Lora Weiss

Performance based task assignment in multi-robot patrolling

This article applies a performance metric to the multi-robot patrolling task to more efficiently distribute patrol areas among robot team members. The multi-robot patrolling task employs multiple robots to perform frequent visits to known areas in an environment, while minimizing the time between node visits. Conventional strategies for performing this task assume that the robots will perform as expected and do not address situations in which some team members patrol inefficiently. However, reliable performance of team members may not always be a valid assumption. This paper considers an approach for monitoring robot performance in a patrolling task and dynamically reassigning tasks from those team members that perform poorly. Experimental results from simulation and on a team of indoor robots demonstrate that in using this approach, tasks can be dynamically and more efficiently distributed in a multi-robot patrolling application.

Dynamic, cooperative multi-robot patrolling with a team of UAVs

The multi-robot patrolling task has practical relevance in surveillance, search and rescue, and security appli- cations. In this task, a team of robots must repeatedly visit areas in the environment, minimizing the time in-between visits to each. A team of robots can perform this task efficiently; however, challenges remain related to team formation and task assignment. This paper presents an approach for monitoring patrolling performance and dynamically adjusting the task assignment function based on observations of teammate performance. Experimental results are presented from realistic simulations of a cooperative patrolling scenario, using a team of UAVs.

Evaluating Counter-IED Strategies

Improvised explosive devices (IEDs) are one of the largest threats facing coalition forces in current military conflicts. The United States and other nations are greatly invested in mitigating these deadly devices. Past results have shown that completely technological counter-IED (cIED) efforts will be insufficient and, therefore, attention is focusing on augmenting the technological methods with neutralizing factors that contribute to human involvement in the IED perpetration process.To do so successfully requires an understanding of the behavioral aspects and influences of human involvement.This has led to an interest in socio-technical and systems-based models of terrorist activity. By integrating behavioral aspects of adversarial activities with computational methods, a greater understanding of these activities can be attained; simultaneously, potentially effective intervention points can be ascertained.This is often accomplished by modeling individuals, organizations, and societies via the creation of micro-, meso-, and macro-scale models to analyze and experiment with the impact of potential influences on population behavior. In addition to providing insight, model flexibility and model dynamics are required to assessmultiple interpretations of situations as they play out over time. Static models often cannot achieve this since disparate motivations and ideological factors evolve as a function of time. This article focuses on modeling the IED perpetration process, where knowledge was provided by subject matter experts (SMEs) from the United States and the United Kingdom, to ascertain behavioral aspects of cIED efforts. Defense & Security AnalysisVol. 27, No. 2, pp. 135–147, June 2011

A systems-level understanding of adversarial behaviour

Modelling behaviour related to the perpetration of improvised explosive devices is extremely complex. Behavioural aspects range from those who create a plan to those who gather supplies for developing the devices to those who passively look the other way. Developing computational approaches to understanding such behaviour necessitates either a decomposition of behavioural activity into smaller, manageable behaviours or generalising larger, group behaviour where gross trends are observed. This may suffice for particular applications; however, additional consideration can be given to developing more comprehensive approaches. Specifically, for those seeking to understand terrorism, a number of social, cultural and behavioural perspectives are being developed by experts worldwide. These perspectives may complement each other or they may be in conflict, but they equally contribute to a broader understanding. Our research is developing computational methods to analyse and experiment with differing views and perspectives of potential influences on adversarial behaviour at this system-level.

Model docking using knowledge-level analysis

This paper presents an initial approach for exploring the docking of social models at the knowledge level. We have prototyped a simple blackboard environment allowing for model docking experimentation. There are research challenges in identifying which models are appropriate to dock and the concepts that they should exchange to build a richer multi-scale view of the world. Our early approach includes docking of societal system dynamics models with individual and organizational behaviors represented in agent-based models. Case-based models allow exploration of historical knowledge by other models. Our research presents initial efforts to attain opportunistic, asynchronous interactions among multi-scale models through investigation and experimentation of knowledge-level model docking. A docked system can supply a multi-scale modeling capability to support a users what-if analysis through combinations of case-based modeling, system dynamics approaches and agent-based models working together. An example is provided for the domain of terrorist recruiting.

A network-based approach for assessing co-operating manned and unmanned systems (MUMS)

Traditionally, robots have been programmed to do precisely what their human operators instruct them to do, but more recently, they have become more sophisticated, intelligent, and autonomous. Once they reach a sufficiently high level of intelligent autonomy, they can support more collaborative interactions with each other and with people. As robots become more and more intelligent, we will begin designing systems where robots interact with humans, rather than designing robots that are commanded by people with continual oversight. One approach to assessing how humans and robots will interact in the future is to frame the problem as a collection of intelligent nodes. Multiple, collaborating, and interacting manned and robotic systems can be represented as a collection of dynamic, interacting nodes. This paper develops preliminary metrics to support understanding the extent of preferential attachment that would arise in a system of cooperating manned and unmanned systems (MUMS). The metrics seek to help explain if attachments are localized to specific situations or if they are more pervasive throughout a MUMS society.