10373451
Reciprocal resource sharing in P2P environments
Peer-to-peer (P2P) systems enable users to share resources in a networked environment without worrying about issues such as scalability and load balancing. Unlike exchange of goods in a traditional market, resource exchange in P2P networks does not involve monetary transactions. This makes P2P systems vulnerable to problems including the free-rider problem that enables users to acquire resources without contributing anything, collusion between groups of users to incorrectly promote or malign other users, and zero-cost identity that enables nodes to obliterate unfavorable history without incurring any expenditure. Previous research addresses these issues using user-reputation, referrals, and shared history based techniques. Here, we describe a multi-agent based reciprocity mechanism where each user's agent makes the decision to share a resource with a requesting user based on the amount of resources previously provided by the requesting user to the providing user and globally in the system. A robust reputation mechanism is proposed to avoid the differential exploitations by the free-riders and to prevent collusion. Experimental results on a simulated P2P network addresses the problems identified above and shows that users adopting the reciprocative mechanism outperform users that do not share resources in the P2P network. Hence, our proposed reciprocative mechanism effectively suppresses free-riding.
1062630
The Role of Commitment in Cooperative Negotiation
Cooperative information agents need mechanisms that enable them to work together effectively while solving common problems. We investigate the use of commitment by agents to proposed actions as a mechanism that allow agents to work concurrently on interdependent problems. Judicious use of commitment can not only increase the throughput of cooperative information systems, but also allow them to deal flexibly with dynamically changing environments. We use the domain of distributed scheduling to demonstrate that static commitment strategies are ineffective. Results from simulated experiments are used to identify the environmental features on which an adaptive commitment strategy should be predicated.
2403961
Learning Mutual Trust
The multiagent learning literature has looked at iterated two-player games to develop mechanisms that allow agents to learn to converge on Nash Equilibrium strategy profiles. An equilibrium configuration implies that there is no motivation for one player to change its strategy if the other does not. Often, in general sum games, a higher payoff can be obtained by both players if one chooses not to respond optimally to the other player. By developing mutual trust, agents can avoid iterated best responses that will lead to a lesser payoff Nash Equilibrium. In this paper we consider 1-level agents (modelers) who select actions based on expected utility considering probability distributions over the actions of the opponent(s). We show that in certain situations, such stochastically-greedy agents can perform better (by developing mutually trusting behavior) than those that explicitly attempt to converge to Nash Equilibrium. We also experiment with an interesting action revealation strategy that can give the revealer better payoff on convergence than a non-revealing approach. By revealing, the revealer enables the opponent to agree to a more trusted equilibrium.
15974834
Learning Pareto-optimal Solutions in 2x2 Conflict Games
Multiagent learning literature has investigated iterated two-player games to develop mechanisms that allow agents to learn to converge on Nash Equilibrium strategy profiles. Such equilibrium configurations imply that no player has the motivation to unilaterally change its strategy. Often, in general sum games, a higher payoff can be obtained by both players if one chooses not to respond myopically to the other player. By developing mutual trust, agents can avoid immediate best responses that will lead to a Nash Equilibrium with lesser payoff. In this paper we experiment with agents who select actions based on expected utility calculations that incorporate the observed frequencies of the actions of the opponent(s). We augment these stochastically greedy agents with an interesting action revelation strategy that involves strategic declaration of one's commitment to an action to avoid worst-case, pessimistic moves. We argue that in certain situations, such apparently risky action revelation can indeed produce better payoffs than a non-revealing approach. In particular, it is possible to obtain Pareto-optimal Nash Equilibrium outcomes. We improve on the outcome efficiency of a previous algorithm and present results over the set of structurally distinct two-person two-action conflict games where the players' preferences form a total order over the possible outcomes. We also present results on a large number of randomly generated payoff matrices of varying sizes and compare the payoffs of strategically revealing learners to payoffs at Nash equilibrium.
16541041
Helping based on future expectations
Autonomous agents interacting in an open world can be considered to be primarily driven by self interests. Previous work in this area has prescribed a strategy of reciprocal behavior, based on past interactions, for promoting and sustaining cooperation among such self-interested agents. Here we present a new mechanism where agents base their decisions both on historical data as well as on future interaction expectations. A decision mechanism is presented that compares current helping cost with expected future savings from interaction with the agent requesting help. We experiment with heterogeneous agents that have varying expertise for different job types. We evaluate the effect of both change of agent expertise and distribution of task types on subsequent agent relationships. The reciprocity mechanism based on future expectations is found to be robust and flexible in adjusting to the environmental dynamics.
