The k-means algorithm is a well-known method for partitioning n points that lie in the d-dimensional space into k clusters. Its main features are simplicity and speed in practice....
Weadvance a knowledge-based learning method that augments conventional generalization to permit concept acquisition in failure domains. These are domains in whichlearning must pro...
In this paper we study the constrained consensus problem, i.e. the problem of reaching a common point from the estimates generated by multiple agents that are constrained to lie in...
In this paper I give a brief overview of recent work on uncertainty inAI, and relate it to logical representations. Bayesian decision theory and logic are both normative frameworks...
The concept of an agent has recently become important in Artificial Intelligence (AI), and its relatively youthful subfield, Distributed AI (DAI). Our aim in this paper is to poin...