As an alternative to social co-robots, or collaborative robots, displacing workers in the workforce, social robots may, in the future, be used to augment workers with intellectual and developmental disabilities (IDD) and improve their work environments. We are developing AIDA, a co-robot to uplift and augment the abilities of workers who have IDD and work in a light manufacturing facility. This paper presents the design process for discovering the workers' needs and a description of our works-in-progress prototype social co-robot, AIDA, an artificially intelligent disability aide designed to function safely and securely alongside these workers.

The ever increasing amount of personal data accumulated by companies offering innovative services through the cloud, Internet of Things devices and, more recently, social robots has started to alert consumers and legislative authorities. In the advent of the first modern laws trying to protect user privacy, such as the European Union General Data Protection Regulation, it is still unclear what are the tools and techniques that the industry should employ to comply with regulations in a transparent and cost effective manner. We propose an architecture for a public blockchain based ledger that can provide strong evidence of policy compliance. To address scalability concerns, we define a new type of off-chain channel that is based on general state channels and offers verification for information external to the blockchain. We also create a model of the business relationships in a smart home setup that includes a social robot and suggest a sticky policy mechanism to monitor cross-boundary policy compliance.

The objective of the panel is to discuss how to enhance diversity in software engineering programs. Brief possition statements from the panelists are provided. A complete record of the panel discussion was not made available for publication as part of the conference proceedings.

In order for humanoid robots to have believable conversations with humans, the robots will need a reliable method for detecting the topics shared in the interaction to formulate a relevant response. This paper presents a novel application of intelligent indexing and ontology analysis for use in conversational topic detection for human-robot interaction. We evaluate a method for training on a corpus of transcribed phone conversations and using a concept association matrix to determine the strongest common-sense linkages to words in the conversation. This model is placed within the conversation and emotion interface of our humanoid robot, MU-L8, and tested with users. Evaluation is performed both computationally, with the corpus, and perceptually with users, and the promising results are presented in this paper.

In the near future, humanoid robots may be available to act as personal health coaches that can socially interact and exercise with people to increase their physical activity and improve their nutritional habits. Although there has been work to demonstrate the long-term effects of using a robot to motivate and record exercise and nutrition data, we are developing a social and mobile humanoid health coach that will explain and perform the physical exercises along with the human in an effort to increase their physical activity. In this paper, we describe a pilot study to compare the effects on young adults of coaching delivered by a social and mobile humanoid robot health coach versus a human health coach. While data analysis bore out no significant statistical effect of coach type on daily activity level, the results demonstrated encouraging trends and suggest further research with a larger sample size.

In the future, humans will have robot friends that will need to have social and computational awareness for intuitive human-robot interactions. In this paper, we demonstrate a method for incorporating computational awareness in a humanoid robot comedian scenario that uses tactile feedback to respond to the audience. Our findings indicate that incorporating computational awareness in a humanoid robot must also take into account the users' ability to detect responsiveness in a robot.

Many realistic problem domains are composed of heterogeneous tasks distributed in a physical environment. A team of mobile agents has to autonomously allocate these tasks, navigate to them and finally execute them. Recently auctions have been used for task allocation among homogeneous agents. Less studied is the case of allocation where both the tasks and the agents are heterogeneous in nature. In this paper, we investigate the market-based allocation of heterogeneous tasks to heterogeneous agents in domains where the distribution of the task heterogeneity is known a priori. We present a model of task heterogeneity, and define a metric that allows us to assess the fitness of a team for a particular task domain. We then present a sequential, round-based, auction setup for allocating heterogeneous tasks to heterogeneous teams and empirically investigate the performance of three different allocation strategies.

A successful collaboration between Spelman College and Carnegie Mellon University led to an NSF-funded Broadening Participation in Computing project to set up robotics education laboratories and introduce undergraduate instruction in cognitive robotics at three other Historically Black Colleges and Universities (HBCUs). We give a brief overview of cognitive robotics and the Tekkotsu software architecture, and describe our experiences teaching computer science students with no previous robotics exposure to program sophisticated mobile robots.

In this paper, we propose a method for multi-robot task allocation based on the concept of task decomposition for a lunar mission scenario. This methodology focuses on segmenting a task scenario into a sequence of operations called functional primitives that are defined a priori by a set of performance metrics and resource requirements. In real-time, multiple robotic agents determine their capabilities and skill sets associated with the defined functional primitives in order to determine a suitable allocation scheme. We discuss the methodology in detail and provide results for a simulated lunar mission construction scenario using the Multi-Agent Robot Simulator for Lunar Construction (MARS-LC) system.

Agent technologies represent a promising approach for the integration of interorganizational capabilities across distributed, networked environments. However, knowledge sharing interoperability problems can arise when agents incorporating differing ontologies try to synchronize their internal information. Moreover, in practice, agents may not have a common or global consensus ontology that will facilitate knowledge sharing and integration of functional capabilities. We propose a method to enable agents to develop a local consensus ontology during operation time as needed. By identifying similarities in the ontologies of their peer agents, a set of agents can discover new concepts/relations and integrate them into a local consensus ontology on demand. We evaluate this method, both syntactically and semantically, when forming local consensus ontologies with and without the use of a lexical database. We also report on the effects when several factors, such as the similarity measure, the relation search level depth, and the merge order, are varied. Finally, experimenting in the domain of agent-supported Web service composition, we demonstrate how our method allows us to successfully autonomously form service-oriented local consensus ontologies.

Abstract As digital human animation and simulation systems are coming into widespread use, scenario designers and digital animators must introduce specific tasks, procedures and animation sequences into a digital human's repertoire of capabilities. The parameterized action representation (PAR) architecture allows the animator to introduce dynamic behaviors through natural language instructions. However, this architecture lacks any natural language feedback which would allow the animator to deal with inconsistencies, incompleteness and conflicts within the behavior. In this paper we introduce an extension to a typical PAR architecture that provides that natural language feedback mechanism and define a set of task oriented discourses (TOD) that allow this mechanism to be used in synthesizing and managing behaviors. We demonstrate our behavior development techniques in two applications. Copyright © 2004 John Wiley & Sons, Ltd.

Multiagent learning is deeply rooted in single-agent learning. It is common thought that multiagent learning has a better result than single-agent learning with communication and knowledge sharing. This paper gives a different result in the robot foraging domain with multiagent and single-agent reinforcement learning methods. We show how a single-agent reinforcement learning method performs better than various multiagent reinforcement learning methods. Thus we propose a hypothesis: In normal robot foraging tasks with reinforcement learning, single-agent reinforcement learning is better that any multiagent reinforcement learning.

New methods of electronic collaboration are needed to manage and reconcile the vast scientific knowledge made available through the experience of diverse experts. Fundamental research in capturing, managing, analyzing, and explaining information and making it available for use is seen as a high priority for software research worldwide. Therefore, we have developed a method and software for intelligent-agent assisted knowledge fusion systems to address these needs. This system utilizes intelligent agents to facilitate the construction and fusion of knowledge from distributed human experts working on a common knowledge-sharing task. We describe our multi-agent system prototype, the Internet-Based Distributed Ophthalmology Consensus System (I-DOCS) and how we are employing it for finding disease genes. We also describe some results of using I-DOCS with clinicians attempting to develop a common clinical nomenclature and classification system for disease subtypes.