3526092
Selecting partners
1. I N T R O D U C T I O N In an open environment the goal of a self-interested agent would be to interact with or enter into partnership with those agents that will produce maximal local utility for this agent [5]. We assume that an agent can get one of several payoffs (utilities) for joining a particular coalition which is determined by a static probability distribution over the payoffs for each coalition. We consider choosing a single coalition to interact with repeatedly when the total number of interactions is known ahead of time.
35225520
Some Observations on Skip-Lists
The Skip-list data-structure was proposed by Pugh as an alternate to balanced binary search trees. This remarkably simple data-structure uses randomization and Pugh proved that the expected performance is comparable to the balanced trees. In this note we prove that the performance can be further guaranteed in the following sense: the probability of the search time or space complexity exceeding their expected values, approaches 0 rapidly as the number of keys increases.
16234197
Development of a wireless integrated toxic and explosive MEMS based gas sensor
Demand of strict environmental safety norms in mines, industries leads to the development of smart sensor systems for monitoring the explosive and toxic gases like methane (CH/sub 4/) and carbon monoxide (CO). In the present work a wireless integrated CH/sub 4/ and CO gas sensor with a unique transmission scheme has been proposed. It detects CH/sub 4/ & CO gas and generates frequency modulated (FM) signal in two distinct frequency bands in the range of 2.5823 GHz to 2.5542 GHz for CH/sub 4/ concentration from 1% to 2.5% in air and 2.4854 GHz to 2.404 GHz for CO concentration from 50ppm to 300ppm. Also it transmits the generated FM signal through antenna. The receiver section demodulates the signal and displays the gas concentration.
14273924
The Success and Failure of Tag-Mediated Evolution of Cooperation
Use of tags to limit partner selection for playing has been shown to produce stable cooperation in agent populations playing the Prisoner's Dilemma game. There is, however, a lack of understanding of how and why tags facilitate such cooperation. We start with an empirical investigation that identifies the key dynamics that result in sustainable cooperation in PD. Sufficiently long tags are needed to achieve this effect. A theoretical analysis shows that multiple simulation parameters including tag length, mutation rate and population size will have significant effect on sustaining cooperation. Experiments partially validate these observations. Additionally, we claim that tags only promote mimicking and not coordinated behavior in general, i.e., tags can promote cooperation only if cooperation requires identical actions from all group members. We illustrate the failure of the tag model to sustain cooperation by experimenting with domains where agents need to take complementary actions to maximize payoff.
7334644
Analyzing peer-to-peer traffic across large networks
The use of peer-to-peer (P2P) applications is growing dramatically, particularly for sharing large video/audio files and software. In this paper, we analyze P2P traffic by measuring flow-level information collected at multiple border routers across a large ISP network, and report our investigation of three popular P2P systems-FastTrack, Gnutella, and Direct-Connect. We characterize the P2P traffic observed at a single ISP and its impact on the underlying network. We observe very skewed distribution in the traffic across the network at different levels of spatial aggregation (IP, prefix, AS). All three P2P systems exhibit significant dynamics at short time scale and particularly at the IP address level. Still, the fraction of P2P traffic contributed by each prefix is more stable than the corresponding distribution of either Web traffic or overall traffic. The high volume and good stability properties of P2P traffic suggests that the P2P workload is a good candidate for being managed via application-specific layer-3 traffic engineering in an ISP's network.
1889631
Characterizing Unstructured Overlay Topologies in Modern P2P File-Sharing Systems
During recent years, peer-to-peer (P2P) file-sharing systems have evolved in many ways to accommodate growing numbers of participating peers. In particular, new features have changed the properties of the unstructured overlay topology formed by these peers. Despite their importance, little is known about the characteristics of these topologies and their dynamics in modern file-sharing applications.
 This paper presents a detailed characterization of P2P overlay topologies and their dynamics, focusing on the modern Gnutella network. Using our fast and accurate P2P crawler, we capture a complete snapshot of the Gnutella network with more than one million peers in just a few minutes. Leveraging more than 18,000 recent overlay snapshots, we characterize the graph-related properties of individual overlay snapshots and overlay dynamics across hundreds of back-to-back snapshots. We show how inaccuracy in snapshots can lead to erroneous conclusions--such as a power-law degree distribution. Our results reveal that while the Gnutella network has dramatically grown and changed in many ways, it still exhibits the clustering and short path lengths of a small world network. Furthermore, its overlay topology is highly resilient to random peer departure and even systematic attacks. More interestingly, overlay dynamics lead to an "onion-like" biased connectivity among peers where each peer is more likely connected to peers with higher uptime. Therefore, long-lived peers form a stable core that ensures reachability among peers despite overlay dynamics.