11,733 research outputs found
Exploiting Deep Semantics and Compositionality of Natural Language for Human-Robot-Interaction
We develop a natural language interface for human robot interaction that
implements reasoning about deep semantics in natural language. To realize the
required deep analysis, we employ methods from cognitive linguistics, namely
the modular and compositional framework of Embodied Construction Grammar (ECG)
[Feldman, 2009]. Using ECG, robots are able to solve fine-grained reference
resolution problems and other issues related to deep semantics and
compositionality of natural language. This also includes verbal interaction
with humans to clarify commands and queries that are too ambiguous to be
executed safely. We implement our NLU framework as a ROS package and present
proof-of-concept scenarios with different robots, as well as a survey on the
state of the art
Affect and believability in game characters:a review of the use of affective computing in games
Virtual agents are important in many digital environments. Designing a character that highly engages users in terms of interaction is an intricate task constrained by many requirements. One aspect that has gained more attention recently is the effective dimension of the agent. Several studies have addressed the possibility of developing an affect-aware system for a better user experience. Particularly in games, including emotional and social features in NPCs adds depth to the characters, enriches interaction possibilities, and combined with the basic level of competence, creates a more appealing game. Design requirements for emotionally intelligent NPCs differ from general autonomous agents with the main goal being a stronger player-agent relationship as opposed to problem solving and goal assessment. Nevertheless, deploying an affective module into NPCs adds to the complexity of the architecture and constraints. In addition, using such composite NPC in games seems beyond current technology, despite some brave attempts. However, a MARPO-type modular architecture would seem a useful starting point for adding emotions
Robotic ubiquitous cognitive ecology for smart homes
Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent- based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feed- back received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work
Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms
The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications
Engineering Multiagent Systems - Reflections
This report documents the programme and outcomes of Dagstuhl Seminar 12342 ``Engineering multiagent Systems\u27\u27. The seminar brought together researchers from both academia and industry to identify the potential for and facilitate convergence towards standards for agent technology. As such it was particularly relevant to industrial research.
A key objective of the seminar, moreover, has been to establish a road map for engineering multiagent systems. Various research areas have been identified as important topics for a research agenda with a focus on the development of multiagent systems. Among others, these include the integration of agent technology and legacy systems, component-based agent design, standards for tooling, establishing benchmarks for agent technology, and the development of frameworks for coordination and organisation of multiagent systems. This report presents a more detailed discussion of these and other research challenges that were identified.
The unique atmosphere of Dagstuhl provided the perfect environment for leading researchers from a wide variety of backgrounds to discuss future directions in programming languages, tools and platforms for multiagent systems, and the road map produced by the seminar will have a timely and decisive impact on the future of this whole area of research
A knowledge-based approach towards human activity recognition in smart environments
For many years it is known that the population of older persons is on the rise. A recent report estimates that globally, the share of the population aged 65 years or over is expected to increase from 9.3 percent in 2020 to around 16.0 percent in 2050 [1]. This point has been one of the main sources of motivation for active research in the domain of human
activity recognition in smart-homes. The ability to perform ADL without assistance from
other people can be considered as a reference for the estimation of the independent living
level of the older person. Conventionally, this has been assessed by health-care domain
experts via a qualitative evaluation of the ADL. Since this evaluation is qualitative, it can
vary based on the person being monitored and the caregiver\u2019s experience. A significant
amount of research work is implicitly or explicitly aimed at augmenting the health-care
domain expert\u2019s qualitative evaluation with quantitative data or knowledge obtained from
HAR. From a medical perspective, there is a lack of evidence about the technology readiness
level of smart home architectures supporting older persons by recognizing ADL [2]. We
hypothesize that this may be due to a lack of effective collaboration between smart-home
researchers/developers and health-care domain experts, especially when considering HAR.
We foresee an increase in HAR systems being developed in close collaboration with caregivers
and geriatricians to support their qualitative evaluation of ADL with explainable quantitative
outcomes of the HAR systems. This has been a motivation for the work in this thesis. The
recognition of human activities \u2013 in particular ADL \u2013 may not only be limited to support
the health and well-being of older people. It can be relevant to home users in general. For
instance, HAR could support digital assistants or companion robots to provide contextually
relevant and proactive support to the home users, whether young adults or old. This has also
been a motivation for the work in this thesis.
Given our motivations, namely, (i) facilitation of iterative development and ease in collaboration between HAR system researchers/developers and health-care domain experts in ADL,
and (ii) robust HAR that can support digital assistants or companion robots. There is a need
for the development of a HAR framework that at its core is modular and flexible to facilitate
an iterative development process [3], which is an integral part of collaborative work that involves develop-test-improve phases. At the same time, the framework should be intelligible
for the sake of enriched collaboration with health-care domain experts. Furthermore, it
should be scalable, online, and accurate for having robust HAR, which can enable many
smart-home applications. The goal of this thesis is to design and evaluate such a framework.
This thesis contributes to the domain of HAR in smart-homes. Particularly the contribution can be divided into three parts. The first contribution is Arianna+, a framework to develop
networks of ontologies - for knowledge representation and reasoning - that enables smart
homes to perform human activity recognition online. The second contribution is OWLOOP,
an API that supports the development of HAR system architectures based on Arianna+. It
enables the usage of Ontology Web Language (OWL) by the means of Object-Oriented
Programming (OOP). The third contribution is the evaluation and exploitation of Arianna+
using OWLOOP API. The exploitation of Arianna+ using OWLOOP API has resulted in four
HAR system implementations. The evaluations and results of these HAR systems emphasize
the novelty of Arianna+
- …