Using Methods of Declarative Logic Programming for Intelligent Information Agents

TL;DR

This paper explores how declarative logic programming methods can enhance reasoning capabilities in multi-agent information retrieval systems, addressing challenges like source heterogeneity and incomplete data.

Contribution

It reviews existing systems and evaluates logic programming approaches for improving reasoning in information agents, highlighting advantages, drawbacks, and open issues.

Findings

Logic programming aids in source assessment and query planning.

Nonmonotonic reasoning handles incomplete and inconsistent information.

Identifies open challenges for future development.

Abstract

The search for information on the web is faced with several problems, which arise on the one hand from the vast number of available sources, and on the other hand from their heterogeneity. A promising approach is the use of multi-agent systems of information agents, which cooperatively solve advanced information-retrieval problems. This requires capabilities to address complex tasks, such as search and assessment of sources, query planning, information merging and fusion, dealing with incomplete information, and handling of inconsistency. In this paper, our interest is in the role which some methods from the field of declarative logic programming can play in the realization of reasoning capabilities for information agents. In particular, we are interested in how they can be used and further developed for the specific needs of this application domain. We review some existing systems and current projects, which address information-integration problems. We then focus on declarative knowledge-representation methods, and review and evaluate approaches from logic programming and nonmonotonic reasoning for information agents. We discuss advantages and drawbacks, and point out possible extensions and open issues.