Programming Robosoccer agents by modelling human behavior

The Robosoccer simulator is a challenging environment for artificial intelligence, where a human has to program a team of agents and introduce it into a soccer virtual environment. Most usually, Robosoccer agents are programmed by hand. In some cases, agents make use of Machine learning (ML) to adapt and predict the behavior of the opposite team, but the bulk of the agent has been preprogrammed. The main aim of this paper is to transform Robosoccer into an interactive game and let a human control a Robosoccer agent. Then ML techniques can be used to model his/her behavior from training instances generated during the play. This model will be used later to control a Robosoccer agent, thus imitating the human behavior. We have focused our research on low-level behavior, like looking for the ball, conducting the ball towards the goal, or scoring in the presence of opponent players. Results have shown that indeed, Robosoccer agents can be controlled by programs that model human play.Publicad

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Using Natural Language as Knowledge Representation in an Intelligent Tutoring System

Knowledge used in an intelligent tutoring system to teach students is usually acquired from authors who are experts in the domain. A problem is that they cannot directly add and update knowledge if they don’t learn formal language used in the system. Using natural language to represent knowledge can allow authors to update knowledge easily. This thesis presents a new approach to use unconstrained natural language as knowledge representation for a physics tutoring system so that non-programmers can add knowledge without learning a new knowledge representation. This approach allows domain experts to add not only problem statements, but also background knowledge such as commonsense and domain knowledge including principles in natural language. Rather than translating into a formal language, natural language representation is directly used in inference so that domain experts can understand the internal process, detect knowledge bugs, and revise the knowledgebase easily. In authoring task studies with the new system based on this approach, it was shown that the size of added knowledge was small enough for a domain expert to add, and converged to near zero as more problems were added in one mental model test. After entering the no-new-knowledge state in the test, 5 out of 13 problems (38 percent) were automatically solved by the system without adding new knowledge

An Intelligent Robot and Augmented Reality Instruction System

Human-Centered Robotics (HCR) is a research area that focuses on how robots can empower people to live safer, simpler, and more independent lives. In this dissertation, I present a combination of two technologies to deliver human-centric solutions to an important population. The first nascent area that I investigate is the creation of an Intelligent Robot Instructor (IRI) as a learning and instruction tool for human pupils. The second technology is the use of augmented reality (AR) to create an Augmented Reality Instruction (ARI) system to provide instruction via a wearable interface. To function in an intelligent and context-aware manner, both systems require the ability to reason about their perception of the environment and make appropriate decisions. In this work, I construct a novel formulation of several education methodologies, particularly those known as response prompting, as part of a cognitive framework to create a system for intelligent instruction, and compare these methodologies in the context of intelligent decision making using both technologies. The IRI system is demonstrated through experiments with a humanoid robot that uses object recognition and localization for perception and interacts with students through speech, gestures, and object interaction. The ARI system uses augmented reality, computer vision, and machine learning methods to create an intelligent, contextually aware instructional system. By using AR to teach prerequisite skills that lend themselves well to visual, augmented reality instruction prior to a robot instructor teaching skills that lend themselves to embodied interaction, I am able to demonstrate the potential of each system independently as well as in combination to facilitate students\u27 learning. I identify people with intellectual and developmental disabilities (I/DD) as a particularly significant use case and show that IRI and ARI systems can help fulfill the compelling need to develop tools and strategies for people with I/DD. I present results that demonstrate both systems can be used independently by students with I/DD to quickly and easily acquire the skills required for performance of relevant vocational tasks. This is the first successful real-world application of response-prompting for decision making in a robotic and augmented reality intelligent instruction system

Robot Assisted 3D Block Building to Augment Spatial Visualization Skills in Children - An exploratory study

