Humans have been involved in search activities from the beginning of their history: searching for food and shelter in prehistoric times; searching for new continents and countries in the Middle Ages; and searching for information in the digital age. It should not be surprising to see that in the new ‘information age’ some of the world’s leading companies are building their business on the basis of their search abilities. To name just two, Google has changed the world by providing search engines for users to look for specific content, and Facebook enables users to search for their own social identity. Both companies rely on dual search targets in their business models. On the one hand, they search and provide the exact information that their users are looking for. On the other hand, they search and provide lists of potential target users to their clients, such as media publishers
and business organizations, according to predefined categories and queries.
It should be noted that all the above-mentioned search activities are probabilistic in
nature. An ancient hunter-gatherer could only estimate were he should look for food. And
although he could not formalize his thoughts in probabilistic terms and within a statistical
framework, he captured information by his senses, processed it in his brain, and moved to
areas where he believed that his chances of success would be higher.
Google and Facebook use an identical search scheme in a more formalized and modern manner. Information is gathered from the network and processed by sophisticated algorithms with a similar mission – to increase the probability of a successful search. This book is about probabilistic search. We address this challenge and provide a more formalized framework for one of the most investigated questions in the history of science.
A major part of this challenge is to consolidate the different research directions, schemes, notations, assumptions, and definitions that are related to probabilistic search in various research fields, such as operations research, artificial intelligence, stochastic search, information theory, statistics, and decision theory – to name but a few .
The book is intended for practitioners as well as theorists. It presents some of the main concepts and building blocks that are shared by many probabilistic search algorithms. It includes both well-known methods and our own methods for an information-driven search. It uses information theory concepts that are general enough to cope with the generalized search scheme. Moreover, it focuses on group testing, which is the theory that enables conclusions to be drawn on a group of search points simultaneously, for example, a conclusion made by the ancient hunter-gatherer to avoid a certain search area where the chances of success
We do not claim to provide a unified and general probabilistic search framework, yet we hope that this book bridges some of the gaps between the various research areas that tackle probabilistic search tasks.
Finally, we would like to thank our families for their continuous support and love. The book is dedicated to them and to information-seekers around the world who search for good answers.
Last modified: May 2018