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Paul Scerri

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30 papers
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30

JAAMAS Journal 2016 Journal Article

Interacting with team oriented plans in multi-robot systems

  • Alessandro Farinelli
  • Masoume M. Raeissi
  • Paul Scerri

Abstract Team oriented plans have become a popular tool for operators to control teams of autonomous robots to pursue complex objectives in complex environments. Such plans allow an operator to specify high level directives and allow the team to autonomously determine how to implement such directives. However, the operators will often want to interrupt the activities of individual team members to deal with particular situations, such as a danger to a robot that the robot team cannot perceive. Previously, after such interrupts, the operator would usually need to restart the team plan to ensure its success. In this paper, we present an approach to encoding how interrupts can be smoothly handled within a team plan. Building on a team plan formalism that uses Colored Petri Nets, we describe a mechanism that allows a range of interrupts to be handled smoothly, allowing the team to efficiently continue with its task after the operator intervention. We validate the approach with an application of robotic watercraft and show improved overall efficiency. In particular, we consider a situation where several platforms should travel through a set of pre-specified locations, and we identify three specific cases that require the operator to interrupt the plan execution: (i) a boat must be pulled out; (ii) all boats should stop the plan and move to a pre-specified assembly position; (iii) a set of boats must synchronize to traverse a dangerous area one after the other. Our experiments show that the use of our interrupt mechanism decreases the time to complete the plan (up to 48 % reduction) and decreases the operator load (up to 80 % reduction in number of user actions). Moreover, we performed experiments with real robotic platforms to validate the applicability of our mechanism in the actual deployment of robotic watercraft.

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AAMAS Conference 2013 Conference Paper

An Approach to Team Programming with Markup for Operator Interaction

  • Nathan Brooks
  • Ewart de Visser
  • Timur Chabuk
  • Elan Freedy
  • Paul Scerri

This paper presents a team plan specification language that combines work in the creation of generic team plans and design of intelligent interfaces. Two motivations the language are (1) to combine inter-agent cooperation and operator interaction of complex behaviors into a single plan, and (2) to separate plan design and UI design such that they are created by application domain experts and human interaction experts, respectively. The result is a generic language for multi-robot plans that defines tasks to be performed, interactions for maintaining situational awareness, and mixed initiative reactions to operator workload.

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AAMAS Conference 2012 Conference Paper

An Information Sharing Algorithm For Large Dynamic Mobile Multi-agent Teams

  • Linglong Zhu
  • Yang Xu
  • Paul Scerri
  • Han Liang

In large-scale multi-agent systems, communicating effectively is necessary for agents to cooperatively achieve joint goals. Despite significant progress on the multi-agent information sharing problem, existing research has not adequately dealt with the case of very large teams coordinating using a wireless network with changing team structure and density, where messages are broadcast to multiple members of the team. In this paper, we developed a compact and effective information sharing approach for teams with a dynamically changing, broadcast communication medium. By using a matrix representation of information status, the network structure and information needs, the model allows efficient reasoning about communication in a single computation. Empirical simulation results show that the approach performs well in large team, and effectively balances sharing key information with minimizing communication costs.

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AAMAS Conference 2012 Conference Paper

Prioritized Shaping of Models for Solving DEC-POMDPs

  • Pradeep Varakantham
  • William Yeoh
  • Prasanna Velagapudi
  • Katia Sycara
  • Paul Scerri

An interesting class of multi-agent POMDP planning problems can be solved by having agents iteratively solve individual POMDPs, find interactions with other individual plans, shape their transition and reward functions to encourage good interactions and discourage bad ones and then recompute a new plan. D-TREMOR showed that this approach can allow distributed planning for hundreds of agents. However, the quality and speed of the planning process depends on the prioritization scheme used. Lower priority agents shape their models with respect to the models of higher priority agents. In this paper, we introduce a new prioritization scheme that is guaranteed to converge and is empirically better, in terms of solution quality and planning time, than the existing prioritization scheme for some problems.

