Three Cases of Connectivity and Global Information Transfer in Robot Swarms

In this work we consider three different cases of robot-robot interactions and resulting global information transfer in robot swarms. These mechanisms define cooperative properties of the system and can be used for designing collective behavior. These three cases are demonstrated and discussed based on experiments in a swarm of microrobots "Jasmine"

Decentralised Coordination in RoboCup Rescue

Emergency responders are faced with a number of significant challenges when managing major disasters. First, the number of rescue tasks posed is usually larger than the number of responders (or agents) and the resources available to them. Second, each task is likely to require a different level of effort in order to be completed by its deadline. Third, new tasks may continually appear or disappear from the environment, thus requiring the responders to quickly recompute their allocation of resources. Fourth, forming teams or coalitions of multiple agents from different agencies is vital since no single agency will have all the resources needed to save victims, unblock roads, and extinguish the ?res which might erupt in the disaster space. Given this, coalitions have to be efficiently selected and scheduled to work across the disaster space so as to maximise the number of lives and the portion of the infrastructure saved. In particular, it is important that the selection of such coalitions should be performed in a decentralised fashion in order to avoid a single point of failure in the system. Moreover, it is critical that responders communicate only locally given they are likely to have limited battery power or minimal access to long range communication devices. Against this background, we provide a novel decentralised solution to the coalition formation process that pervades disaster management. More specifically, we model the emergency management scenario defined in the RoboCup Rescue disaster simulation platform as a Coalition Formation with Spatial and Temporal constraints (CFST) problem where agents form coalitions in order to complete tasks, each with different demands. In order to design a decentralised algorithm for CFST we formulate it as a Distributed Constraint Optimisation problem and show how to solve it using the state-of-the-art Max-Sum algorithm that provides a completely decentralised message-passing solution. We then provide a novel algorithm (F-Max-Sum) that avoids sending redundant messages and efficiently adapts to changes in the environment. In empirical evaluations, our algorithm is shown to generate better solutions than other decentralised algorithms used for this problem

Binary max-sum for multi-team task allocation in RoboCup Rescue

Coordination of agents involved in rescue missions is an important open research problem. We focus on the RoboCup Rescue Simulation (RCS) challenge, where different teams of agents perform urban rescue operations. Previous approaches typically cast such coordination problem as separate single-team allocation problems, and solve them separately. Our first key contribution is to focus on the max-sum approach, which has been successfully applied in this setting. We show that it is possible to reduce the computational complexity associated to max- sum from exponential to polynomial time. Our empirical evaluation shows that, by using our approach, the fire brigades team obtains significantly better results when compared to state-of- the-art approaches. Our second key contribution is a methodology that allows teams in RCS to make joint allocations. Specifically, our approach supports a modular design, where teams are independently modeled and subsequently connected via well-defined coordination points. To the best of our knowledge, this is the first task-assignment approach in the literature that enables teams in RCS to make simultaneous joint allocations. Experiments with fire brigades and police agents show that teams employing inter-team coordination are significantly more effective than uncoordinated teams.Work funded by projects RECEDIT (TIN2009-13591-C02- 02), AT (CSD2007-0022), COR (TIN2012-38876-C02-01), MECER (201250E053), the Generalitat of Catalunya grant 2009-SGR-1434, and the Ministry of Economy and Competitivity grant BES-2010-030466Peer Reviewe

Multiagent Teamwork: Hybrid Approaches

Conference paper published in CSI Communications</p

In-the-loop or on-the-loop? Interactional arrangements to support team coordination with a planning agent

In this paper we present the study of interactional arrangements that support the collaboration of headquarters (HQ), field responders and a computational planning agent in a time-critical task setting created by a mixed-reality game. Interactional arrangements define the extent to which control is distributed between the collaborative parties.We provide two field trials, one to study an “on-the-loop” arrangement in which HQ monitors and intervenes in agent instructions to field players on demand, and the other to study a version that places headquarters more tightly “in-the-loop”. The studies provide and understanding of the sociotechnical collaboration between players and the agent in these interactional arrangements, by conducting interaction analysis of video recordings and game log data. The first field trial focuses on the collaboration of field responders with the planning agent. Findings highlight how players negotiate the agent guidance within the social interaction of the collocated teams. The second field trial focuses on the collaboration between the automated planning agent and the headquarters. We find that the human coordinator and the agent can successfully work together in most cases, with human coordinators inspecting and ‘correcting’ the agent-proposed plans. Through this field trial-driven development process, we generalise interaction design implications of automated planning agents around the themes of supporting common ground and mixed-initiative planning

Human–agent collaboration for disaster response

In the aftermath of major disasters, first responders are typically overwhelmed with large numbers of, spatially distributed, search and rescue tasks, each with their own requirements. Moreover, responders have to operate in highly uncertain and dynamic environments where new tasks may appear and hazards may be spreading across the disaster space. Hence, rescue missions may need to be re-planned as new information comes in, tasks are completed, or new hazards are discovered. Finding an optimal allocation of resources to complete all the tasks is a major computational challenge. In this paper, we use decision theoretic techniques to solve the task allocation problem posed by emergency response planning and then deploy our solution as part of an agent-based planning tool in real-world field trials. By so doing, we are able to study the interactional issues that arise when humans are guided by an agent. Specifically, we develop an algorithm, based on a multi-agent Markov decision process representation of the task allocation problem and show that it outperforms standard baseline solutions. We then integrate the algorithm into a planning agent that responds to requests for tasks from participants in a mixed-reality location-based game, called AtomicOrchid, that simulates disaster response settings in the real-world. We then run a number of trials of our planning agent and compare it against a purely human driven system. Our analysis of these trials show that human commanders adapt to the planning agent by taking on a more supervisory role and that, by providing humans with the flexibility of requesting plans from the agent, allows them to perform more tasks more efficiently than using purely human interactions to allocate tasks. We also discuss how such flexibility could lead to poor performance if left unchecked