Heterogeneous Explore-Exploit Strategies on Multi-Star Networks

Explore or Exploit? Effective Strategies for Disambiguating Large Databases

Data ambiguity is inherent in applications such as data integration, location-based services, and sensor monitoring. In many situations, it is possible to “clean”, or remove, ambiguities from these databases. For example, the GPS location of a user is inexact due to measurement errors, but context information (e.g., what a user is doing) can be used to reduce the imprecision of the location val...

Evaluation of Explore-Exploit Policies in Multi-result Ranking Systems

We analyze the problem of using Explore-Exploit techniques to improve precision in multi-result ranking systems such as web search, query autocompletion and news recommendation. Adopting an exploration policy directly online, without understanding its impact on the production system, may have unwanted consequences the system may sustain large losses, create user dissatisfaction, or collect expl...

Explore vs. Exploit: Task Allocation for Multi-robot Foraging

This paper describes two measures of performance that can be used to allocate tasks in a multirobot foraging problem. These heuristics can be used by a human supervisor or an automated control algorithm to adjust the number of robots exploring the environment versus the number of robots greedily harvesting based on current knowledge. A numerical simulation study is presented that offers prelimi...

Explore, Excogitate, Exploit: Component Mining

nents? In a panel at TOOLS USA 1994, Eric Aranow nicely outlined the basic question: “Is it nature or nurture?” In other words, are components born— devised from the start as components— or are components made? That is to say, have they evolved from program elements that may have been originally built for other purposes? Although some panel members argued for the nature view, it was clear to ev...

A unifying theory of explore-exploit decisions