Towards Learning ‘Self’ and Emotional Knowledge in Social and Cultural Human-Agent Interactions

Original article can be found at: http://www.igi-global.com/articles/details.asp?ID=35052 Copyright IGI. Posted by permission of the publisher.This article presents research towards the development of a virtual learning environment (VLE) inhabited by intelligent virtual agents (IVAs) and modeling a scenario of inter-cultural interactions. The ultimate aim of this VLE is to allow users to reflect upon and learn about intercultural communication and collaboration. Rather than predefining the interactions among the virtual agents and scripting the possible interactions afforded by this environment, we pursue a bottomup approach whereby inter-cultural communication emerges from interactions with and among autonomous agents and the user(s). The intelligent virtual agents that are inhabiting this environment are expected to be able to broaden their knowledge about the world and other agents, which may be of different cultural backgrounds, through interactions. This work is part of a collaborative effort within a European research project called eCIRCUS. Specifically, this article focuses on our continuing research concerned with emotional knowledge learning in autobiographic social agents.Peer reviewe

Building long-term relationships with virtual and robotic characters: the role of remembering

With the recent advances, today people are able to communicate with embodied (virtual/robotic) entities using natural ways of communication. In order to use them in our daily lives, they need to be intelligent enough to make long-term relationships with us and this is highly challenging. Previous work on long-term interaction frequently reported that after the novelty effect disappeared, users' interest into the interaction decreased with time. Our primary goal in this study was to develop a system that can still keep the attention of the users after the first interaction. Incorporating the notion of time, we think that the key to long-term interaction is the recall of past memories during current conversation. For this purpose, we developed a long-term interaction framework with remembering and dialogue planning capability. In order to see the effect of remembering on users, we designed a tutoring application and measured the changes in social presence and task engagement levels according to the existence of memory. Different from previous work, users' interest in our system did not decrease with time with the important contributions of remembering to the engagement level of user

Recognizing emotional state of user based on learning method and conceptual memories

With the increased use of computers, electronic devices and human interaction with computer in the broad spectrum of human life, the role of controlling emotions and increasing positive emotional states becomes more prominent. If a user's negative emotions increase, his/her efficiency will decrease greatly as well. Research has shown that colors are to be considered as one of the most influential basic functions in sight, identification, interpretation, perception and senses. It can be said that colors have impact on individuals' emotional states and can change them. In this paper, by learning the reactions of users with different personality types against each color, communication between the user's emotional states and personality and colors were modeled for the variable "emotional control". For the sake of learning, we used a memory-based system with the user’s interface color changing in accordance with the positive and negative experiences of users with different personalities. The end result of comparison of the testing methods demonstrated the superiority of memory-based learning in all three parameters of emotional control, enhancement of positive emotional states and reduction of negative emotional states. Moreover, the accuracy of memory- based learning method was almost 70 percent

Memory and mental time travel in humans and social robots.

From neuroscience, brain imaging and the psychology of memory, we are beginning to assemble an integrated theory of the brain subsystems and pathways that allow the compression, storage and reconstruction of memories for past events and their use in contextualizing the present and reasoning about the future-mental time travel (MTT). Using computational models, embedded in humanoid robots, we are seeking to test the sufficiency of this theoretical account and to evaluate the usefulness of brain-inspired memory systems for social robots. In this contribution, we describe the use of machine learning techniques-Gaussian process latent variable models-to build a multimodal memory system for the iCub humanoid robot and summarize results of the deployment of this system for human-robot interaction. We also outline the further steps required to create a more complete robotic implementation of human-like autobiographical memory and MTT. We propose that generative memory models, such as those that form the core of our robot memory system, can provide a solution to the symbol grounding problem in embodied artificial intelligence. This article is part of the theme issue 'From social brains to social robots: applying neurocognitive insights to human-robot interaction'.Funding. The preparation of this chapter was supported by funding from the EU Seventh Framework Programme as part of the projects Experimental Functional Android Assistant (EFAA, FP7-ICT-270490) and What You Say Is What You Did (WYSIWYD, FP7-ICT-612139) and by the EU H2020 Programme as part of the Human Brain Project (HBP-SGA1, 720270; HBP-SGA2, 785907). Acknowledgements. The authors are grateful to Paul Verschure, Peter Dominey, Giorgio Metta, Yiannis Demiris and the other members of the WYSIWYD and EFAA consortia; to members of the HBP EPISENSE group; and to our colleagues at the University of Sheffield who have helped us to develop memory systems for the iCub, particularly Luke Boorman, Harry Jackson and Matthew Evans. The Sheffield iCub was purchased with the support of the UK Engineering and Physical Sciences Research Council (EPSRC)

Social Cognition for Human-Robot Symbiosis—Challenges and Building Blocks

The next generation of robot companions or robot working partners will need to satisfy social requirements somehow similar to the famous laws of robotics envisaged by Isaac Asimov time ago (Asimov, 1942). The necessary technology has almost reached the required level, including sensors and actuators, but the cognitive organization is still in its infancy and is only partially supported by the current understanding of brain cognitive processes. The brain of symbiotic robots will certainly not be a “positronic” replica of the human brain: probably, the greatest part of it will be a set of interacting computational processes running in the cloud. In this article, we review the challenges that must be met in the design of a set of interacting computational processes as building blocks of a cognitive architecture that may give symbiotic capabilities to collaborative robots of the next decades: (1) an animated body-schema; (2) an imitation machinery; (3) a motor intentions machinery; (4) a set of physical interaction mechanisms; and (5) a shared memory system for incremental symbiotic development. We would like to stress that our approach is totally un-hierarchical: the five building blocks of the shared cognitive architecture are fully bi-directionally connected. For example, imitation and intentional processes require the “services” of the animated body schema which, on the other hand, can run its simulations if appropriately prompted by imitation and/or intention, with or without physical interaction. Successful experiences can leave a trace in the shared memory system and chunks of memory fragment may compete to participate to novel cooperative actions. And so on and so forth. At the heart of the system is lifelong training and learning but, different from the conventional learning paradigms in neural networks, where learning is somehow passively imposed by an external agent, in symbiotic robots there is an element of free choice of what is worth learning, driven by the interaction between the robot and the human partner. The proposed set of building blocks is certainly a rough approximation of what is needed by symbiotic robots but we believe it is a useful starting point for building a computational framework