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.
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.
43158386
Evolution and learning in multiagent systems
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.
482217
Believing others: pro and cons
Under the assumption that agents remain in the system for significant time periods, or that the agent composition changes only slowly Sen (1996) has previously presented a prescriptive strategy for promoting and sustaining cooperation among self-interested agents. The adaptive, probabilistic policy prescribed promotes reciprocative cooperation that improves both individual and group performance in the long run. In the short run, however, selfish agents could still exploit reciprocative agents. In this paper we evaluate the hypothesis that the exploitative tendencies of selfish agents can be effectively curbed if reciprocative agents share their "opinions" of other agents. Since the true nature of agents are not known a priori and is learned from experience, believing others can also pose other hazards. We provide a learned trust-based evaluation function that is shown to resist both individual and concerted deception on the part of selfish agents.
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.
207161230
FeedMe: a collaborative alert filtering system
As the number of alerts generated by collaborative applications grows, users receive more unwanted alerts. FeedMe is a general alert management system based on XML feed protocols such as RSS and ATOM. In addition to traditional rule-based alert filtering, FeedMe uses techniques from machine-learning to infer alert preferences based on user feedback. In this paper, we present and evaluate a new collaborative naïve Bayes filtering algorithm. Using FeedMe, we collected alert ratings from 33 users over 29 days. We used the data to design and verify the accuracy of the filtering algorithm and provide insights into alert prediction.
5876633
Analysis of Communities of Interest in Data Networks
Communities of interest (COI) have been applied in a variety of environments ranging from characterizing the online buying behavior of individuals to detecting fraud in telephone networks. The common thread among these applications is that the historical COI of an individual can be used to predict future behavior as well as the behavior of other members of the COI. It would clearly be beneficial if COIs can be used in the same manner to characterize and predict the behavior of hosts within a data network. In this paper, we introduce a methodology for evaluating various aspects of COIs of hosts within an IP network. In the context of this study, we broadly define a COI as a collection of interacting hosts. We apply our methodology using data collected from a large enterprise network over a eleven week period. First, we study the distributions and stability of the size of COIs. Second, we evaluate multiple heuristics to determine a stable core set of COIs and determine the stability of these sets over time. Third, we evaluate how much of the communication is not captured by these core COI sets.
781819
Dynamic point location in arrangements of hyperplanes
We present algorithms for maintaining data structures supporting fast (polylogarithmic) point-location and ray-shooting queries in arrangements of hyperplanes. This data structure allows for deletion and insertion of hyperplanes. Our algorithms use random bits in the construction of the data structure but do not make any assumptions about the update sequence or the hyperplanes in the input. The query bound for our data structure isO(polylog(n)), wheren is the number of hyperplanes at any given time, and theO notation indicates that the bound holds with high probability, where the probability is solely with respect to randomization in the data structure. By high probability we mean that the probability of error is inversely proportional to a large degree polynomial inn. The space requirement isO(nd). The cost of update isO(nd?1 logn. The expected cost of update isO(nd?1); the expectation is again solely with respect to randomization in the data structure. Our algorithm is extremely simple. 
 
We also give a related algorithm with optimalO(logn) query time, expectedO(nd) space requirement, and amortizedO(nd?1) expected cost of update. Moreover, our approach has a versatile quality which is likely to have further applications to other dynamic algorithms. 
 
Ford=2, 3 we also show how to obtain polylogarithmic update time in the CRCW PRAM model so that the processor-time product matches (within a polylogarithmic factor) the sequential update time.
37914727
Fractional Cascading Revisited
We present an alternative implementation of the fractional cascading data structure of Chazelle and Guibas (Algorithmica1 (1986), 133-162) that performs iterative search for a key in multiple ordered lists. The construction of our data structure uses randomization and simplifies the algorithm of Chazelle and Guibas, vastly making it practical to implement. Although our bounds are asymptotically similar to the earlier ones, there are improvements in the constant factors. Our analysis is novel and captures some of the inherent difficulties associated with the fractional cascading data structure. In particular, we use tools from branching process theory and derive some useful asymptotic bounds. The probability of deviation from the expected performance bounds decreases exponentially with number of keys. Also, under a restricted model, we obtain efficient bounds for updates in the data structure.
16333487
Playback restart in interactive streaming video applications
Low latency is crucial in networked multimedia applications such as on-demand streaming audio and video. In this paper we consider the problem of restarting or resuming playback following an interactive operation such as fast forward, rewind, or indexed jump in stored on-demand video. We present two approaches to restart playback after an interactive operation, and develop algorithms to compute the latencies incurred in restarting playback. Using long MPEG-1 traces, with an optimal smoothing technique to transmit data for regular playback, we demonstrate that the latencies incurred under our approaches are very small. We next examine the latencies incurred when restarting playback in a video server under two policies for sharing its bandwidth: (a) one in which only regular playback bandwidth is available to restart playback and (b) one in which additional free server bandwidth (if any) is accessible. We find playback restart latencies to be similarly low under both policies, suggesting that the simpler approach (a) is sufficient.