34 research outputs found
Crisis Management Art from the Risks to the Control: A Review of Methods and Directions
A crisis is an exceptional event that causes damage and negative impacts on organizations. For this reason, crisis management is considered as a significant action needed to follow crisis causes and consequences for preventing or avoiding these exceptional events from occurring again. Studies have devoted their efforts to proposing methods, techniques, and approaches in the crisis management direction. As a result, it is critical to provide a consolidated study that has an integrated view of proposed crisis management methods, crisis impacts, and effective response strategies. For this purpose, this paper first highlights the proposed techniques used in crisis management and presents the main objective behind each technique. Second, the risks and impacts resulting from a crisis are highlighted. Finally, crisis response strategies are discussed. The major contribution of this study is it can guide researchers to define research gaps or new directions in crisis management and choose the proper techniques that cope with their research problems or help them discover new research problems
Plan Acquisition Through Intentional Learning in BDI Multi-Agent Systems
Multi-Agent Systems (MAS), a technique emanating from Distributed Artificial Intelligence, is a suitable technique to study complex systems. They make it possible to represent and simulate both elements and interrelations of systems in a variety of domains. The most commonly used approach to develop the individual components (agents) within MAS is reactive agency. However, other architectures, like cognitive agents, enable richer behaviours and interactions to be captured and modelled. The well-known Belief-Desire-Intentions architecture (BDI) is a robust approach to develop cognitive agents and it can emulate aspects of autonomous behaviour and is thus a promising tool to simulate social systems.
Machine Learning has been applied to improve the behaviour of agents both individually or collectively.
However, the original BDI model of agency, is lacking learning as part of its core functionalities. To cope with learning, the BDI agency has been extended by Intentional Learning (IL) operating at three levels: belief adjustment, plan selection, and plan acquisition. The latter makes it possible to increase the agent’s catalogue
of skills by generating new procedural knowledge to be used onwards.
The main contributions of this thesis are: a) the development of IL in a fully-fledged BDI framework at the plan acquisition level, b) extending IL from the single-agent case to the collective perspective; and c) a novel framework that melts reactive and BDI agents through integrating both MAS and Agent-Based Modelling approaches, it allows the configuration of diverse domains and environments. Learning is demonstrated in a test-bed environment to acquire a set of plans that drive the agent to exhibit behaviours such as target-searching and left-handed wall-following. Learning in both decision strata, single and collective, is tested in a more challenging and socially relevant environment: the Disaster-Rescue problem
Robotic Wireless Sensor Networks
In this chapter, we present a literature survey of an emerging, cutting-edge,
and multi-disciplinary field of research at the intersection of Robotics and
Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor
Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system
that aims to achieve certain sensing goals while meeting and maintaining
certain communication performance requirements, through cooperative control,
learning and adaptation. While both of the component areas, i.e., Robotics and
WSN, are very well-known and well-explored, there exist a whole set of new
opportunities and research directions at the intersection of these two fields
which are relatively or even completely unexplored. One such example would be
the use of a set of robotic routers to set up a temporary communication path
between a sender and a receiver that uses the controlled mobility to the
advantage of packet routing. We find that there exist only a limited number of
articles to be directly categorized as RWSN related works whereas there exist a
range of articles in the robotics and the WSN literature that are also relevant
to this new field of research. To connect the dots, we first identify the core
problems and research trends related to RWSN such as connectivity,
localization, routing, and robust flow of information. Next, we classify the
existing research on RWSN as well as the relevant state-of-the-arts from
robotics and WSN community according to the problems and trends identified in
the first step. Lastly, we analyze what is missing in the existing literature,
and identify topics that require more research attention in the future
Evidence Report, Risk of Inadequate Design of Human and Automation/Robotic Integration
The success of future exploration missions depends, even more than today, on effective integration of humans and technology (automation and robotics). This will not emerge by chance, but by design. Both crew and ground personnel will need to do more demanding tasks in more difficult conditions, amplifying the costs of poor design and the benefits of good design. This report has looked at the importance of good design and the risks from poor design from several perspectives: 1) If the relevant functions needed for a mission are not identified, then designs of technology and its use by humans are unlikely to be effective: critical functions will be missing and irrelevant functions will mislead or drain attention. 