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AAMAS Conference 2011 Conference Paper

Allocating Spatially Distributed Tasks in Large, Dynamic Robot Teams

  • Steven Okamoto
  • Nathan Brooks
  • Sean Owens
  • Katia Sycara
  • Paul Scerri

For an interesting class of emerging applications, a large robot team will need to distributedly allocate many more tasks than there are robots, with dynamically appearing tasks and a limited ability to communicate. The LA-DCOP algorithm can conceptually handle both large-scale problems and multiple tasks per robot, but has key limitations when allocating spatially distributed tasks. In this paper, we extend LA-DCOP with several alternative acceptance rules for robots to determine whether to take on an additional task, given the interaction with the tasks it has already committed to. We show that these acceptance rules dramatically outperform a naive LA-DCOP implementation. In addition, we developed a technique that lets the robots use completely local knowledge to adjust their task acceptance criteria to get the best possible performance at a given communication bandwidth level.

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AAMAS Conference 2011 Conference Paper

An Investigation of the Vulnerabilities of Scale Invariant Dynamics in Large Teams

  • Robin Glinton
  • Paul Scerri
  • Katia Sycara

Large heterogeneous teams in a variety of applications must make joint decisions using large volumes of noisy and uncertain data. Often not all team members have access to a sensor, relying instead on information shared by peers to make decisions. These sensors can become permanently corrupted through hardware failure or as a result of the actions of a malicious adversary. Previous work showed that when the trust between agents was tuned to a specific value the resulting dynamics of the system had a property called scale invariance which led to agents reaching highly accurate conclusion with little communication. In this paper we show that these dynamics also leave the system vulnerable to most agents coming to incorrect conclusions as a result of small amounts of anomalous information maliciously injected in the system. We conduct an analysis that shows that the efficiency of scale invariant dynamics is due to the fact that large number of agents can come to correct conclusions when the difference between the percentage of agents holding conflicting opinions is relatively small. Although this allows the system to come to correct conclusions quickly, it also means that it would be easy for an attacker with specific knowledge to tip the balance. We explore different methods for selecting which agents are Byzantine and when attacks are launched informed by the analysis. Our study reveals global system properties that can be used to predict when and where in the network the system is most vulnerable to attack. We use the results of this study to design an algorithm used by agents to effectively attack the network, informed by local estimates of the global properties revealed by our investigation.

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AAMAS Conference 2011 Conference Paper

Distributed Model Shaping for Scaling to Decentralized POMDPs with Hundreds of Agents

  • Prasanna Velagapudi
  • Pradeep Varakantham
  • Katia Sycara
  • Paul Scerri

The use of distributed POMDPs for cooperative teams has been severely limited by the incredibly large joint policyspace that results from combining the policy-spaces of the individual agents. However, much of the computational cost of exploring the entire joint policy space can be avoided by observing that in many domains important interactions between agents occur in a relatively small set of scenarios, previously defined as coordination locales (CLs). Moreover, even when numerous interactions might occur, given a set of individual policies there are relatively few actual interactions. Exploiting this observation and building on an existing model shaping algorithm, this paper presents D-TREMOR, an algorithm in which cooperative agents iteratively generate individual policies, identify and communicate possible interactions between their policies, shape their models based on this information and generate new policies. D-TREMOR has three properties that jointly distinguish it from previous DEC-POMDP work: (1) it is completely distributed; (2) it is scalable (allowing 100 agents to compute a "good" joint policy in under 6 hours) and (3) it has low communication overhead. D-TREMOR complements these traits with the following key contributions, which ensure improved scalability and solution quality: (a) techniques to ensure convergence; (b) faster approaches to detect and evaluate CLs; (c) heuristics to capture dependencies between CLs; and (d) novel shaping heuristics to aggregate effects of CLs. While the resulting policies are not globally optimal, empirical results show that agents have policies that effectively manage uncertainty and the joint policy is better than policies generated by independent solvers.

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AAMAS Conference 2010 Conference Paper

Analyzing the impact of human bias on human-agent teams in resource allocation domains

  • Praveen Paruchuri
  • Pradeep Varakantham
  • Katia Sycara
  • Paul Scerri

As agent-human teams get increasingly deployed in the real-world, agent designers need to take into account that humans and agentshave different abilities to specify preferences. In this paper, we focus on how human biases in specifying preferences for resourcesimpacts the performance of large, heterogeneous teams. In particular, we model the inclination of humans to simplify their preference functions and to exaggerate their utility for desired resources. We then study the effect of these biases on two different problems, which are representative of most resource allocation problems addressed in literature.

