LeaF: A Learning-based Fault Diagnostic System for Multi-Robot Teams

The failure-prone complex operating environment of a standard multi-robot application dictates some amount of fault-tolerance to be incorporated into every system. In fact, the quality of the incorporated fault-tolerance has a direct impact on the overall performance of the system. Despite the extensive work being done in the field of multi-robot systems, there does not exist a general methodology for fault diagnosis and recovery. The objective of this research, in part, is to provide an adaptive approach that enables the robot team to autonomously detect and compensate for the wide variety of faults that could be experienced. The key feature of the developed approach is its ability to learn useful information from encountered faults, unique or otherwise, towards a more robust system. As part of this research, we analyzed an existing multi-agent architecture, CMM – Causal Model Method – as a fault diagnostic solution for a sample multi-robot application. Based on the analysis, we claim that a causal model approach is effective for anticipating and recovering from many types of robot team errors. However, the analysis also showed that the CMM method in its current form is incomplete as a turn-key solution. Due to the significant number of possible failure modes in a complex multi-robot application, and the difficulty in anticipating all possible failures in advance, one cannot guarantee the generation of a complete a priori causal model that identifies and specifies all faults that may occur in the system. Therefore, based on these preliminary studies, we designed an alternate approach, called LeaF: Learning based Fault diagnostic architecture for multi-robot teams. LeaF is an adaptive method that uses its experience to update and extend its causal model to enable the team, over time, to better recover from faults when they occur. LeaF combines the initial fault model with a case-based learning algorithm, LID – Lazy Induction of Descriptions — to allow robot team members to diagnose faults and to automatically update their causal models. The modified LID algorithm uses structural similarity between fault characteristics as a means of classifying previously un-encountered faults. Furthermore, the use of learning allows the system to identify and categorize unexpected faults, enable team members to learn from problems encountered by others, and make intelligent decisions regarding the environment. To evaluate LeaF, we implemented it in two challenging and dynamic physical multi-robot applications. The other significant contribution of the research is the development of metrics to measure the fault-tolerance, within the context of system performance, for a multi-robot system. In addition to developing these metrics, we also outline potential methods to better interpret the obtained measures towards truly understanding the capabilities of the implemented system. The developed metrics are designed to be application independent and can be used to evaluate and/or compare different fault-tolerance architectures like CMM and LeaF. To the best of our knowledge, this approach is the only one that attempts to capture the effect of intelligence, reasoning, or learning on the effective fault-tolerance of the system, rather than relying purely on traditional redundancy based measures. Finally, we show the utility of the designed metrics by applying them to the obtained physical robot experiments, measuring the effective fault-tolerance and system performance, and subsequently analyzing the calculated measures to help better understand the capabilities of LeaF

The control of search and rescue robots with the general suppression control framework

The paper described the use of the general suppression control framework (GSCF) for the control and coordination of a team of search and rescue robots undertaking exploration operation. This study adopts the biological analogy of the human immune system to derive the GSCF having the behavior of immunological cells. The framework directs the coordination of these robots in tackling search and rescue operations in an unstructured environment. Simulation study is performed to demonstrate the effectiveness of the control framework.published_or_final_versio

The accessibility of administrative processes: Assessing the impacts on students in higher education

Administrative processes that need to be completed to maintain a basic standard of living, to study, or to attain employment, are perceived to create burdens for disabled people. The navigation of information, forms, communications, and assessments to achieve a particular goal raises diverse accessibility issues. In this paper we explore the different types of impacts these processes have on disabled university students. We begin by surveying literature that highlights the systemic characteristics of administrative burdens and barriers for disabled people. We then describe how a participatory research exercise with students led to the development of a survey on these issues. This was completed by 104 respondents with a diverse range of declared disabilities. This provides evidence for a range of impacts, and understanding of the perceived level of challenge of commonly experienced processes. The most common negative impact reported was on stress levels. Other commonly reported impacts include exacerbation of existing conditions, time lost from study, and instances where support was not available in a timely fashion. Processes to apply for disability-related support were more commonly challenging than other types of processes. We use this research to suggest directions for improving accessibility and empowerment in this space

Unfreezing without Refreezing Change Management: Dilemmatic Roles of Agents in Succeeding the Bureaucracy Reform

The implementation of bureaucracy reform in many countries continues to experience various problems, in relation to the system, regulations and actors. The success of such reform shows the important role of the actors involved in promoting change agendas. Studies on bureaucracy reform have covered many aspects of the system, stages and factors that influence its success or failure. This study specifically analyses the aspect of the related actors, namely the role of change agents in the implementation of change management, who support the implementation of bureaucracy reform. Departing from theory regarding the role of agent of change and the stages of change management during the process of bureaucracy reform, the data collection was conducted through in-depth interviews with a number of stakeholders of National Development Planning Agency, Indonesia.  The qualitative data is processed using the Discourse Network Analyzer. The results show that there are three roles conducted by the agent of change in pushing reform agendas, namely as catalysts, solution givers and as process helper. To improve the performance of their roles, there are at least two attributes that they must have, i.e., skills and behavioral attributes, which both play a significant role in supporting the success of bureaucracy reform

Supporting Peer Help and Collaboration in Distributed Workplace Environments

Special Issue on Computer Supported Collaborative LearningIncreasingly, organizations are geographically distributed with activities coordinated and integrated through the use of information technology. Such organizations face constant change and the corresponding need for continual learning and renewal of their workers. In this paper we describe a prototype system called PHelpS (Peer Help System) that facilitates workers in carrying out such "life long learning". PHelpS supports workers as they perform their tasks, offers assistance in finding peer helpers when required, and mediates communication on task-related topics. When a worker runs into difficulty in carrying out a task, PHelpS provides a list of other workers who are ready, willing and able to help him or her. The worker then selects a particular helper with PHelpS supporting the subsequent help interaction. The PHelpS system acts as a facilitator to stimulate learning and collaboration, rather than as a directive agent imposing its perspectives on the workers. In this way PHelpS facilitates the creation of extensive informal peer help networks, where workers help one another with tasks and opens up new research avenues for further exploration of AI-based computer-supported collaborative learning. (http://aied.inf.ed.ac.uk/members98/archive/vol_9/greer/full.html

State of the art of a multi-agent based recommender system for active software engineering ontology

Software engineering ontology was first developed to provide efficient collaboration and coordination among distributed teams working on related software development projects across the sites. It helped to clarify the software engineering concepts and project information as well as enable knowledge sharing. However, a major challenge of the software engineering ontology users is that they need the competence to access and translate what they are looking for into the concepts and relations described in the ontology; otherwise, they may not be able to obtain required information. In this paper, we propose a conceptual framework of a multi-agent based recommender system to provide active support to access and utilize knowledge and project information in the software engineering ontology. Multi-agent system and semantic-based recommendation approach will be integrated to create collaborative working environment to access and manipulate data from the ontology and perform reasoning as well as generate expert recommendation facilities for dispersed software teams across the sites

CAPIR: Collaborative Action Planning with Intention Recognition

We apply decision theoretic techniques to construct non-player characters that are able to assist a human player in collaborative games. The method is based on solving Markov decision processes, which can be difficult when the game state is described by many variables. To scale to more complex games, the method allows decomposition of a game task into subtasks, each of which can be modelled by a Markov decision process. Intention recognition is used to infer the subtask that the human is currently performing, allowing the helper to assist the human in performing the correct task. Experiments show that the method can be effective, giving near-human level performance in helping a human in a collaborative game.Comment: 6 pages, accepted for presentation at AIIDE'1