ARA: A Robust Audit to Prevent Free-Riding in P2P Networks

A number of solutions have been proposed to address the free-riding problem in peer-to-peer file sharing systems. The solutions are either imperfect--they allow some users to cheat the system with malicious behavior, or expensive-- they require human intervention, require servers, or incur high mental transaction costs. We propose a method to address these weaknesses. Specifically, we introduce a utility function to capture contributions made by a user and an auditing scheme to ensure the integrity of a utility function's values. Our method enables us to reduce cheating by a malicious peer: we show that our approach can efficiently detect malicious peers with a probability over 98%

Market-based coordination and auditing mechanisms for self-interested multi-robot systems

We propose market-based coordinated task allocation mechanisms, which allocate complex tasks that require synchronized and collaborated services of multiple robot agents to robot agents, and an auditing mechanism, which ensures proper behaviors of robot agents by verifying inter-agent activities, for self-interested, fully-distributed, and large-scale multi-robot systems that have multi-agent tasks with real-time constraints in dynamic environments. The studied coordinated task allocation mechanisms include auction mechanisms such as forward auction, reverse auction, forward/reverse auction, and sealed-bid auction with different bidding strategies, which are represented by different utility functions. The coordinated task allocation mechanisms also include 1-to-1 task exchange, which reallocates tasks assigned by auctions in order to adapt to changing environments in real-time. Because the robot agents are self-interested, the agents may try free-riding and cheating, which is to consume resources without providing enough services. Free-riding and cheating attempts may deteriorate the overall performance by discouraging contribution of each robot agent—contribution of honest agents may be not rewarded with enough amount of service consumption. In order to encourage contribution and to ensure the amount contribution is properly accounted, we propose an auditing mechanism with a credit system, which can be combined with the proposed coordinated task allocation mechanisms. We conduct both physical robot experiments and software agent simulations for the coordinated task allocation mechanisms. The experimental results suggest that the proposed mechanisms are scalable and fault-tolerant and work in physical robot systems. We compare the proposed methods to control methods with various performance metrics and the results suggest that the performance can be enhanced by the proposed approaches. We observe and analyze issues such as deadlock, pingpong-bidding, pingpong-swapping, and others. We analyze the auditing mechanism with mathematical analysis and software simulations on the behavior of the mechanism. The results from both analysis and simulations suggest that the auditing mechanism can detect cheating attempts with high probability without excessive communication overheads in various conditions. We also suggest an adaptive mechanism to be included in the auditing mechanism so that each robot or peer can tune the communication overheads and detection probability according to the dynamic environments. The analysis and simulations of the evolutionary game theory on the behaviors of peers or robots suggest that the auditing mechanism can discourage free-riding and cheating attempts effectively without harming the popularity of systems with auditing mechanisms. The analysis shows that a strategy where nodes behave honestly and contribute properly is evolutionarily stable and the simulations show that such a strategy takes the dominance even if the strategy had minor initial population

Market-Based Coordination and Auditing Mechanisms for Self-Interested Multi-Robot Systems

119 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2009.We conduct both physical robot experiments and software agent simulations for the coordinated task allocation mechanisms. The experimental results suggest that the proposed mechanisms are scalable and fault-tolerant and work in physical robot systems. We compare the proposed methods to control methods with various performance metrics and the results suggest that the performance can be enhanced by the proposed approaches. We observe and analyze issues such as deadlock, pingpong-bidding, pingpong-swapping, and others. We analyze the auditing mechanism with mathematical analysis and software simulations on the behavior of the mechanism. The results from both analysis and simulations suggest that the auditing mechanism can detect cheating attempts with high probability without excessive communication overheads in various conditions. We also suggest an adaptive mechanism to be included in the auditing mechanism so that each robot or peer can tune the communication overheads and detection probability according to the dynamic environments. The analysis and simulations of the evolutionary game theory on the behaviors of peers or robots suggest that the auditing mechanism can discourage free-riding and cheating attempts effectively without harming the popularity of systems with auditing mechanisms. The analysis shows that a strategy where nodes behave honestly and contribute properly is evolutionarily stable and the simulations show that such a strategy takes the dominance even if the strategy had minor initial population.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

Market-based coordination strategies for large-scale multi-agent systems

Abstract: This paper studies market-based mechanisms for dynamic coordinated task assignment in large scale agent systems carrying out search and rescue missions. Specifically, the effect of different auction mechanisms and swapping are studied. The paper describes results from a large number of simulations of homogeneous agents, where by homogeneous we mean that agents in a given simulation use the same strategy. The information available to agents and their bidding strategies are used as simulation parameters. The simulations provide insight about the interaction between the strategy used by individual agents and the market mechanism. Performance is evaluated using several metrics: mission time, distance traveled, communication and computation costs, and workload distribution. Some of the results obtained include: limiting information may improve performance, different utility functions may affect the performance in non-uniform ways, and swapping may help improve the efficiency of assignments in dynamic environments

Task Assignment for a Physical Agent Team via a Dynamic Forward/Reverse Auction Mechanism

In the dynamic distributed task assignment (DDTA) problem, a team of agents is required to accomplish a set of tasks while maximizing the overall team utility. An effective solution to this problem needs to address two closely related questions: first, how to find a near-optimal assignment from agents to tasks under resource constraints, and second, how to efficiently maintain the optimality of the assignment over time. We address the first problem by extending an existing forward/reverse auction algorithm which was designed for bipartite maximal matching to find an initial near-optimal assignment. A difficulty with such an assignment is that the dynamicity of the environment compromises the optimality of the initial solution. We address the dynamicity problem by using swapping to locally move agents between tasks. By linking these local swaps, the current assignment is morphed into one which is closer to what would have been obtained if we had re-executed the computationally more expensive auction algorithm. In this paper, we detail the application of this dynamic auctioning scheme in the context of a UAV (Unmanned Aerial Vehicle) search and rescue mission and present early experimentations using physical agents to show the feasibility of the proposed approach