Sentence examples for agents to compute from inspiring English sources

An earlier result had shown that, in a competitive environment, one way to allow agents to compute the true value would be to enforce a "zero-profit" condition on them.

Last, follow-on work should examine differing inputs, such as restricting the information used by the agents to compute the best responses.

The question is whether the agents can be incented to communicate their true input to the center, allowing all agents to compute the function correctly.

For the case when this assumption does not hold, we introduce an algorithm distributed over the mirror digraph which allows the agents to compute a doubly stochastic weight assignment if the digraph is doubly stochasticable and announce otherwise if it is not.

In the nonparametric setting, we introduce an on-line algorithm that allows the agents to simultaneously compute the function estimate with small computational, communication and data storage efforts, as well as to quantify its distance from the centralized estimate given by a Regularization Network, one of the most powerful regularized kernel methods.

This process performs a distributed computation using mobile agents: by applying a similarity-based reasoning on information extracted from the web pages of potential addressees, the agents are able to compute a numeric value in [0,1] which provides, for any address, a measure of the "interest" in receiving the E-mail.

These signals are interpreted as force fields by agents in order to compute their reactive behavior.

The agents neither have to compute knowledge nor can they be held responsible for answering queries based on their knowledge under the implicit understanding of knowledge.

By introducing a gradient descent term into the estimator, the explicit knowledge of the bound of the agents' speed is not necessary in contrast to existing works, and each agent is able to compute the centroid of the whole formation in finite time.

In Section "Decision-making with limited resources" we analyze the decision-making problem faced by agents that are unable to compute the single best policy.

(2) The agents are extracted from the environment, and their data are sent to a fusion center to compute the agents' trajectories (illustrated for two agents).