26714
Local Decision Procedures for Avoiding the Tragedy of Commons
The social sciences literature abound in problems of providing and maintaining a public good in a society composed of self-interested individuals [6]. Public goods are social benefits that can be accessed by individuals irrespective of their personal contributions. Such problems are also addressed in the domain of agent based systems [15]. In this paper we address the problem of the Tragedy of the Commons [9], a particularly common social dilemma that leads to inefficient usage of shared resources. We present a decision procedure following which rational agents can optimally use a shared resource using only local information. Our experimental results confirm that the tragedy of the commons is successfully avoided and the shared resource is utilized to its capacity when agents following our prescribed decision procedure.
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.
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.
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
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.
16079000
Searching for optimal coalition structures
Coalition formation has been a very active area of research in multiagent systems. Most of this research has concentrated on decentralized procedures that allow self-interested agents to negotiate the formation of coalitions and division of coalition payoffs. A different line of research has addressed the problem of finding the optimal division of agents into coalitions such that the sum total of the the payoffs to all the coalitions is maximized (Larson and Sandholm, 1999). This is the optimal coalition structure identification problem. Deterministic search algorithms have been proposed and evaluated under the assumption that the performance of a coalition is independent of other coalitions. We use an order-based genetic algorithm (OBGA) as a stochastic search process to identify the optimal coalition structure. We compare the performance of the OBGA with a representative deterministic algorithm presented in the literature. Though the OBGA has no performance guarantees, it is found to dominate the deterministic algorithm in a significant number of problem settings. An additional advantage of the OBGA is its scalability to larger problem sizes and to problems where performance of a coalition depends on other coalitions in the environment.
16723670
PLEASE: A Prototype Learning System Using Genetic Algorithms
Prototypes have been proposed as representation of concepts that are used eeec-tively by humans. Developing computational schemes for generating prototypes from examples , however, has proved to be a dii-cult problem. We present a novel genetic algorithm based prototype learning system, PLEASE, for constructing appropriate prototypes from classiied training instances. After constructing a set of prototypes for each of the possible classes, the class of a new input instance is determined by the nearest prototype to this instance. Attributes are assumed to be ordinal in nature and prototypes are represented as sets of feature-value pairs. A genetic algorithm is used to evolve the number of prototypes per class and their positions on the input space. We present experimental results on a series of artiicial problems of varying complexity. PLEASE performs competitively with several nearest neighbor clas-siication algorithms on the problem set. An analysis of the strengths and weaknesses of the initial version of our system motivates the need for additional operators. The inclusion of these operators substantially improves the performance of the system on particularly diicult problems.
41531267
A linear time algorithm for approximate 2-means clustering
Matousek [Discrete Comput. Geom. 24 (1) (2000) 61-84] designed an O(nlogn) deterministic algorithm for the approximate 2-means clustering problem for points in fixed dimensional Euclidean space which had left open the possibility of a linear time algorithm. In this paper, we present a simple randomized algorithm to determine an approximate 2-means clustering of a given set of points in fixed dimensional Euclidean space, with constant probability, in linear time.We first approximate the mean of the larger cluster using random sampling. We then show that the problem can be reduced to a set of lines, on which it can be solved by carefully pruning away points of the larger cluster and randomly sampling on the remaining points to obtain an approximate to the mean of the smaller cluster.
10300805
Randomized Parallel Algorithms
6305829
Online Smoothing of Variable-Bit-Rate Streaming Video
Bandwidth smoothing techniques for stored video perform end to end workahead transmission of frames into the client playback buffer, in advance of their display times. Such techniques are very effective in reducing the burstiness of the bandwidth requirements for transmitting compressed, stored video. This paper addresses online bandwidth smoothing for a growing number of streaming video applications such as newscasts, sportscasts, and distance learning, where many clients may be willing to tolerate a playback delay of a few seconds in exchange for a smaller bandwidth requirement. The smoothing can be performed at either the source of the videocast or at special smoothing server(s) (e.g., proxies or gateways) within the network. In contrast to previous work on stored video, the online smoothing server has limited knowledge of frame sizes and access to only a segment of the video at a time. This is either because the feed is live or because it is streaming past the server. We formulate an online smoothing model which incorporates playback delay, client and server buffer sizes, server processing capacity, and frame size prediction techniques. Our model can accommodate an arbitrary arrival process. Using techniques for smoothing stored video at the source as a starting point, we develop an online, window-based smoothing algorithm for delay tolerant applications. Extensive experiments with MPEG-1 and M-JPEG video traces demonstrate that online smoothing significantly reduces the peak rate, coefficient of variation, and effective bandwidth of variable-bit-rate video streams. These reductions can be achieved with modest playback delays of a few seconds to a few tens of seconds and moderate client buffer sizes, and closely approximate the performance of optimal offline smoothing of stored video. In addition, we show that frame size prediction can offer further reduction in resource requirements, though prediction becomes relatively less important for longer playback delays. However, the ability to predict future frame sizes affects the appropriate division of buffer space between the server and client sites. Our experiments show that the optimal buffer allocation shifts to placing more memory at the server as the server has progressively less information about future frame sizes.