Year: 2024
Type: article
Abstract: The growing volume of information available to decision-makers makes it increasingly challenging to process all data during decision-making. As a result, a method for selecting only relevant information is highly desirable. Moreover, since the meaning of information depends on its context, the decision-making process requires mechanisms to identify the context of specific scenarios. In this paper, we propose a conceptual framework that utilizes Situation Theory to formalize the concept of context and analyze information relevance. Building on this framework, we introduce an inference-based reasoning process that automatically identifies the information necessary to characterize a given situation. We evaluate our approach in a cybersecurity scenario where computer agents respond to queries by utilizing available information and sharing relevant facts with other agents. The results show that our method significantly reduces the time required to infer answers to situation-specific queries. Additionally, we demonstrate that using only relevant information provides the same answers as using the entire knowledge base. Finally, we show that the method can be applied to a limited set of training queries, allowing the reuse of relevant facts to address new queries effectively.
Source: Information
DOI: 10.3390/info15100651
Authors: Shan Lu, Mieczyslaw Kokar
Institution: Northeastern University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,819
OpenAlex ID: W4403484338