The unique social presence of robots can be leveraged in learning situations to increase comfortability and engagement of kids, while still providing instructional guidance. When and how to interfere to provide feedback on their mistakes is still not fully clear. One effective feedback strategy used by human tutors is to implicitly inform the students of their errors rather than explicitly providing corrective feedback. This essay explores if and how a social robot can be utilized to provide implicit feedback to a user who is performing spatial visualization tasks. We explore impact of implicit and explicit feedback strategies on user's learning gains, self-regulation and perception of robot during 3D block building tasks in one-on-one child-robot tutoring. We demonstrate a realtime system that tracks the assembly of a 3D block structure using a RealSense RGB-D camera. The system allows three control actions: Add, Remove and Adjust on blocks of four basic colors to manipulate the structure in the play area. 3D structures can be authored in the Learning mode for system to record, and tracking enables the robot to provide selected feedback in the Teaching mode depending on the type of mistake made by the user. Proposed system can detect five types of mistakes i.e., mistake in: shape, color, orientation, level from base and position of the block. The feedback provided by the robot is based on mistake made by the user. Either implicit or explicit feedback, chosen randomly, is narrated by the robot. Various feedback statements are designed to implicitly inform the user of the mistake made. Two robot behaviours have been designed to support the effective delivery of feedback statements i.e., nodding and referential gaze. We conducted an exploratory study to evaluate our robot assisted 3D block building system to augment spatial visualization skills with one participant. We found that the system was easy to use. The robot was perceived as trustworthy, fun and interesting. Intentions of the robot are communicated through feedback statements and its behaviour. Our goal is to explore that the suggestion of mistakes in implicit ways can help the users self-regulate and scaffold their learning processes

eXtended Reality for Education and Training

L'abstract è presente nell'allegato / the abstract is in the attachmen

Designing Embodied Interactive Software Agents for E-Learning: Principles, Components, and Roles

Embodied interactive software agents are complex autonomous, adaptive, and social software systems with a digital embodiment that enables them to act on and react to other entities (users, objects, and other agents) in their environment through bodily actions, which include the use of verbal and non-verbal communicative behaviors in face-to-face interactions with the user. These agents have been developed for various roles in different application domains, in which they perform tasks that have been assigned to them by their developers or delegated to them by their users or by other agents. In computer-assisted learning, embodied interactive pedagogical software agents have the general task to promote human learning by working with students (and other agents) in computer-based learning environments, among them e-learning platforms based on Internet technologies, such as the Virtual Linguistics Campus (www.linguistics-online.com). In these environments, pedagogical agents provide contextualized, qualified, personalized, and timely assistance, cooperation, instruction, motivation, and services for both individual learners and groups of learners. This thesis develops a comprehensive, multidisciplinary, and user-oriented view of the design of embodied interactive pedagogical software agents, which integrates theoretical and practical insights from various academic and other fields. The research intends to contribute to the scientific understanding of issues, methods, theories, and technologies that are involved in the design, implementation, and evaluation of embodied interactive software agents for different roles in e-learning and other areas. For developers, the thesis provides sixteen basic principles (Added Value, Perceptible Qualities, Balanced Design, Coherence, Consistency, Completeness, Comprehensibility, Individuality, Variability, Communicative Ability, Modularity, Teamwork, Participatory Design, Role Awareness, Cultural Awareness, and Relationship Building) plus a large number of specific guidelines for the design of embodied interactive software agents and their components. Furthermore, it offers critical reviews of theories, concepts, approaches, and technologies from different areas and disciplines that are relevant to agent design. Finally, it discusses three pedagogical agent roles (virtual native speaker, coach, and peer) in the scenario of the linguistic fieldwork classes on the Virtual Linguistics Campus and presents detailed considerations for the design of an agent for one of these roles (the virtual native speaker)

Cognitive neurorobotics and self in the shared world, a focused review of ongoing research

Through brain-inspired modeling studies, cognitive neurorobotics aims to resolve dynamics essential to different emergent phenomena at the level of embodied agency in an object environment shared with human beings. This article is a review of ongoing research focusing on model dynamics associated with human self-consciousness. It introduces the free energy principle and active inference in terms of Bayesian theory and predictive coding, and then discusses how directed inquiry employing analogous models may bring us closer to representing the sense of self in cognitive neurorobots. The first section quickly locates cognitive neurorobotics in the broad field of computational cognitive modeling. The second section introduces principles according to which cognition may be formalized, and reviews cognitive neurorobotics experiments employing such formalizations. The third section interprets the results of these and other experiments in the context of different senses of self, both “minimal” and “narrative” self. The fourth section considers model validity and discusses what we may expect ongoing cognitive neurorobotics studies to contribute to scientific explanation of cognitive phenomena including the senses of minimal and narrative self

Proceedings of the Workshop on NLG for Human–Robot Interaction

Foster ME, Buschmeier H, Dimitra G, eds. Proceedings of the Workshop on NLG for Human–Robot Interaction. Stroudsburg, PA, USA: Association for Computational Linguistics; 2018