Local search: A guide for the information retrieval practitioner

There are a number of combinatorial optimisation problems in information retrieval in which the use of local search methods are worthwhile. The purpose of this paper is to show how local search can be used to solve some well known tasks in information retrieval (IR), how previous research in the field is piecemeal, bereft of a structure and methodologically flawed, and to suggest more rigorous ways of applying local search methods to solve IR problems. We provide a query based taxonomy for analysing the use of local search in IR tasks and an overview of issues such as fitness functions, statistical significance and test collections when conducting experiments on combinatorial optimisation problems. The paper gives a guide on the pitfalls and problems for IR practitioners who wish to use local search to solve their research issues, and gives practical advice on the use of such methods. The query based taxonomy is a novel structure which can be used by the IR practitioner in order to examine the use of local search in IR

General Terms

In this paper we introduce an application of real-coded genetic algorithms to the problem of consistent graph layout and exploring the role of mutation for this particular problem. We introduce several forms of mutation, some of which being specific to this problem, and show that the choice of this operator can have a great impact on the performance of the algorithm

Crossover improvement for the genetic algorithm in information retrieval

Abstract- Genetic algorithms (GAs) search for good solutions to a problem by operations inspired from the natural selection of living beings. Among their many uses, we can count information retrieval (IR). In this field, the aim of the GA is to help an IR system to find, in a huge documents text collection, a good reply to a query expressed by the user. The analysis of phenomena seen during the implementation of a GA for IR has brought us to a new crossover operation. This article introduces this new operation and compares it with other learning methods

NPCs and Chatterbots with Personality and Emotional Response

Abstract — Chatterbots are computer programs that simulate intelligent conversation. They are situated between games and toys, as their aim is mostly to be entertaining, but the user doesn’t have to follow precise rules when playing with the program. Currently business and educational applications have started to emerge as a further development of the idea of intelligent dialog. For the game industry, they come close to the concept of NPC, or Non-Player Character, and they may become part of making such virtual beings more believable and life-like in the future. In this paper we present application introducing an emotional component designed to enhance the realism of the conversation