A Web Services, Ontology and Big Data Analysis Technology-Based Cloud Case-Based Reasoning Agent for Energy Conservation of Sustainability Science

Energy conservation is one of the important topics for sustainability science, while case-based reasoning is one of the most important techniques for sustainable processing. This study aimed to develop a cloud case-based reasoning agent that integrates multiple intelligent technologies and supports, which can help users to quickly, accurately, and effectively obtain useful cloud energy-saving information in a timely manner for sustainability science. The system was successfully built with the support of Web services technology, ontology, and big data analytics. To set up this energy-saving case-based reasoning agent, this study reviewed the relevant technologies for building a web services platform and explored how to widely integrate and support the cloud interaction of the energy-saving data processing agent via the technologies. In addition to presenting relevant R&D technologies and results in detail, this study carefully conducted performance and learning experiments to prove the system’s effectiveness. The results showed that the core technology of the case-based reasoning agent achieved good performance and that the learning effectiveness of the overall system was also great.

Common-sense reasoning, theories of

The task of formalizing common-sense reasoning within a logical framework can be viewed as an extension of the programme of formalizing mathematical and scientific reasoning that has occupied philosophers throughout much of the twentieth century. The most significant progress in applying logical techniques to the study of common-sense reasoning has been made, however, not by philosophers, but by researchers in artificial intelligence, and the logical study of common-sense reasoning is now a recognized sub-field of that discipline. The work involved in this area is similar to what one finds in philosophical logic, but it tends to be more detailed, since the ultimate goal is to encode the information that would actually be needed to drive a reasoning agent. Still, the formal study of common-sense reasoning is not just a matter of applied logic, but has led to theoretical advances within logic itself. The most important of these is the development of a new field of ‘non-monotonic’ logic, in which the conclusions supported by a set of premises might have to be withdrawn as the premise set is supplemented with new information.

A Goal Reasoning Agent for Controlling UAVs in Beyond-Visual-Range Air Combat

We describe the Tactical Battle Manager (TBM), an intelligent agent that uses several integrated artificial intelligence techniques to control an autonomous unmanned aerial vehicle in simulated beyond-visual-range (BVR) air combat scenarios. The TBM incorporates goal reasoning, automated planning, opponent behavior recognition, state prediction, and discrepancy detection to operate in a real-time, dynamic, uncertain, and adversarial environment. We describe evidence from our empirical study that the TBM significantly outperforms an expert-scripted agent in BVR scenarios. We also report the results of an ablation study which indicates that all components of our agent architecture are needed to maximize mission performance.