2) If functions are not distributed effectively among the (multiple) participating humans and automation/robotic systems, later design choices can do little to repair this: additional unnecessary coordination work may be introduced, workload may be redistributed to create problems, limited human attentional resources may be wasted, and the capabilities of both humans and technology underused. 3) If the design does not promote accurate understanding of the capabilities of the technology, the operators will not use the technology effectively: the system may be switched off in conditions where it would be effective, or used for tasks or in contexts where its effectiveness may be very limited. 4) If an ineffective interaction design is implemented and put into use, a wide range of problems can ensue. Many involve lack of transparency into the system: operators may be unable or find it very difficult to determine a) the current state and changes of state of the automation or robot, b) the current state and changes in state of the system being controlled or acted on, and c) what actions by human or by system had what effects. 5) If the human interfaces for operation and control of robotic agents are not designed to accommodate the unique points of view and operating environments of both the human and the robotic agent, then effective human-robot coordination cannot be achieved
COIN@AAMAS2015
COIN@AAMAS2015 is the nineteenth edition of the series and the fourteen papers included in these proceedings demonstrate the vitality of the community and will provide the grounds for a solid workshop program and what we expect will be a most enjoyable and enriching debate.Peer reviewe
CAMP-BDI: an approach for multiagent systems robustness through capability-aware agents maintaining plans
Rational agent behaviour is frequently achieved through the use of plans, particularly
within the widely used BDI (Belief-Desire-Intention) model for intelligent agents. As
a consequence, preventing or handling failure of planned activity is a vital component
in building robust multiagent systems; this is especially true in realistic environments,
where unpredictable exogenous change during plan execution may threaten intended
activities.
Although reactive approaches can be employed to respond to activity failure through
replanning or plan-repair, failure may have debilitative effects that act to stymie recovery
and, potentially, hinder subsequent activity. A further factor is that BDI agents typically
employ deterministic world and plan models, as probabilistic planning methods
are typical intractable in realistically complex environments. However, deterministic
operator preconditions may fail to represent world states which increase the risk of
activity failure.
The primary contribution of this thesis is the algorithmic design of the CAMP-BDI
(Capability Aware, Maintaining Plans) approach; a modification of the BDI reasoning
cycle which provides agents with beliefs and introspective reasoning to anticipate
increased risk of failure and pro-actively modify intended plans in response.
We define a capability meta-knowledge model, providing information to identify
and address threats to activity success using precondition modelling and quantitative
quality estimation. This also facilitates semantic-independent communication of capability
information for general advertisement and of dependency information - we define
use of the latter, within a structured messaging approach, to extend local agent algorithms
towards decentralized, distributed robustness. Finally, we define a policy based
approach for dynamic modification of maintenance behaviour, allowing response to
observations made during runtime and with potential to improve re-usability of agents
in alternate environments.
An implementation of CAMP-BDI is compared against an equivalent reactive system
through experimentation in multiple perturbation configurations, using a logistics
domain. Our empirical evaluation indicates CAMP-BDI has significant benefit if activity
failure carries a strong risk of debilitative consequence
Human factors of semi-autonomous robots for urban search and rescue
During major disasters or other emergencies, Urban Search and Rescue (USAR) teams are responsible for extricating casualties safely from collapsed urban structures. The rescue work is dangerous due to possible further collapse, fire, dust or electricity hazards. Sometimes the necessary precautions and checks can last several hours before rescuers are safe to start the search for survivors. Remote controlled rescue robots provide the opportunity to support human rescuers to search the site for trapped casualties while they remain in a safe place.
The research reported in this thesis aimed to understand how robot behaviour and interface design can be applied to utilise the benefits of robot autonomy and how to inform future human-robot collaborative systems. The data was analysed in the context of USAR missions when using semi-autonomous remote controlled robot systems. The research focussed on the influence of robot feedback, robot reliability, task complexity, and transparency. The influence of these factors on trust, workload, and performance was examined. The overall goal of the research was to make the life of rescuers safer and enhance their performance to help others in distress.
Data obtained from the studies conducted for this thesis showed that semi-autonomous robot reliability is still the most dominant factor influencing trust, workload, and team performance. A robot with explanatory feedback was perceived as more competent, more efficient and less malfunctioning. The explanatory feedback was perceived as a clearer type of communication compared to concise robot feedback. Higher levels of robot transparency were perceived as more trustworthy. However, single items on the trust questionnaire were manipulated and further investigation is necessary. However, neither explanatory feedback from the robot nor robot transparency, increased team performance or mediated workload levels.
