790181
Predicting agent strategy mix of evolving populations
We study agent societies where self-interested agents interact repeatedly over extended time periods. In particular, we are interested in environments where agents can form mutually beneficial relationships by exchanging help but an agent would rather receive help than give it. Evolutionary tournaments with competing help-giving strategies can model scenarios where agents periodically adopt strategies that are outperforming others in the population. Such experiments, however, can be computationally costly and hence it is difficult to prescribe a rational strategy choice given environmental conditions like task mix, strategy distribution in the population, etc. A preferred approach, pursued in this paper, is to analytically capture the dynamics of the strategy mix in the population under an evolutionary tournament. Such an analytical model can be used to predict the evolutionarily dominant strategy, the rational strategy choice.
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.
43158386
Evolution and learning in multiagent systems
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.
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.
12119095
Correlating Internal Parameters and External Performance: Learning Soccer Agents
We have developed a Soccer simulator in Java, which allows us to evaluate agent behavioral strategies for effectively collaborating with other team members while countering threats posed by the opposing team. Whereas action selection mechanisms to decide when to shoot, pass, dribble, guard opponent, tackle, etc. are important from a player's viewpoint, learning the capabilities of individual opponents from repeated encounters can provide critical information for the success of a team. Each agent is described by a set of skill levels for different soccer skills. A soccer agent uses its incomplete perceptions, model of the changing environment, knowledge of the skill levels of its own team players, and estimated skill levels of the opponent team players to select the most prudent action. In this paper, we identify learning opportunities for soccer agents, and investigate some of these possibilities in detail. Initial experimental results demonstrate the advantage of learning agents.
2420350
Smooth workload adaptive broadcast
The high-bandwidth requirements and long-lived characteristics of digital video make transmission bandwidth usage a key limiting factor in the widespread streaming of such content over the Internet. A challenging problem is to develop bandwidth-efficient techniques for delivering popular videos to a large, asynchronous client population with time-varying demand characteristics. In this paper, we propose smooth workload adaptive broadcast to address the above issues. A key component of our scheme is Flexible Periodic Broadcast (FPB). By introducing a feedback control loop into FPB, and enhancing FPB using techniques such as parsimonious transmission, smooth workload adaptive broadcast provides instantaneous or near-instantaneous playback services and can smoothly adapt to workload changes. Furthermore, FPB, as proposed in this paper, is bandwidth efficient and exhibits the periodic smooth channel transition property.
11169442
Linear Time Algorithms for Clustering Problems in Any Dimensions
We generalize the k-means algorithm presented by the authors [14] and show that the resulting algorithm can solve a larger class of clustering problems that satisfy certain properties (existence of a random sampling procedure and tightness). We prove these properties for the k-median and the discrete k-means clustering problems, resulting in O(2(k/e)O(1)dn) time (1+e)-approximation algorithms for these problems. These are the first algorithms for these problems linear in the size of the input (nd for n points in d dimensions), independent of dimensions in the exponent, assuming k and e to be fixed. A key ingredient of the k-median result is a (1+e)-approximation algorithm for the 1-median problem which has running time O(2(1/e)O(1)d). The previous best known algorithm for this problem had linear running time.
29616158
Improved decremental algorithms for maintaining transitive closure and all-pairs shortest paths
This paper presents improved algorithms for the following problem: given an unweighted directed graph G(V,E) and a sequence of on-line shortest-path/reachability queries interspersed with edge-deletions, develop a data-structure that can answer each query in optimal time, and can be updated efficiently after each edge-deletion. The central idea underlying our algorithms is a scheme for implicitly storing all-pairs reachability/shortest-path information, and an efficient way to maintain this information. Our algorithms are randomized and have one-sided inverse polynomial error for query.
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.
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.