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IROS Conference 2010 Conference Paper

Decentralized prioritized planning in large multirobot teams

  • Prasanna Velagapudi
  • Katia P. Sycara
  • Paul Scerri

In this paper, we address the problem of distributed path planning for large teams of hundreds of robots in constrained environments. We introduce two distributed prioritized planning algorithms: an efficient, complete method which is shown to converge to the centralized prioritized planner solution, and a sparse method in which robots discover collisions probabilistically. Planning is divided into a number of iterations, during which every robot simultaneously and independently computes a planning solution based on other robots' path information from the previous iteration. Paths are exchanged in ways that exploit the cooperative nature of the team and a statistical phenomenon known as the “birthday paradox”. Performance is measured in simulated 2D environments with teams of up to 240 robots. We find that in moderately constrained environments, these methods generate solutions of similar quality to a centralized prioritized planner, but display interesting communication and planning time characteristics.

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AAMAS Conference 2010 Conference Paper

Exploiting Scale Invariant Dynamics for Efficient Information Propagation in Teams

  • Robin Glinton
  • Paul Scerri
  • Katia Sycara

Large heterogeneous teams will often be in situations where sensor datathat is uncertain and conflicting is shared across a peer-to-peer network. Not every team member will have direct access to sensors and team members will be influenced mostly by teammates with whom they communicatedirectly. In this paper, we investigate the dynamics and emergent behaviors of a large team sharing beliefs to reach conclusions about the world. We find empirically that the dynamics of information propagation in suchbelief sharing systems are characterized by information avalanches of belief changes caused by a single additional sensor reading. The distributionof the size of these avalanches dictates the speed and accuracy with whichthe team reaches conclusions. A key property of the system is that it exhibits qualitatively different dynamics and system performance over smallchanges in system parameter ranges. In one particular range, the systemexhibits behavior known as scale-invariant dynamics which we empiricallyfind to correspond to dramatically more accurate conclusions being reachedby team members. Due to the fact that the ranges are very sensitive toconfiguration details, the parameter ranges over which specific system dynamics occur are extremely difficult to predict precisely. In this paper we(a) develop techniques to mathematically characterize the dynamics of theteam belief propagation (b) obtain through simulations the relation betweenthe dynamics and overall system performance, and (c) develop a novel distributed algorithms that the agents in the team use locally to steer the wholeteam to areas of optimized performance.

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IROS Conference 2010 Conference Paper

Towards an understanding of the impact of autonomous path planning on victim search in USAR

  • Paul Scerri
  • Prasanna Velagapudi
  • Katia P. Sycara
  • Huadong Wang
  • Shih Yi Chien
  • Michael Lewis 0001

Technology for multirobot systems has advanced to the point where we can consider their use in a variety of important domains, including urban search and rescue. A key to the practical usefulness of multirobot systems is the ability to have a large number of robots effectively controlled by small numbers of operators. In this paper, two modalities for controlling a team of 24 robots in a foraging task in an urban search and rescue environment are compared. In both modalities, multiple operators must monitor video streams from the robots to detect and mark victims on a map as well as teleoperating robots that cannot get themselves out of difficult situations. In the first modality, the operators must also provide waypoints for the robots to explore, using both video and a partially completed map to choose appropriate waypoints. In the second modality, the robots autonomously plan their paths, allowing operators to focus on monitoring the video, but without being able to interpret video streams to guide exploration. Experimental results show that significantly better overall performance is achieved with autonomous path planning, although the reduction in operator workload is not significant.

