In this paper, we address the tradeo between exploration and exploitation for agents which need to learn more about the structure of their environment in order to perform more e e...
Shlomo Argamon-Engelson, Sarit Kraus, Sigalit Sina
In many dynamic matching applications—especially high-stakes ones—the competitive ratios of prior-free online algorithms are unacceptably poor. The algorithm should take distr...
John P. Dickerson, Ariel D. Procaccia, Tuomas Sand...
This paper presents a statistical learning approach to predicting people's bidding behavior in negotiation. Our study consists of multiple 2-player negotiation scenarios wher...
We present black-box techniques for learning how to interleave the execution of multiple heuristics in order to improve average-case performance. In our model, a user is given a s...
Matthew J. Streeter, Daniel Golovin, Stephen F. Sm...
A granular neural Web-based stock prediction agent is developed using the granular neural network (GNN) that can discover fuzzy rules. Stock data sets are downloaded from www.yahoo...
Yan-Qing Zhang, Somasheker Akkaladevi, George J. V...