Task complexity mainly influenced human-robot team performance and the participants’ control allocation strategy. Participants allowed the robot to find more targets and missed more robot errors in the high complexity conditions compared to the low task complexity conditions. Participants found more targets manually in the low complexity tasks.
In addition, the research showed that recording the observed robot performance (the performance of the robot that was witnessed by the participant) can help to identify the cause of contradicting results: participants might not have noticed some of the robots mistakes and therefore they were not able to distinguish between the robot reliability levels.
Furthermore, the research provided a foundation of knowledge regarding the real world application of USAR in the United Kingdom. This included collecting knowledge via an autoethnographic approach about working processes, command structures, currently used technical equipment, and attitudes of rescuers towards robots. Also, recommendations about robot behaviour and interface design were collected throughout the research.
However, recommendations made in the thesis include consideration of the overall outcome (mission performance) and the perceived usefulness of the system in order to support the uptake of the technology in real world applications. In addition, autonomous features might not be appropriate in all USAR applications. When semi-autonomous robot trials were compared to entirely manual operation, only the robot with an average of 97% reliability significantly increased the team performance and reduced the time needed to complete the USAR scenario compared to the manually operated robot. Unfortunately, such high robot success levels do not exist to date.
This research has contributed to our understanding of the factors influencing human-robot collaboration in USAR operations, and provided guidance for the next generation of autonomous robots
Human factors of semi-autonomous robots for urban search and rescue
During major disasters or other emergencies, Urban Search and Rescue (USAR) teams are responsible for extricating casualties safely from collapsed urban structures. The rescue work is dangerous due to possible further collapse, fire, dust or electricity hazards. Sometimes the necessary precautions and checks can last several hours before rescuers are safe to start the search for survivors. Remote controlled rescue robots provide the opportunity to support human rescuers to search the site for trapped casualties while they remain in a safe place.
The research reported in this thesis aimed to understand how robot behaviour and interface design can be applied to utilise the benefits of robot autonomy and how to inform future human-robot collaborative systems. The data was analysed in the context of USAR missions when using semi-autonomous remote controlled robot systems. The research focussed on the influence of robot feedback, robot reliability, task complexity, and transparency. The influence of these factors on trust, workload, and performance was examined. The overall goal of the research was to make the life of rescuers safer and enhance their performance to help others in distress.
Data obtained from the studies conducted for this thesis showed that semi-autonomous robot reliability is still the most dominant factor influencing trust, workload, and team performance. A robot with explanatory feedback was perceived as more competent, more efficient and less malfunctioning. The explanatory feedback was perceived as a clearer type of communication compared to concise robot feedback. Higher levels of robot transparency were perceived as more trustworthy. However, single items on the trust questionnaire were manipulated and further investigation is necessary. However, neither explanatory feedback from the robot nor robot transparency, increased team performance or mediated workload levels.
Task complexity mainly influenced human-robot team performance and the participants’ control allocation strategy. Participants allowed the robot to find more targets and missed more robot errors in the high complexity conditions compared to the low task complexity conditions. Participants found more targets manually in the low complexity tasks.
In addition, the research showed that recording the observed robot performance (the performance of the robot that was witnessed by the participant) can help to identify the cause of contradicting results: participants might not have noticed some of the robots mistakes and therefore they were not able to distinguish between the robot reliability levels.
Furthermore, the research provided a foundation of knowledge regarding the real world application of USAR in the United Kingdom. This included collecting knowledge via an autoethnographic approach about working processes, command structures, currently used technical equipment, and attitudes of rescuers towards robots. Also, recommendations about robot behaviour and interface design were collected throughout the research.
However, recommendations made in the thesis include consideration of the overall outcome (mission performance) and the perceived usefulness of the system in order to support the uptake of the technology in real world applications. In addition, autonomous features might not be appropriate in all USAR applications. When semi-autonomous robot trials were compared to entirely manual operation, only the robot with an average of 97% reliability significantly increased the team performance and reduced the time needed to complete the USAR scenario compared to the manually operated robot. Unfortunately, such high robot success levels do not exist to date.
This research has contributed to our understanding of the factors influencing human-robot collaboration in USAR operations, and provided guidance for the next generation of autonomous robots