Many believe that humans need to build trust in the artificial intelligence that they use or collaborate with. Transparency and accountability are two fundamental requirements for building that trust. In this paper, we argue that tracking decision provenance is a foundational capability for providing transparency and accountability. The provenance model used in the research described is a simple one, standardized by the World Wide Web Consortium. Provided are descriptions of research that aim to discern critical information about decisions made by autonomous agents through the graphs built by tracking provenance, despite the simplicity of the model and the possible granularity of the resulting graph. The use of provenance to provide explanations of decisions is also described, utilizing Rhetorical Structure Graphs to add application domain and presentation domain knowledge to the spare provenance data model.
A critical factor in utilizing agents with Artificial Intelligence (AI) is their robustness to novelty. AI agents include models that are either engineered or trained. Engineered models include knowledge of those aspects of the environment that are known and considered important by the engineers. Learned models form embeddings of aspects of the environment based on connections made through the training data. In operation, however, a rich environment is likely to present challenges not seen in training sets or accounted for in engineered models. Worse still, adversarial environments are subject to change by opponents. A program at the Defense Advanced Research Project Agency (DARPA) seeks to develop the science necessary to develop and evaluate agents that are robust to novelty. This capability will be required, before AI has the role envisioned within mission critical environments.
Sea navigation and operations within areas of interest has been a major focus of naval research. Documents such as Raster Navigational Charts (RNC) that help with sea navigation tasks are critically important. A RNC is a copy of a navigational paper chart in image form. Therefore, RNC contains important information such as navigational channels, water depths, rocky areas etc. However, a RNC is hard to interpret by computers and even humans as it contains very dense information due to the different layers of drawings from the information mentioned above. In this paper, we introduce a reverse engineering approach using computer vision to extract features from the RNC image. We use optical character recognition to extract text features and templates matching for symbolic features. With the new approach, we show that RNC will become machine readable, and the features extracted can be used to draw tactical regions of interest.
Amplifying human ability for controlling complex environments featuring autonomous units can be aided by learned models of human and system performance. In developing a command and control system that allows a small number of people to control a large number of autonomous teams, we employ an autonomics framework to manage the networks that represent mission plans and the networks that are composed of human controllers and their autonomous assistants. Machine learning allows us to build models of human and system performance useful for monitoring plans and managing human attention and task loads. Machine learning also aids in the development of tactics that human supervisors can successfully monitor through the command and control system.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.