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AAMAS Conference 2009 Conference Paper

Analyzing the Performance of Randomized Information Sharing

  • Prasanna Velagapudi
  • Oleg Prokopyev
  • Katia Sycara
  • Paul Scerri

In large, collaborative, heterogeneous teams, team members often collect information that is useful to other members of the team. Recognizing the utility of such information and delivering it efficiently across a team has been the focus of much research, with proposed approaches ranging from flooding to complex filters and matchmakers. Interestingly, random forwarding of information has been found to be a surprisingly effective information sharing approach in some domains. In this paper, we investigate this phenomenon in detail and show that in certain systems, random forwarding of information performs almost half as well as a globally optimal approach. We present analytic and empirical results comparing random methods with theoretically optimal sharing in small-worlds, scale-free, and random networks. In addition, we demonstrate a method for modeling real domains that allows our results to be applied toward estimating information sharing performance.

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ICAPS Conference 2009 Conference Paper

Exploiting Coordination Locales in Distributed POMDPs via Social Model Shaping

  • Pradeep Varakantham
  • Jun-young Kwak
  • Matthew E. Taylor
  • Janusz Marecki
  • Paul Scerri
  • Milind Tambe

Distributed POMDPs provide an expressive framework for modeling multiagent collaboration problems, but NEXP-Complete complexity hinders their scalability and application in real-world domains. This paper introduces a subclass of distributed POMDPs, and TREMOR, an algorithm to solve such distributed POMDPs. The primary novelty of TREMOR is that agents plan individually with a single agent POMDP solver and use social model shaping to implicitly coordinate with other agents. Experiments demonstrate that TREMOR can provide solutions orders of magnitude faster than existing algorithms while achieving comparable, or even superior, solution quality.

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IROS Conference 2009 Conference Paper

Scaling effects for streaming video vs. static panorama in multirobot search

  • Prasanna Velagapudi
  • Huadong Wang
  • Paul Scerri
  • Michael Lewis 0001
  • Katia P. Sycara

Camera guided teleoperation has long been the preferred mode for controlling remote robots with other modes such as asynchronous control only used when unavoidable. Because controlling multiple robots places additional demands on the operator we hypothesized that removing the forced pace for reviewing camera video might reduce workload and improve performance. In an earlier experiment participants operated four teams performing a simulated urban search and rescue (USAR) task using a conventional streaming video plus map interface or an experimental interface without streaming video but with the ability to store panoramic images on the map to be viewed at leisure. Operators were more accurate in marking victims on maps using the conventional interface; however, ancillary measures suggested that the asynchronous interface succeeded in reducing temporal demands for switching between robots. This raised the possibility that the asynchronous interface might perform better if teams were larger. In this experiment we evaluate the usefulness of asynchronous video for teams of 4, 8, or 12 robots. Operators in the two conditions were equally successful in finding victims, however, the streaming video maintained its advantage for accuracy in locating victims.

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IS Journal 2009 Journal Article

Scaling Up Wide-Area-Search-Munition Teams

  • Michael Lewis
  • Katia Sycara
  • Paul Scerri

Wide area search munitions (WASMs) are a cross between an unmanned aerial vehicle (UAV) and a munition. The first of these high-concept munitions, the low-cost autonomous attack system, was a miniature, autonomous WASM capable of broad-area search, identification, and destruction of a range of mobile ground targets. The LoCAAS used a small turbojet engine capable of powering the vehicle for up to 30 minutes and laser radar (ladar) with automatic target recognition to identify potential targets. The original LoCAAS was a fire-and-forget munition designed to operate independently. It flew preprogrammed search patterns until it located a target or ran out of fuel.

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AAMAS Conference 2008 Conference Paper

A Decentralized Approach to Cooperative Situation Assessment in Multi-Robot Systems

  • Giuseppe Settembre
  • Alessandro Farinelli
  • Paul Scerri
  • Katia Sycara
  • Daniele Nardi

To act effectively under uncertainty, multi-robot teams need to accurately estimate the state of the environment. Although individual robots, with uncertain sensors, may not be able to accurately determine the current situation, the team as a whole should have the capability to perform situation assessment. However, sharing all information with all other team mates is not scalable nor is centralization of all information possible. This paper presents a decentralized approach to cooperative situation assessment that balances use of communication bandwidth with the need for good situation assessment. When a robot believes locally that a particular plan should be executed, it sends a proposal for that plan, to one of its team mates. The robot receiving the plan proposal, can either agree with the plan and forward it on, or it can provide sensor information to suggest that an alternative plan might have higher expected utility. Once sufficient robots agree with the proposal, the plan is initiated. The algorithm successfully balances the value of cooperative sensing against the cost of sharing large volumes of information. Experiments verify the utility of the approach, showing that the algorithm dramatically out-performs individual decisionmaking and obtains performance similar to a centralized approach.

