15623138
Using bayesian networks to model agent relationships
An agent-society of the future is envisioned to be as complex as a human society. Just like human societies, such multiagent systems (MAS) deserve an in-depth study of the dynamics, relationships, and interactions of the constituent agents. An agent in a MAS may have only approximate a priori estimates of the trustworthiness of another agent. But it can learn from interactions with other agents, resulting in more accurate models of these agents and their dependencies together with the influences of other environmental factors. Such models are proposed to be represented as Bayesian or belief networks. An objective mechanism is presented to enable an agent elicit crucial information from the environment regarding the true nature of the other agents. This mechanism allows the modeling agent to choose actions that will produce guaranteed minimal improvement of the model accuracy. The working of the proposed maxim in entropy procedure is demonstrated in a multiagent scenario.
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.
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.
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.
43158386
Evolution and learning in multiagent systems
396384
On Unbiased Sampling for Unstructured Peer-to-Peer Networks
This paper presents a detailed examination of how the dynamic and heterogeneous nature of real-world peer-to-peer systems can introduce bias into the selection of representative samples of peer properties (e.g., degree, link bandwidth, number of files shared). We propose the metropolized random walk with backtracking (MRWB) as a viable and promising technique for collecting nearly unbiased samples and conduct an extensive simulation study to demonstrate that our technique works well for a wide variety of commonly-encountered peer-to-peer network conditions. We have implemented the MRWB algorithm for selecting peer addresses uniformly at random into a tool called ion-sampler. Using the Gnutella network, we empirically show that ion-sampler yields more accurate samples than tools that rely on commonly-used sampling techniques and results in dramatic improvements in efficiency and scalability compared to performing a full crawl.
33712953
Fractional Cascading Simplified
We give an alternate implementation of the fractional cascading data-structure of Chazelle and Guibas to do 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 casading 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 rapidly with number of keys.
8938271
Kernel-Based Reinforcement Learning
We present a kernel-based approach to reinforcement learning that overcomes the stability problems of temporal-difference learning in continuous state-spaces. First, our algorithm converges to a unique solution of an approximate Bellman's equation regardless of its initialization values. Second, the method is consistent in the sense that the resulting policy converges asymptotically to the optimal policy. Parametric value function estimates such as neural networks do not possess this property. Our kernel-based approach also allows us to show that the limiting distribution of the value function estimate is a Gaussian process. This information is useful in studying the bias-variance tradeoff in reinforcement learning. We find that all reinforcement learning approaches to estimating the value function, parametric or non-parametric, are subject to a bias. This bias is typically larger in reinforcement learning than in a comparable regression problem.
17022940
Using Windows NT for Real-Time Applications: Experimental Observations and Recommendations
Windows NT was not designed as a real time operating system, but market forces and the acceptance of NT in industrial applications have generated a need for achieving real time functionality with NT. As its use for real time applications proliferates, based on an experimental evaluation of NT, we quantitatively characterize the obstacles placed by NT. As a result of these observations, we provide a set of recommendations for users to consider while building real time applications on NT. These are validated by the use of NT for a prototype application involving real time control that includes multimedia information processing. The results of the above study should provide system designers with guidelines, as well as insight, into the design of an architecture based on NT for supporting applications with components having real time constraints.
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.