Robin Glinton

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.

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.

AAAI Conference 2008 Conference Paper

Agent Organized Networks Redux

• Robin Glinton

Individual robots or agents will often need to form coalitions to accomplish shared tasks, e. g. ,in sensor networks or markets. Furthermore, in most real systems it is infeasible for entities to interact with all peers. The presence of a social network can alleviate this problem by providing a neighborhood system within which entities interact with a reduced number of peers. Previous research has shown that the topology of the underlying social network has a dramatic effect on the quality of coalitions formed and consequently on system performance (Gaston & desJardins 2005a). It has also been shown that it is feasible to develop agents which dynamically alter connections to improve an organization’s ability to form coalitions on the network. However those studies have not analysed the network topologies that result from connectivity adaptation strategies. In this paper the resulting network topologies were analysed and it was found that high performance and rapid convergence were attained because scale free networks were being formed. However it was observed that organizational performance is not impacted by limiting the number of links per agent to the total number of skills available within the population, implying that bandwidth was wasted by previous approaches. We used these observations to inform the design of a token based algorithm that attains higher performance using an order of magnitude less messages for both uniform and non-uniform distributions of skills.

AAMAS Conference 2007 Conference Paper

Locating RF Emitters with Large UAV Teams

• Paul Scerri
• Robin Glinton
• Sean Owens
• Steven Okamoto
• Katia Sycara

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