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AAMAS Conference 2008 Conference Paper

An Approach to Online Optimization of Heuristic Coordination Algorithms

  • Jumpol Polvichai
  • Paul Scerri
  • Michael Lewis

Due to computational intractability, large scale coordination algorithms are necessarily heuristic and hence require tuning for particular environments. In domains where characteristics of the environment vary dramatically from scenario to scenario, it is desirable to have automated techniques for appropriately configuring the coordination. This paper presents an approach that takes performance data from a simulator to train a stochastic neural network that concisely models the complex, probabilistic relationship between configurations, environments and performance metrics. The stochastic neural network is used as the core of a tool that allows rapid online or offline configuration of coordination algorithms to particular scenarios and user preferences. The overall system allows rapid adaptation of coordination, leading to better performance in new scenarios.

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IROS Conference 2008 Conference Paper

Scaling effects in multi-robot control

  • Prasanna Velagapudi
  • Paul Scerri
  • Katia P. Sycara
  • Huadong Wang
  • Michael Lewis 0001
  • Jijun Wang 0002

The present study investigates the effect of the number of controlled robots on performance of an urban search and rescue (USAR) task using a realistic simulation. Task performance increased in going from four to eight controlled robots but deteriorated in moving from eight to twelve. Workload increased monotonically with number of robots. Performance per robot decreased with increases in team size. Results are consistent with earlier studies suggesting a limit of between 8-12 robots for direct human control. This study demonstrates that these findings generalize to a more realistic setting and complex task.

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AAMAS Conference 2008 Conference Paper

Transitioning Multiagent Technology to UAV Applications

  • Paul Scerri
  • Tracy Von Gonten
  • Gerald Fudge
  • Sean Owens
  • Katia Sycara

This paper describes the transition of academically developed multiagent technology for UAV coordination to an industrially developed application. The specific application is the use of lightweight UAVs with small Received Signal Strength Indicator sensors to cooperatively locate targets emitting radio frequency signals in a large area. It is shown that general techniques can be effectively transitioned, sometimes with minimal changes. However, clear differences in engineering and testing requirements of academia and commercialization require extensive effort in developing simulation and live flight testbeds. Although the technology has not yet been commercialized, initial live flight testing shows the potential of the approach.

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IJCAI Conference 2007 Conference Paper

  • Alessandro Farinelli
  • Paul Scerri
  • Alberto Ingenito
  • Daniele Nardi

A prerequisite to efficient behavior by a multi-robot team is the ability to accurately perceive the environment. In this paper, we present an approach to deal with sensing uncertainty at the coordination level. Specifically, robots attach information regarding features that caused the initiation of a course of action, to any coordination message for that activity. Further information regarding such features, acquired by the team, are then combined and the expected utility of the started action is re-evaluated accordingly. Experiments show that the approach allows to coordinate a large group of robots, addressing sensing uncertainty in a tractable way.

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AAMAS Conference 2006 Conference Paper

CSC: Criticality-Sensitive Coordination Pedro Szekely, Marcel Becker, Stephen Fitzpatrick, Gergely Gati, David Hanak, Jing Jin, Gabor Karsai, Rajiv T. Maheswaran, Bob Neches, Craig M. Rogers, Romeo Sanchez, Chris van Buskirk CALO Conflict Negotiation (PTIME and DCOP) Pauline M. Berry, Cory Albright, Emma Bowring, Kenneth Conley, Kenneth Nitz, Jonathan Pearce, Bart Peintner, Shahin Saadati, Milind Tambe, Tomas Uribe, and Neil Yorke-Smith Synergistic Integration of Agent Technologies for Military Simulation

  • Sean Owens
  • Paul Scerri
  • Robin Glinton
  • Bin Yu
  • Katia Sycara