A Cyber Science Based Ontology for Artificial General Intelligence Containment

The development of artificial general intelligence is considered by many to be inevitable. What such intelligence does after becoming aware is not so certain. To that end, research suggests that the likelihood of artificial general intelligence becoming hostile to humans is significant enough to warrant inquiry into methods to limit such potential. Thus, containment of artificial general intelligence is a timely and meaningful research topic. While there is limited research exploring possible containment strategies, such work is bounded by the underlying field the strategies draw upon. Accordingly, we set out to construct an ontology to describe necessary elements in any future containment technology. Using existing academic literature, we developed a single domain ontology containing five levels, 32 codes, and 32 associated descriptors. Further, we constructed ontology diagrams to demonstrate intended relationships. We then identified humans, AGI, and the cyber world as novel agent objects necessary for future containment activities. Collectively, the work addresses three critical gaps: (a) identifying and arranging fundamental constructs; (b) situating AGI containment within cyber science; and (c) developing scientific rigor within the field.Comment: 12 pages, 4 figures, 3 table

A Demo for Constructing Domain Ontology from Academic Papers ABSTRACT

Traditional construction methods of domain ontology usually have following two limits. First, these methods usually depend on some high cost resources. Second, these methods are easily to result in error propagation because of the errors introduced in the concept identification step. In this paper we present a demo that constructs domain ontology with an easy method. And three main features distinguish our method from traditional methods. First, the proposed method uses academic papers to construct domain ontology. Second, the proposed method carefully selects some keywords in these academic papers as domain concepts. Thus error propagation is reduced accordingly. Third, the proposed method mines hierarchical relations among concepts with a graph generation and conversion method. The effects of our proposed method are evaluated from two perspectives in an IT domain ontology which is constructed with the proposed method: the quality of domain concepts and the quality of concept’s relations. And evaluation results show that both of them achieve high qualities