Andrew Koster, Jordi Sabater Mir and Marco Schorlemmer, Towards an inductive algorithm for learning trust alignment . . . 5; Angel Rolando Medellin, Katie Atkinson and Peter McBurney, A Preliminary Proposal for Model Checking Command Dialogues. . . 12; Declan Mungovan, Enda Howley and Jim Duggan, Norm Convergence in Populations of Dynamically Interacting Agents . . . 19; Akın Günay, Argumentation on Bayesian Networks for Distributed Decision Making . . 25; Michael Burkhardt, Marco Luetzenberger and Nils Masuch, Towards Toolipse 2: Tool Support for the JIAC V Agent Framework . . . 30; Joseph El Gemayel, The Tenacity of Social Actors . . . 33; Cristian Gratie, The Impact of Routing on Traffic Congestion . . . 36; Andrei-Horia Mogos and Monica Cristina Voinescu, A Rule-Based Psychologist Agent for Improving the Performances of a Sportsman . . . 39; --Autonomer Agent,Agent,Künstliche Intelligenz

Allostatic self-efficacy: a metacognitive theory of dyshomeostasis-induced fatigue and depression

This paper outlines a hierarchical Bayesian framework for interoception, homeostatic/allostatic control, and meta-cognition that connects fatigue and depression to the experience of chronic dyshomeostasis. Specifically, viewing interoception as the inversion of a generative model of viscerosensory inputs allows for a formal definition of dyshomeostasis (as chronically enhanced surprise about bodily signals, or, equivalently, low evidence for the brain's model of bodily states) and allostasis (as a change in prior beliefs or predictions which define setpoints for homeostatic reflex arcs). Critically, we propose that the performance of interoceptive-allostatic circuitry is monitored by a metacognitive layer that updates beliefs about the brain's capacity to successfully regulate bodily states (allostatic self-efficacy). In this framework, fatigue and depression can be understood as sequential responses to the interoceptive experience of dyshomeostasis and the ensuing metacognitive diagnosis of low allostatic self-efficacy. While fatigue might represent an early response with adaptive value (cf. sickness behavior), the experience of chronic dyshomeostasis may trigger a generalized belief of low self-efficacy and lack of control (cf. learned helplessness), resulting in depression. This perspective implies alternative pathophysiological mechanisms that are reflected by differential abnormalities in the effective connectivity of circuits for interoception and allostasis. We discuss suitably extended models of effective connectivity that could distinguish these connectivity patterns in individual patients and may help inform differential diagnosis of fatigue and depression in the future

Dynamic Occupancy Grid Prediction for Urban Autonomous Driving: A Deep Learning Approach with Fully Automatic Labeling

Long-term situation prediction plays a crucial role in the development of intelligent vehicles. A major challenge still to overcome is the prediction of complex downtown scenarios with multiple road users, e.g., pedestrians, bikes, and motor vehicles, interacting with each other. This contribution tackles this challenge by combining a Bayesian filtering technique for environment representation, and machine learning as long-term predictor. More specifically, a dynamic occupancy grid map is utilized as input to a deep convolutional neural network. This yields the advantage of using spatially distributed velocity estimates from a single time step for prediction, rather than a raw data sequence, alleviating common problems dealing with input time series of multiple sensors. Furthermore, convolutional neural networks have the inherent characteristic of using context information, enabling the implicit modeling of road user interaction. Pixel-wise balancing is applied in the loss function counteracting the extreme imbalance between static and dynamic cells. One of the major advantages is the unsupervised learning character due to fully automatic label generation. The presented algorithm is trained and evaluated on multiple hours of recorded sensor data and compared to Monte-Carlo simulation